Analytica Docs - User contributions [en]

Jobs for Analytica experts

2010-07-15T21:12:08Z

Sswamy: /* Jobs */

=Employment Opportunities=

==Guidelines==
When adding an employment opportunity to the job listings, please indicate the following:
*Date of Job posting
*Job Title
*Name of Company/Organization
*Location (City, State)

Please provide a brief description of the job requirements in addition to your contact details.

You may edit the sample template below as required:
===Date Title, Company Name, Location===
:: '''Description:'''
:: '''Contact:'''

==Jobs==

=== July 15th, 2010. Energy Market Analyst/Emerging Technologies Consultant, Customized Energy Solutions ===

: '''Description:''' We are looking for a talented and hard-working individual with a minimum of five years experience in the deregulated electric industry to support the electricity market analysis consulting work of Customized Energy Solutions. This position will be largely focused performing analytical project work geared towards evaluations of aspects of the electricity markets. Specifically, the analyses are likely to deal emerging technologies including energy storage technologies, renewable energy products, demand response opportunities, and smart grid initiatives, but will also deal with conventional generation and load modeling. Examples of specific responsibilities include:

:* Lead interactions with energy storage and renewable technology companies to develop projects for market evaluation.
:* Participate in market analysis projects with energy storage and renewable elements, as well as other analysis projects. These projects will require the Consultant to produce reports based on market knowledge, understanding of available technologies, market rule research, pricing research, and other inputs.
:* Assist clients in developing proposals for emerging technology funding from venture capital funds, financial institutions, and / or other funding agencies.
:* Assist financial institutions in due diligence of new technologies based on available market opportunities, potential technology improvements, market rule changes and competition from other solution providers.
:* Participate in conferences / tradeshows for the primary purpose of developing additional business for Customized Energy Solutions.

: '''Preferred experience:'''
:* General knowledge of wholesale electricity markets
:* Some specific understanding of energy storage technology
:* A Masters Degree; preferably in electrical engineering or other engineering field
:* Experience in performing project research
:* Strong analytical reasoning skills
:* Understanding of project financial modeling
:* Experience with statistical analysis & Modeling tools, such as Palisade Decision Tools suite including @Risk and / or Analytica
:* Good written and oral communication skills
:* Ability to be self directed as well as work well with others
:* Experience with standard office software
:* Specific knowledge of the RTO/ISO wholesale markets
:* Data mining experience
:* Database software skills, specifically Microsoft SQL experience
:* Advanced education

Determination of Consultant / Analyst and compensation is commensurate with experience and performance. Benefits include 401K,
profit sharing plan, and comprehensive medical and dental insurance. This opportunity is based out of our center city
Philadelphia, Pennsylvania location with close proximity to public transportation and cultural attractions.

: '''Contact:''' Send your resumes to rahul@ces-ltd.com Rahul Walawalkar Ph.D.,Vice President, Emerging Technologies & Markets

===June 1, 2009. Principal Energy Analyst, Lumina Decision Systems, Los Gatos, CA===

: '''Description:''' In addition to developing and publishing Analytica, Lumina has a small growing consulting team that provides consulting and creates analytic tools for our clients, based on Analytica. We specialize in applications in energy, environment, and economics. We are seeking an experienced energy analyst as a key member of our consulting team. The successful candidate will assist companies, government, and NGOs to help them navigate the rapidly changing opportunities and risks in the way the produce and consume energy. The candidate will also help in the design and development of analytic tools. You have experience developing analytic models to provide new insights into complex situations. You enjoy working for a small company with the feel of a start-up. You want the the opportunity to lead a consulting group, to recruit, manage, and mentor other analysts, and to share in shaping our company strategy. You want to contribute your substantial talents in speeding the transition to a more sustainable world.

: '''Qualifications:'''
:* A Masters degree, MBA, or preferably PhD in a technical area, science, engineering, economics, business, or policy.
:* Extensive experience in developing quantitative models to provide useful insights into complex situations.
:* Deep knowledge and interest in energy and environmental issues.
:* Experience working with clients to help them understand what they need and providing it to them.
:* Excellence in speaking and writing complex ideas with clarity.
:* Ability to manage multiple projects and work with minimal supervision.
:* Ability to lead a small group, manage, and mentor other analysts.

: '''Preferred experience:'''
:* Work with utility companies and other energy businesses.
:* Decision analysis and expert elicitation of probabilities.
:* R&D portfolio analysis.
:* Analytica or other quantitative modeling and simulation tools.
:* Optimization tools.

: '''Contact:''' Please email your resume and cover letter to [mailto:info@Lumina.com]

===December 15, 2008, Senior Analyst, Enrich Consulting, San Jose, California===
: '''Description:''' Enrich Consulting builds web-based enterprise tools to enable stage-gate, portfolio management, and project valuation decision making. We use Analytica as a behind-the-scenes number cruncher extraordinaire and are currently looking for experienced Analytica modelers who can support our clients' needs for sophisticated yet easy-to-use financial modeling tools. Below I've included information on one of our openings. For more information please see the 'company' section of our website [http://enrichconsulting.com] --Rich Sonnenblick

:As a Senior Analyst, you will perform a leading role on client engagements, working closely with clients to understand key business issues and translate them into business and financial models.

:'''Other duties:'''
:* Develop comprehensive financial models to evaluate individual R&D initiatives and R&D portfolios
:* Responsible for driving implementations of the Enrich Portfolio System (EPS) software
:* Help clients understand and incorporate market and development risk in their analysis of strategic alternatives
:* Conduct training sessions on the EPS and decision analysis
:* Assist clients with the development of their portfolio processes
:* Develop and summarize insights for communicating to clients
:* Help to develop new approaches to problems and expand applications to new industries
:'''Qualifications:'''
:* BA, MS, or PhD in Operations Research, Applied Math, Physics, Engineering, Economics or related field -OR-
:* MBA with a concentration in quantitative business forecasting, decision making, or market analysis
:* 3-6 years work experience in a quantitative position in the management consulting, pharmaceutical, biotechnology, and/or high-technology industries
:* Demonstrated excellent analytical and computer skills
:* Proficiency modeling with Excel spreadsheets
:* Very strong oral and written communication skills
:* Enjoy explaining/teaching concepts to others
:* Desire for a high level of responsibility and ability to work with minimal supervision
:* Understanding of financial statements and financial analysis
:* Ability to quickly learn new skills and methodologies
:* Desire to work in a team-oriented, informal, small company environment
:* Willingness to travel up to 30%
:* Willingness to relocate to the San Francisco / South Bay area
:'''Preferred:'''
:* Experience creating financial models for R&D valuations and/or R&D portfolio management
:* Demonstrated leadership skills in multiple settings and ability to manage many priorities and multi-task
:* Familiarity with decision modeling software such as Analytica, DPL, Crystal Ball, or enterprise data analysis applications
:* Experience deploying analytic applications on the web
:* Knowledge of the pharmaceutical, biotechnology, and/or high-tech industries

: '''Contact:''' Please send your resume and a cover letter describing your qualifications for the position to [mailto:careers@enrichconsulting.com]

Articles that refer to Analytica

2010-01-04T22:01:49Z

Sswamy:

This page lists articles about projects based on Analytica models. If you have published an article based on work that used an Analytica model, or know of publications not listed here, feel free to add the citation to this page. Where possible, please include links to the article.

----

= About Analytica =

Max Henrion, [http://lumina.com/dlana/papers/Whats%20wrong%20with%20spreadsheets.pdf ''What's Wrong with Spreadsheets and How to Fix Them''], [http://lumina.com Lumina Web Site]].

Granger Morgan and Max Henrion (1998), [http://www.lumina.com/software/ch10.9.PDF Analytica:A Software Tool for Uncertainty Analysis and Model Communication], Chapter 10 of ''Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis'', second edition, Cambridge University Press, New York.

Robert D. Brown (2008), [http://www.lumina.com/whatsnew/ORMS%20review.htm Analytica 4.1, Software Review, ORMS Today, June 2008]

= Energy =

Stadler M., Marnay C., Azevedo I.L., Komiyama R., Lai J. (2009), [http://eetd.lbl.gov/ea/emp/reports/lbnl-1884e.pdf '' The Open Source Stochastic Building Simulation Tool SLBM and Its Capabilities to Capture Uncertainty of Policymaking in the U.S. Building Sector''],

Ye Li and H. Keith Florig (Sept. 2006), [https://wpweb2.tepper.cmu.edu/ceic/pdfs/CEIC_06_10.pdf ''Modeling the Operation and Maintenance Costs of a Large Scale Tidal Current Turbine Farm''], [http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4098949 Oceans (2006)]:1-6.

L.F.Miller, Brian Thomas, J.McConn, J. Hou, J.Preston, T.Anderson, and M.Humberstone (2007), [http://hou.jia.googlepages.com/2007_ANS_Summer_Abstract.doc ''Uncertainty Analysis Methods for Equilibrium Fuel Cycles''], ANS Summer Abstract.

Gregory A. Norris and Peter Yost (Fall 2001), [http://www.mitpressjournals.org/doi/abs/10.1162/10881980160084015 ''Journal of Industrial Ecology''] 5(4):15-28, MIT Press Journals.

Dallas Burtraw, Karen Palmer, Anthony Paul (Oct 1998),
[https://www.lumina.com/taf/taflist/dltaf/paper_981029.pdf ''The Welfare Impacts of Restructuring and Environmental Regulatory Reform in the Electric Power Sector''], Resources for the Future, presented at Southern Economics Association Meetings, Nov 8-10, 1998 Baltimore, Maryland.

Jouni T Tuomisto and Marko Tainio (2005),
[http://www.biomedcentral.com/1471-2458/5/123 ''An economic way of reducing health, environmental, and other pressures of urban traffic: a decision analysis on trip aggregation''], BMC Public Health 5:123. doi:10.1186/1471-2458-5-123

Yurika Nishioka, Jonathan I. Levy, Gregory A. Norris, Andrew Wilson, Patrick Hofstetter, John D. Spengler (Oct 2002),
[http://www.blackwell-synergy.com/doi/abs/10.1111/1539-6924.00266 ''Integrating Risk Assessment and Life Cycle Assessment: A Case Study of Insulation''], Risk Analysis 22(5):1003-1017.

= Environmental =

== Climate Change ==

David G. Groves nad Robert J. Lempert (Feb 2007), [http://dx.doi.org/10.1016/j.gloenvcha.2006.11.006 ''A new analytic method for finding policy-relevant scenarios''], Global Environmental Change 17(1):73-85.

Maged Senbel, Timothy McDaniels, and Hadi Dowlatabadi (July 2003),
[http://dx.doi.org/10.1016/S0959-3780(03)00009-8 ''The ecological footprint: a non-monetary metric of human consumption applied to North America''], Global Environmental Change 13(2):83-100.

Dowlatabadi, H. (1998). ''Sensitivity of Climate Change Mitigation Estimates to Assumptions About Technical Change.'' Energy Economics 20: 473-93.

West, J. J. and H. Dowlatabadi (1998). ''On assessing the economic impacts of sea level rise on developed coasts.'' Climate, change and risk. London, Routledge. 205-20.

Leiss, W., H. Dowlatabadi, and Greg Paoli (2001). ''Who's Afraid of Climate Change? A guide for the perplexed.'' Isuma 2(4): 95-103.

Morgan, M. G., M. Kandlikar, J. Risbey and H. Dowlatabadi (1999). ''Why conventional tools for policy analysis are often inadequate for problems of global change.'' Climatic Change 41: 271-81.

Casman, E. A., M. G. Morgan and H. Dowlatabadi (1999). ''Mixed Levels of Uncertainty in Complex Policy Models.'' Risk Analysis 19(1): 33-42.

Dowlatabadi, H. (2003). ''Scale and Scope In Integrated Assessment: lessons from ten years with ICAM. Scaling in Integrated Assessment.'' J. Rotmans and D. S. Rothman. Lisse, Swetz & Zeitlinger: 55-72.

Dowlatabadi, H. (2000). ''Bumping against a gas ceiling.'' Climatic Change 46(3): 391-407.

Morgan, M. G. and H. Dowlatabadi (1996). ''Learning From Integrated Assessment of Climate Change.'' Climatic Change 34: 337-368.

== Ecology ==

O'Ryan R., Diaz M. (2008), [http://www.informaworld.com/smpp/section?content=a793817831&fulltext=713240928 ''The Use of Probabilistic Analysis to Improve Decision-Making in Environmental Regulation in a Developing Context: The Case of Arsenic Regulation in Chile''], Human and Ecological Risk Assessment: An International Journal, Vol 14, Issue 3, pg: 623-640.

Andrew Gronewold and Mark Borsuk, "A probabilistic modeling tool for assessing water quality standard compliance", submitted to EMS Oct 2008.

Mark E. Borsuk, Peter Reichert, Armin Peter, Eva Schager and Patricia Burkhardt-Holm (feb 2006),
[http://dx.doi.org/10.1016/j.ecolmodel.2005.07.006 ''Assessing the decline of brown trout (Salmo trutta) in Swiss rivers using a Bayesian probability network''], Ecological Modelling 192(1-2):224-244.

Mark E. Borsuk, , Craig A. Stow1 and Kenneth H. Reckhow (Apr 2004),
[http://dx.doi.org/10.1016/j.ecolmodel.2003.08.020 ''A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis''], Ecological Modelling 173(2-3):219-239.

Mark E. Borsuk, Sean P. Powers, and Charles H. Peterson (2002),
[http://article.pubs.nrc-cnrc.gc.ca/ppv/RPViewDoc?_handler_=HandleInitialGet&journal=cjfas&volume=59&calyLang=eng&articleFile=f02-093.pdf ''A survival model of the effects of bottom-water hypoxia on the population density of an estuarine clam (Macoma balthica)''], Canadian Journal of Fisheries and Aquatic Sciences (59):1266-1274.

Rebecca Montville and Donald Schaffner (Feb 2005),
[http://aem.highwire.org/cgi/content/abstract/71/2/746 ''Monte Carlo Simulation of Pathogen Behavior during the Sprout Production Process''], Applied and Environmental Microbiology 71(2):746-753.

S. K. J. Rasmussen, T. Ross, J. Olley and T. McMeekin (2002),
[http://dx.doi.org/10.1016/S0168-1605(01)00687-0 ''A process risk model for the shelf life of Atlantic salmon fillets''], International Journal of Food Microbiology 73(1):47-60.

== Emissions Policy Analysis ==

C. Bloyd, J. Camp, G. Conzelmann, J. Formento, J. Molburg, J. Shannon, M. Henrion, R. Sonnenblick, K. Soo Hoo, J. Kalagnanam, S. Siegel, R. Sinha, M. Small, T. Sullivan, R. Marnicio, P. Ryan, R. Turner, D. Austin, D. Burtraw, D. Farrell, T. Green, A. Krupnick, and E. Mansur (Dec 1996), [http://lumina.com/taf/taflist/dltaf/TAF.pdf ''Tracking and Analysis Framework (TAF) Model Documentation and User’s Guide: An Interaction Model for Integrated Assessment of Title IV of the Clean Air Act Amendments''], Decision and Information Sciences Division, Argonne National Laboratory.

Max Henrion, Richard Sonnenblick, Cary Bloyd (Jan 1997), [https://www.lumina.com/taf/taflist/dltaf/Innovations.PDF ''Innovations in Integrated Assessment: The Tracking and Analysis Framework (TAF)''], Air and Waste Management Conference on Acid Rain and Electric Utilities, Scottsdale, AZ.

Richard Sonnenblick and Max Henrion (Jan 1997), [https://www.lumina.com/taf/taflist/dltaf/Uncertainty.PDF ''Uncertainty in the Tracking and Analysis Framework Integrated Assessment: The Value of Knowing How Little You Know''], Air and Waste Management Conference
on Acid Rain and Electric Utilities, Scottsdale, Arizona.

R. Sinha, M. J. Small, P. F. Ryan, T. J. Sullivan and B. J. Cosby (July 1998),
[http://www.springerlink.com/content/tq8127805181x57k/ ''Reduced-Form Modelling of Surface Water and Soil Chemistry for the Tracking and Analysis Framework''], Water, Air, & Soil Pollution 105(3-4).

Dallas Burtraw and Erin Mansur (Mar 1999),
[http://www.rff.org/documents/RFF-DP-99-25.pdf ''The Effects of Trading and Banking in the SO2 Allowance Market''], Discussion paper 99-25, [http://rff.org Resources for the Future].

Galen mcKinley, Miriam Zuk, Morten Höjer, Montserrat Avalos, Isabel González, Rodolfo Iniestra, Israel Laguna, Miguel A. Martínez, Patricia Osnaya, Luz M. Reynales, Raydel Valdés, and Julia Martínez (2005), [http://www.aos.wisc.edu/~galen/Downloads/McKinley_EST05.pdf ''Quantification of Local and Global Benefits from Air Pollution Control in Mexico City''], Environ. Sci. Technol. 39:1954-1961.

Luis A. CIFUENTES, Enzo SAUMA, Hector JORQUERA and Felipe SOTO (2000),
[http://www.airimpacts.org/documents/local/M00007481.pdf ''PRELIMINARY ESTIMATION OF THE POTENTIAL ANCILLARY BENEFITS FOR CHILE''], Ancillary Benefits and Costs of Greenhouse Gas Mitigation.

Marko Tainio, Jouni T Tuomisto, Otto Hänninen, Juhani Ruuskanen, Matti J Jantunen, and Juha Pekkanen (2007),
[http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2000460 ''Parameter and model uncertainty in a life-table model for fine particles (PM2.5): a statistical modeling study''], Environ Health 6(24).

L. Basson and J.G. Petrie (Feb 2007),
[http://dx.doi.org/10.1016/j.envsoft.2005.07.026 ''An integrated approach for the consideration of uncertainty in decision making supported by Life Cycle Assessment''], Environmental Modeling & Software 22(2):167-176, Environmental Decision Support Systems, Elsevier.

== Natural Resource Management ==

S. Schweizer, M.E. Borsuk and P. Reichert (2004), [http://www.iemss.org/iemss2004/pdf/integratedmodelling/schwpred.pdf ''Predicting the Hydraulic and Morphological Consequences of River Rehabilitation''], Swiss Frederal Institute for Environmental Science and Technology (EAWAG), [http://www.iemss.org International Environmental Modelling and Software Society] Transactions.

S. Spörri, M. Borsuk, I. Peters, and P. Reichert (Apr 2007), [http://dx.doi.org/10.1016/j.ecolecon.2006.07.001 ''The economic impacts of river rehabilitation: A regional Input–Output analysis''], Ecological Economics 62(2): 341-351.

ME Borsuk, CA Stow, KH Reckhow (2003), [http://www.ncsu.edu/wrri/about/reckhow/Neu_BERN.pdf ''An integrated approach to TMDL development for the Neuse River estuary using a Bayesian probability network model (Neu-BERN)''], Journal of Water Resources Planning and Management.

David G. Groves, Scott Matyac, Tom Hawkins (Apr 2005), [http://www.waterplan.water.ca.gov/docs/cwpu2005/Vol_4/03-Data_and_Tools/V4PRD4-QUAN.PDF ''QUANTIFIED SCENARIOS OF 2030 CALIFORNIA WATER DEMAND: 2005 California Water Plan Update, Volume 4''].

== Wildlife and Forest Management ==

Peter B. Woodbury, James E. Smith, David A. Weinstein and John A. Laurence (Aug 1998), [http://dx.doi.org/10.1016/S0378-1127(97)00323-X ''Assessing potential climate change effects on loblolly pine growth: A probabilistic regional modeling approach''], Forest Ecology and Management 107(1-3), 99-116.

P.R. Richard, M. Power, M. Hammilton (2003), [http://www.dfo-mpo.gc.ca/csas/Csas/DocREC/2003/RES2003_086_E.pdf ''Eastern Hudson Bay Beluga Precautionary Approach Case Study: Risk analysis models for co-management''], Canadian Science Advisory Secretariat Research Document.

P.R. Richard (2003), [http://www.dfo-mpo.gc.ca/csas/Csas/DocREC/2003/RES2003_087_E.pdf ''Incorporating Uncertainty in Population Assessments''], Canadian Science Advisory Secretariat Research Document.

= Food Risk =

Siobain Duffy and Donald W. Schaffner (2001), [http://foodsci.rutgers.edu/schaffner/pdf%20files/Duffy%20JFP%202001.pdf ''Modeling the Survival of Escherichia coli O157:H7 in Apply Cider Using Probability Distribution Functions for Quantitative Risk Assessment''], Journal of Food Protection 64(5):599-605.

T. A. McMeekin (Sept 2007),
[http://dx.doi.org/10.1016/j.meatsci.2007.04.005 ''Predictive microbiology: Quantitative science delivering quantifiable benefits to the meat industry and other food industries''], Meat Science 77(1):17-27.

Y Chen, WH Ross, VN Scott, DE Gombas (2003), [http://www.ingentaconnect.com/content/iafp/jfp/2003/00000066/00000004/art00005 ''Listeria monocytogenes: Low Levels Equal Low Risk''], Journal of Food Protection 66(4):570-577(8), International Association for Food Protection.

John Bowers, Anders Dalsgaard, Angelo DePaola, I. Karunasagar, Thomas McMeekin, Mitsuaki, Nishibuchi, Ken Osaka, John Sumner, Mark Walderhaug (2005), [https://www.who.int/foodsafety/publications/micro/mra8.pdf ''Risk assessment of
Vibrio vulnificus in raw oysters''], World Health Organization: Microbiological Risk Assessment Series (8), 135 pages.

= Health and Epidemiology =

Igor Linkov, Richard Wilson and George M., Gray (1998), [http://toxsci.oxfordjournals.org/cgi/content/abstract/43/1/1 ''Anticarcinogenic Responses in Rodent Cancer Bioassays Are Not Explained by Random Effects''], Toxicological Sciences 43(1), Oxford University Press.

M. Loane and R. Wootton (Oct 2001), [http://www.ingentaconnect.com/content/rsm/jtt/2001/00000007/A00105s1/art00009 ''A simulation model for analysing patient activity in dermatology''], Journal of Telemedicine and Telecare 7(1):23-25(3), Royal Society of Medicine Press.

Davis Bu, Eric Pan, Janice Walker, Julia Adler-Milstein, David Kendrick, Julie M. Hook, Caitlin M. Cusack, David W. Bates, and Blackford Middleton (2007), [http://care.diabetesjournals.org/cgi/content/abstract/30/5/1137 ''Benefits of Information Technology–Enabled Diabetes Management''], Diabetes Care 30:1137-1142, American Diabetes Associaton.

Julia Adler-Milstein, Davis Bu, Eric Pan, Janice Walker, David Kendrick, Julie M. Hook, David W. Bates, Blackford Middleton. [http://www.liebertonline.com/doi/abs/10.1089/dis.2007.103640?cookieSet=1&journalCode=dis ''The Cost of Information Technology-Enabled Diabetes Management''], Disease Management. June 1, 2007, 10(3): 115-128. doi:10.1089/dis.2007.103640.

E. Ekaette, R.C. Lee, K-L Kelly, P. Dunscombe (Aug 2006),
[http://www.palgrave-journals.com/jors/journal/v58/n2/full/2602269a.html ''A Monte Carlo simulation approach to the characterization of uncertainties in cancer staging and radiation treatment decisions''], Journal of the Operational Research Society 58:177-185.

Lyon, Joseph L.; Alder, Stephen C.; Stone, Mary Bishop; Scholl, Alan; Reading, James C.; Holubkov, Richard; Sheng, Xiaoming; White, George L. Jr; Hegmann, Kurt T.; Anspaugh, Lynn; Hoffman, F Owen; Simon, Steven L.; Thomas, Brian; Carroll, Raymond; Meikle, A Wayne (Nov 2006),[http://www.epidem.com/pt/re/epidemiology/abstract.00001648-200611000-00004.htm ''Thyroid Disease Associated With Exposure to the Nevada Nuclear Weapons Test Site Radiation: A Reevaluation Based on Corrected Dosimetry and Examination Data''], Epidemiology 17(6):604-614.

Negar Elmieh, Hadi Dowlatabadi, Liz Casman (Jan 2006), [http://www.cher.ubc.ca/westnile/pdfs/elmieh_jan06.pdf ''A model for Probabilistic Assessment of Malathion Spray Exposures (PAMSE) in British Columbia''], CMU EEP.

Detlofvon Winterfeldt, Thomas Eppel, John Adams, Raymond Neutra, and Vincent Del Pizzo (2004),
[http://www.blackwell-synergy.com/doi/abs/10.1111/j.0272-4332.2004.00544.x ''Managing Potential Health Risks from Electric Powerlines: A Decision Analysis Caught in Controversy''], Risk Analysis 24(6):1487-1502.

Rebecca Montville, Yuhuan Chen and Donald W. Schaffner (March 2002), [http://dx.doi.org/10.1016/S0168-1605(01)00666-3 ''Risk assessment of hand washing efficacy using literature and experimental data''], International Journal of Food Microbiology 73(2-3):305-313.

DC Kendrick, D Bu, E Pan, B Middleton (2007), ''Crossing the Evidence Chasm: Building Evidence Bridges from Process Changes to Clinical Outcomes'', Journal of the American Medical Informatics Association, Elsevier.

Louis Anthony (Tony) Cox, Jr. (May 2005),
[http://dx.doi.org/10.1016/j.envint.2004.10.012 ''Potential human health benefits of antibiotics used in food animals: a case study of virginiamycin''], Environment International 31(4):549-563.

Jan Walker, Eric Pan, Douglas Johnston, Julia Adler-Milstein, David W. Bates, and Blackford Middleton (19 Jan 2005), [http://content.healthaffairs.org/cgi/reprint/hlthaff.w5.10v1.pdf ''The Value Of Health Care Information Exchange And Interoperability''], Health Affairs.

Doug Johnston, Eric Pan, Blackford Middleton, [http://www.citl.org/research/articles.htm ''Finding the Value in Healthcare Information Technologies], Center for Information Technology Leadership (C!TL) whitepaper.

Chrisman, L., Langley, P., Bay, S., and Pohorille, A. (Jan 2003), [http://chrisman.org/Lonnie/psb2003/index.htm "Incorporating biological knowledge into evaluation of causal regulatory hypotheses"], Pacific Symposium on Biocomputing (PSB).

= Technology and Defense =

Henry Heimeier (1996), [[media:MILCOM96.pdf|A New Paradigm For Modeling The Precision Strike Process]], published in MILCOM96. (model: [[media:Milcom96.ana|Milcom96.ana]]).

Russell F. Richards, Henry A. Neimeier, W. L. Hamm, and D. L. Alexander,
“[[media:Cmac2pap_2_.pdf|Analytical Modeling in Support of C4ISR Mission Assessment (CMA)]],” Third
International Symposium on Command and Control Research and Technology,
National Defense University, Fort McNair, Washington, DC, June 17–20, 1997, pp. 626–
639.

Henry Neimeier and C. McGowan (1996), [[media:INCOSE96.pdf|Analyzing Processes with HANQ]], Proceedings of the International Council on Systems Engineering '96.

Kenneth P. Kuskey and Susan K. Parker (2000), [[media:Kuskey_CAPE_MTR-11.pdf|The Architecture of CAPE Models]], MITRE technical paper. See [http://www.mitre.org/work/tech_papers/tech_papers_00/kuskey_architecture/index.html Abstract].

Henry Neimeier (1994), [[media:AnalQueNet.pdf|Analytic Queuing Network]], Conference Proceedings of the 12th International Conference on the System Dynamics Society, in Stirling, Scotland.

Henry Neimeier (1996) [[media:PhalanxMar.pdf|Analytic Uncertainty Modeling Versus Discrete Event Simulation]] by H. Neimeier, PHALANX.

Rahul Tongia, “Can broadband over powerline carrier (PLC) compete?”. The author uses Analytica in order to model the economic viability of the introduction of a PLC service.

Promises and False Promises of PowerLine Carrier (PLC) Broadband Communications – A Techno-Economic Analysis http://tprc.org/papers/2003/246/Tongia-PLC.pdf

Kanchana Wanichkorn and Marvin Sirbu (1998), [http://www2.tepper.cmu.edu/afs/andrew/gsia/45-879/Readings/ip_pbx.pdf ''The Economics of Premises Internet Telephony''], CMU-EPP.

E.L. Kyser, E.R. Hnatek, M.H. Roettgering (2001),
[http://cat.inist.fr/?aModele=afficheN&cpsidt=973960 ''The politics of accelerated stress testing''], Sound and Vibration 35(3):24-29.

Kevin J. Soo Hoo (June 2000),
[http://iis-db.stanford.edu/pubs/11900/soohoo.pdf ''How Much Is Enough? A Risk-Management Approach to Computer Security''], Working Paper, Consortium for Research on Information Security and Policy (CRISP), Stanford University.

M. Steinbach and S. Giles of MITRE (2005), [http://www.aiaa.org/content.cfm?pageid=406&gTable=Paper&gID=37642 ''A Model for Joint Infrastructure Investment''], AIAA-2005-7309, in AIAA 5th ATIO and 16th Lighter-than-air sys tech and balloon systems conferences, Arlington VA, Sep 26-28, 2005.

Bloomfield, R., Guerra, S. (2002), [http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1028892 ''Process modelling to support dependability arguments''], Proceedings. International Conference on Dependable Systems and Networks, pg. 113-122. DSN 2002.

Christopher L Weber and Sanath K Kalidas (Fall 2004),
[http://www.cmu.edu/greenpractices/green_initiatives/new_house_images/NewHouseCBA_final.pdf ''Cost-Benefit Analysis of LEED Silver Certification for New House Residence Hall at Carnegie Mellon University''], Civil Systems Investment Planning and Pricing Project, Dept. of Civil & Environmental Engineering, Carnegie Mellon University.

J. McMahon, X. Liu, I. Turiel (Jun 2000), [http://www.osti.gov/energycitations/product.biblio.jsp?osti_id=767554 ''Uncertainty and sensitivity analyses of ballast life-cycle cost and payback period''], Technical Report LBNL--44450, Lawrence Berkeley Labs, Berkeley CA.

Paul K. Davis (2000), [http://portal.acm.org/citation.cfm?id=510378.510428 ''Dealing with complexity: exploratory analysis enabled by multiresolultion, multiperspective modeling''], Proceedings of the 32nd Conference on Winter Simulation, pg. 293-302.

Paul K. Davis (2000), [http://www.informs-sim.org/wsc00papers/043.PDF ''EXPLORATORY ANALYSIS ENABLED BY MULTIRESOLULTION, MULTIPERSPECTIVE MODELING''], Proceedings of the 2000 Winter Simulation Conference
J. A. Joines, R. R. Barton, K. Kang, and P. A. Fishwick, eds.

NASA (1994), [http://sbir.gsfc.nasa.gov/SBIR/successes/ss/10-018text.html Schedule and Cost Risk Analysis Modeling (SCRAM) System], NASA SBIR Successes.

= Not yet categorized =

Jun Long, Baruch Fischhoff (2000), [http://www.blackwell-synergy.com/doi/abs/10.1111/0272-4332.203033 ''Setting Risk Priorities: A Formal Model Risk Analysis''], Risk Analysis 20(3):339–352.

Articles that refer to Analytica

Analytica User Group

2007-10-11T20:31:58Z

Sswamy: /* Modeling Energy Efficiency in Large Data Centers */

The Analytica User Group provides a support system and various resources among existing Analytica Users.

= Webinar Series =

The Analytica webinar series uses GotoMeeting with conference calling technology as a media for regular presentations on topics of potential interest to the community of Analytica users and modelers. Webinars are interactive, with questions and tangents welcome. Webinars are a great place to learn more about Analytica and other related topics! Seats are limited. To sign up for a particular webinar, see "How to Attend" below.

Topic scope includes:
* Introduction to new Analytica 4.0 features.
* How-to: How to utilize specific Analyica features.
* Case-studies: Presentation about successful applications.
* General modeling topics. E.g., an introduction to XYZ theory, and modeling this in Analytica.
* Analytica training.

In the upcoming weeks, we expect to offer several webinars covering features new to Analyica 4.0.

Webinar presentations may last anywhere from 20 to 90 minutes, depending on the topic (an estimate of duration should be included with the topic).

Lumina may record make a recording of User Group webinars, including audio of the presenter and participants. Lumina reserves the right to make these recordings available (or not) at its discretion.

== Schedule of Upcoming Webinars ==

=== Calling External Applications ===

Date and Time: Thursday, Oct 18, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

The [[RunConsoleProcess] function runs an external program, can exchange data with that program, and can be used to perform a computation or acquire data outside of Analytica, that then can be used within the model. I'll demonstrate how this can be used with a handful of programs, and code written in several programming and scripting languages. I'll demonstrate a user-defined function that retrieves historical stock data from a web site.

=== The [[Using_References|Reference and Dereference Operators]] ===

The reference operators make it possible to represent complex data structures like trees or non-rectangular arrays, bundle heterogenous data into records, maintain arrays of local indexes, and seize control of array abstraction in a variety of scenarios. Using a reference, an array can be made to look like an atomic element to array abstraction, so that arrays of differing dimensionality can be bundled into a single array without an explosion of dimensions. The flexibilities afforded by references are generally for the advanced modeler or programmer, but once mastered, they come in useful fairly often.

=== The [[Iterate]] Function ===

With [[Iterate]], you can create a recurrent loop around a large model, which can be useful for iterating until a convergence condition is reached, for example. For complex iterations, where many variables are being updated at each iteration, requires you to structure your model appropriate, bundling and unbundling values within the single iterative loop. With some work, [[Iterate]] can be used to simulate the functionality [[Dynamic]], and thus provides one option when a second [[Time]]-like index is needed (although not nearly as convenient as [[Dynamic]]).

In this session, we'll explore how [[Iterate]] can be used.

=== Modeling Energy Efficiency in Large Data Centers ===

Date and TimeThursday, Oct 25, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter:Surya Swamy, Lumina Decision Systems

Abstract
The U.S. data center industry is witnessing a tremendous growth period stimulated by increasing demand for data processing and storage. This has resulted in a number of important implications including increased energy costs for business and government, increased emissions from electricity generation, increased strain on the power grid and rising capital costs for data center capacity expansion. In this webinar, Analytica's dynamic modeling capabilities coupled with it's advanced uncertainty capabilities, which offer tremendous support in building cost models for planning and development of energy efficient data centers will be illustrated. The model enables users to explore future technologies, the performance, costs and efficiencies of which are uncertain and hence to be probabilistically evaluated over time.

=== [[Handle]]s and [[Meta-Inference]] ===

Date and Time: TBA

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

Meta-inference refers to computations that reason about your model itself, or that actually alter your model. For example, if you were to write an expression that counted how many variables are in your model, you would be reasoning about your model. Other examples of meta inference include changing visual appearance of nodes to communicate some property, re-arranging nodes, finding objects with given properties, or even creating a transformed model based on portion of your model's structure.

The ability to implement meta-inferential algorithms in Analytica has been greatly enhanced in Analytica 4.0. The key to implementation of meta-inference is the manipulation of [[Handle]]s to objects (formerly refered to as ''varTerms''). This webinar will provide a very brief introduction to handles and using them from within expressions. I will assume you are pretty familiar with creating models and writing expressions in Analyica, but I will not assume that have previous seen or used Handles.

== How to Attend ==

To attend, you need to sign up by contacting Lumina at webinars@lumina.com or (650) 212-1212. Attendence is limited to 15 people, so don't sign up unless you sincerely intend to attend. Also, sign up at least a day prior, since the webinar may be cancelled if there are few pre-registered participants.

These Webinars are FREE to users who have an up-to-date annual maintenance subscription (MTS). If you are unsure, check with mailto:sales@lumina. For those without MTS, an attendence fee of US$100 is charged.

== How to be a Presenter ==

Being a presenter at an Analytica webinar provides an opportunity to make others in the Analytica community aware of your successes or capabilities. Consultants may find this an opportunity for exposure to others with particular modeling needs. Also, if you are an Analytica aficionado, this is a great opportunity to help others.

If you would like to be a presenter, submit your proposed topic to webinars@lumina.com and possible presentation times (include the time zone). We will schedule the GotoMeeting conference (you do not need a gotoMeeting subscription yourself) and we will make you presenter during the session, allowing you to share your screen while you talk. You will most likely make use of Power Point and a running Analytica during your presentation.

== Past Topics ==

=== Introduction to Linear and Quadratic Programming ===

Date and Time: Thursday, Oct 11, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

This talk is an introduction to linear programming and quadratic programming, and an introduction to solving LPs and QPs from inside an Analytica model (via Analytica Optimizer). LPs and QPs can be efficiently encoded using the Analytica Optimizer functions [[LpDefine]] and [[QpDefine]]. I'll introduce what a linear program is for the sake of those who are not already familiar, and examine some example problems that fit into this formalism. We'll encode a few in Analytica and compute optimal solutions. Although LPs and QPs are special cases of non-linear programs (NLPs), they are much more efficient and reliable to solve, avoid many of the complications present in non-linear optimization, and fully array abstract. Many problems that initially appear to be non-linear can often be reformulated as an LP or QP. We'll also see how to compute secondary solutions such as dual values (slack variables and reduced prices) and coefficient sensitivies. Finally, [[LpFindIIS]] can be useful for debugging an LP to isolate why there are no feasible solutions.

You can watch a recording of this webinar here: [http://AnalyticaOnline.com/WebinarArchive/2007-10-11-LP-QP-Optimization.wmv LP-QP-Optimization.wmv] (requires Windows Media Player)

The model file created during this webinar is here: [[media:LP QP User Group.ANA|LP QP Optimization.ana]]

=== Non-Linear Optimization ===

Date and Time: Thursday, Oct 4, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

This talk focuses on the problem of maximizing or minimizing an objective criteria in the presence of contraints. This problem is referred to as a non-linear program, and the capability to solve problems of this form is provided by the Analytica Optimizer via the [[NlpDefine]] function. In this talk, I'll introduce the use of [[NlpDefine]] for those who have not previously used this function, and demonstrate how NLPs are structured within Analytica models. I'll examine various challenges inherent in non-linear optimization, tricks for diagnosing these and some ways to address these. We'll also examine various ways in which to structure models for parametric analyses (e.g., array abstraction over optimization problems), and optimizations in the presence of uncertainty.

You can watch a recording of this session here: [http://AnalyticaOnline.com/WebinarArchive/2007-10-04-Nonlinear-Optimization.wmv Nonlinear-Optimization.wmv]

During the talk, these two models were created:
* [[media:Simple Nonlinear optimization.ANA|Simple Nonlinear Optimization.ana]]
* [[media:Nonlinear asset allocation.ANA|Nonlinear asset allocation.ana]]

=== Writing [[User-Defined Functions]] ===

Date and Time: Thursday, Sept. 27, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

When you need a specialized function that is not already built into Analytica, never fear -- you can create your own [[User-Defined Functions|User-Defined Function (UDF)]]. Creating UDFs in Analytica is very easy. I'll introduce this convenient capability, and demonstrate how UDFs can be organized into libraries and re-used in other models. I'll also review the libraries of functions that come with Analytica, providing dozens of additional functions.

After this introduction to the basics of UDFs, I'll dive into an in-depth look at [[Function Parameter Qualifiers]]. There is a deep richness to function parameter qualifiers, mastery of which can be used to great benefit. One of the main objectives for a UDF author, and certainly a hallmark of good modeling style, should be to ensure that the function fully array abstracts. Although this usually comes for free with simple algorithms, it is sometimes necessary to worry about this explicitly. I will demonstrate how this objective can often be achieved through appropriate function parameter qualification.

Finally, I will cover how to write a custom distribution function, and how to ensure it works with [[Mid]], [[Sample]] and [[Random]].

This talk is appropriate for Analytica modelers from beginning through expert level. At least some experience building Analytica models and writing Analytica expressions is assumed.

The model created during this webinar, complete with the UDFs written during that webinar, can be downloaded here: [[media:Writing User Defined Functions.ana|Writing User Defined Functions.ana]].

You can watch this webinar here: [http://AnalyticaOnline.com/WebinarArchive/2007-09-27-Writing-UDFs.wmv Writing-UDFs.wmv] (Windows Media Player required)

=== Modeling Markov Processes in Analytica ===

Date and Time: Thursday, Sept. 20, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter: Matthew Bingham, Principal Economist, Veritas Economic Consulting

Abstract

The class of mathematical processes characterized by dynamic dependencies between successive random variables is called Markov chains. The rich behavior and wide applicability of Markov chains make them important in a variety of applied mathematical applications including population and demographics, health outcomes, marketing, genetics, and renewable resources. Analytica’s dynamic modeling capabilities, robust array handling, and flexible uncertainty capabilities support sophisticated Markov modeling. In this webinar, a Markov modeling application is demonstrated. The model develops age-structured population simulations using a Leslie matrix structure and dynamic simulation in Analytical.

A recording of this session can be viewed at: [http://AnalyticaOnline.com/WebinarArchive/2007-09-20-Markov-Processes.wmv Markov-Processes.wmv] (requires Windows Media Player)

An article about the model presented here: [[media:AnalyticaMarkovtext.pdf|AnalyticaMarkovtext.pdf]]

=== Manipulating Dates in Analytica ===

Date and Time: Thursday, Sept. 13, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

In this talk, I'll cover numerous aspects relating to the manipulation of dates in Analytica. I'll introduce the encoding of dates as integers and the date origin preference. I'll review how to configure input variables, edit tables, or even individual columns of edit tables to accept (and parse) dates as input. I'll cover date number format capabilities in depth, including how to create your own custom date formats, understanding how date formats interact with your computer's regional settings, and how to restrict a date format to a single column only. We'll also see how axis scaling in graphs is date-aware.

Next, we'll examine various ways to manipulate dates in Analytica expressions. This includes use of the new and powerful functions [[MakeDate]], [[DatePart]], and [[DateAdd]], and some interesting ways in which these can be used, for example, to define date sequences. Finally, we'll practice our array mastery by aggregating results to and from different date granularities, such aggregating from a month sequence to a years, or interpolating from years to months.

The model file resulting by the end of the session is available here: [[media:Manipulating Dates in Analytica.ana|Manipulating Dates in Analytica.ana]].

You can watch a recording of this webinar here: [http://AnalyticaOnline.com/WebinarArchive/2007-09-13-Manipulating-Dates.wmv Manipulating Dates.wmv] (Windows Media Player required) Unfortunately, this one seems to have recorded poorly -- the video size is too small. If you magnify it in your media player, it does become readable. Sorry -- I don't know why it recorded like this.

=== Button Scripting ===

Date and Time: Thursday, Sept. 6, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter: Max Henrion, Lumina Decision Systems

Abstract

This webinar is an introduction to Analytica's typescript and button scripting. Unlike variable definitions, button scripts can have side-effects, and this can be useful in many circumstances. I'll cover the syntax of typescript (and button scripts), and how scripts can be used from buttons, picture nodes or choice inputs. I'll introduce some of the Analytica scripting language to those who may have seen or used it before. And we'll examine some ways in which button scripting can be used.

You can watch the recording of this webinar here: [http://AnalyticaOnline.com/WebinarArchive/2007-09-06-Button-Scripting.wmv Button Scripting.wmv] (Requires Windows Media Player or equiv)

=== Using [[Regression]] ===

Date and Time: Thursday, Aug 30, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

Regression analysis is a statistical technique for curve fitting, discovering relationships in data, and testing hypotheses between variables. In this webinar, I will focus on generalized linear regression, which is provided by Analytica's [[Regression]] function, and examine many ways in which is can be used, including fitting simple lines to data, polynomial regression, use of other non-linear terms, and fitting of autoregressive models (e.g., ARMA). I'll examine how we can assess how likely it is the data might have been generated from the particular form of the regression model used. We can also determine the level of uncertainty in our inferred parameter values, and incorporate these uncertainties into a model that uses the result of the regression. The talk will cover Analytica 4.0 functions [[Regression]], [[RegressionDist]], [[RegressionFitProb]], and [[RegressionNoise]].

The model developed during the course of this session is downloadable from here:
[[media:Using Regression.ana|Using Regression.ana]]

=== Creating Scatter Plots ===

Date and Time: Thursday, Aug 23, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

This webinar focuses on utilizing graphing functionality new to Analytica 4.0, and specifically, functionality enabling the creative use of scatter plots. The talk will focus primarily on techniques for simultaneously displaying many quantities on a single 2-D graph. I'll discuss several methods in which multiple data sources (i.e., variable results) can be brought together for display in a single graph, including the use of result comparison, comparison indexes, and external variables. I'll describe the basic new graphing-role / filler-dimension structure for advanced graphing in Analytica 4.0, enabling multiple dimensions to be displayed on the horizontal and vertical axes, or as symbol shape, color, or symbol size, and how all these can be rapidly pivoted to quickly explore the underlying data. I'll discuss how graph settings adapt to changes in pivot or result view (such as Mean, Pdf, Sample views).

Model used: During this webinar, I started with some example data in the model [[media:Chemical elements orig.ANA|Chemical elements.ana]]. The original file is in the form before graph settings were changed. By the end of the webinar, many graph settings had been altered, and various changes made, resulting in [[media:Chemical elements2.ANA|Chemical elements2.ana]]. These models are very useful for people who didn't attend. For those who did attend, who might want to try repeating the steps involved in setting up scatter plots, I think it is better starting with the original model.

=== Statistical Functions in Analytica 4.0 ===

Date and Time: Thursday, Aug 16, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

A statistical function is a function that process a data set containing many sample points, computing a "statistic" that summarizes the data. Simple examples are [[Mean]] and [[Variance]], but more complex examples may return matrices or tables. In this talk, I'll review statistical functions that are built into Analytica 4.0. In Analytica 4.0, all built-in statistical functions can now be applied to historical data sets over an arbitrary index, as well as to uncertain samples (the Run index), eliminating the need for separate function libraries. I will demonstrate this use, as well as several new statistical functions, e.g., [[Pdf]], [[Cdf]], [[Covariance]]. I will explain how the domain attribute should be utilized to indicate that numeric-valued data is discrete (such as integer counts, for example), and how various statistical functions (e.g., [[Frequency]], [[GetFract]], [[Pdf]], [[Cdf]], etc) make use of this information. In the process, I'll demonstrate numerous examples using these functions, such things as inferring sample covariance or correlation matricies from data, quickly histogramming arbitrary data and using the coordinate index setting to plot it, or using a weighted [[Frequency]] for rapid aggregation.

In addition, all statistical functions in Analytica 4.0 can compute weighted statistics, where each point is assigned a different weight. I'll cover the basics of sample weighting, and demonstrate some simple examples of using this for computing a Bayesian posterior and for importance sampling from an extreme distribution.

The Analytica model file that had resulted by the end of the presentation can be downloaded here:
[[media:User Group Webinar - Statistical Functions.ANA | User Group Webinar - Statistical Functions.ANA]].

=== Manipulating Indexes and Arrays in Analytica Expressions ===

Date and Time: Thursday, Aug 9, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

In this webinar, I will review many of the common operations applied to indexes and arrays from within Analytica expressions, with a particular emphasis on enhancements in this area that are new to Analytica 4.0. I'll review the often used and very powerful [[Subscript]] and [[Slice]] operations, along with the [[Associative_vs._Positional_Indexing|duality of associational and positional indexing]]. I'll introduce newly introduced extensions for positional indexes, such as the @I, A[@I=n], and @[I=n] operations, and extensions that expose positional duals to various previously-existing associational array functions. I will describe the distinction between index and value contexts in Analytica expressions, along with the distinction between a variable's index value, mid value and sample value, how these may differ ([[Self-Indexed Arrays]]), and how we may access each context-value explicitly. I will also introduce slice assignment -- the ability to assign values to individual slices of an array within an algorithm.

The content of this webinar is most appropriate for users with moderate to advanced Analyica model-building experience.

Here is the Analytica model that was created during this talk: [[media:Indexes and Arrays UG2.ANA | "Indexes and Arrays UG2.ANA"]]. (This wouldn't be very interesting for someone who didn't attend, but it contains the examples we tried).

=== Edit Table Enhancements in Analytica 4.0 ===

Date and Time: Thursday, Aug 2, 2007 at 10:00 - 11:00am Pacific Daylight Time

Presenter: Lonnie Chrisman, Lumina Decision Systems

Abstract

In this webinar, I will demonstrate several new edit table functionalities in Analytica 4.0, including:
* Insert [[Choice]] drop-down controls in table cells.
* [[Table Splicing|Splicing]] tables based on computed indexes.
* Customizing the [[TableCellDefault|default cell value(s)]].
* Blank cells to catch entries that need to be filled in.
* [[SubTable]]s
* Using different number formats for each column.

This talk is oriented for model builders with Analytica model-building experience.

The Analytica session that existed by the end of the talk is stored in the following model file: [[Media:Edit_Table_Features.ANA | "Edit Table Features.ana"]].

== Potential future topics ==

If you would like to see a webinar on a given topic, please feel free to add it here. If you see a topic listed and would like to be a presenter, let us know.

* Rapid review of features new to Analytica 4.0.

* Importance Sampling. Global sample weighting and use of extended statistic functions in 4.0.

* Topics in Regression: Using the [[Regression]] function in flexible ways, such as infering VARMA models, DFTs, etc. Estimating secondary statistics (i.e., uncertainty in the parameters) and modeling the full uncertainty in resulting model predictions (the [[RegressionDist]] function, etc). Alternative regression models ([[Probit_Regression]], [[Logistic_Regression]], [[Possion_Regression]], etc).

* Logistic Regression: Quick intro to logistic regression (generalized regression), including [[Probit_Regression]], [[Logistic_Regression]], [[Possion_Regression]], etc. Using these to fit probability estimates in Analyica, Analyica Optimizer experience, exploring result with new graphing features.

* New 4.0 graphing features general overview

* Creating graphs of multi-dimensional data.

* Graph Style Templates: Creating libraries of style templates, including some behind-the-scenes settings.

* An in-depth look at graph style settings.

* MdxQuery - Interacting with Microsoft Analysis Services or other OLAP servers.

* Producing graphs from ADE: Including how to serve graphs from web pages.

* Manipulating dates in Analytica

* Integrating with external programs: Utilizing external programs or scripts (VBScript, Perl, etc) from within an Analytica model.

* An introduction to button scripting in Analytica

* New extensions to the Analytica Optimizer

* Introduction to Linear, Quadratic and Non-Linear Programming, and the basics of using Analytica Optimizer.

* Intracacies of the Domain attribute

* Writing user-defined functions. Understanding parameter qualifiers. Introduction to new 4.0 qualifiers. Writing distribution functions. Writing array abstractable functions.

* Mastering Array Abstraction

* Sampling and Distributions - new 4.0 additions/extensions. Also, writing custom User-Defined Distribution Functions.

* Integrating with external applications using [[RunConsoleProcess]].

* [[Handle]]s and [[Meta-Inference]].

* Using references (the \ and # operators)

* Getting data into Analytica.

* [[DetermTable]]s

* Implementing iterative algorithms. Convergence algorithms using [[Iterate]]. Use of [[While..Do]]. Recursive functions, slice assignment, etc.

* Large-scale sampling. Techniques when memory limitations constrain sampleSize.

* Creating User-Defined functions. Review of [[Function Parameter Qualifiers]].

* Understanding [[Evaluation Contexts]]

* Bayesian Inference

* Sensitivity Analysis.

* Dynamic Programming

* Correlated and multi-dimensional distributions

== User Survey Results ==

During the first week of September, we sent out a survey to people who had attended webinars so far. Please continue providing us with feedback. Here is some feedback to date:

Level of difficult and speed:

So far, of those who answered this question, 25% say "a bit too easy", 25% say "too hard/fast", and 50% say "just right".

Topics requested for future webinars:

* How to run multiple iterations, e.g. 100 iterations with uncertainty sample of 1000.

* Sampling for rare events.

* Using the lognormal function.

* Financial modeling

* New array functionality (subtables, choices in tables)

* Optimizer !!!

* Dynamic models (twice requested)

* Choice of distributions

* Re-sampling and radomize methods and uncertainty sample size

* Tricks for sensitivity analysis

* Input and output nodes

* Importance analysis

= The Analytica Wiki =

The [[Analytica Wiki ]] contains many resources, including in-depth reference materials, relevant articles, example models, tutorials, etc., to help users master Analytica and find what they need. Even better, Analytica users can contribute! You can upload your own models, articles, expand on or correct materials that are there, etc., for the benefit of the entire Analytica community.

= The Analytica Forum =

The [http://revelarium.com/phpBB/index.php Analytica Forum] is a message board where users can post questions to the Analytica community, or view what others have posted. Many materials of general interest have been posted there (however, we hope to eventually update the Wiki to reflect all this material, where it can be more conveniently organized). The forum is maintained indepedently by an enthusiastic Analytica user. Our thanks to to Mike for all his efforts!

Number format

2007-08-23T23:06:19Z

Sswamy:

[[Category:Ana: Status M]] 
[[What's new in Analytica 4.0?]] >

You can use the Number format dialog to set format for numbers in tables, graphs, and input and output nodes. If you set the number format for an Index, for example, it will use that format for that index in every graph or table that uses that index.

To set the number format for a variable:
# Select its node in a diagram, or display its result or edit table window. (You can also set number format for several variables, if you select their nodes.)
# Select '''Number format...''' from the '''Result''' menu, or press ''control-b'', to open the Number format dialog.
# Select the option you want from the Formats list.
# Select options such as Decimal digits, Show trailing zeroes, or Thousands separators depending on what you want and what's available. See below for details.
# Check the example at the top of the dialog to see if it's what you want.
# If so, click '''Apply''' button.

For details on date formats see [[Date Functions]]

[[Image:Number format.png]]

Fixed Point (Currency checked)

[[Image:Fixed_point_currency.png]]

Currency symbol pulldown

[[Image:Symbol.png]]

Currency symbol pulldown

[[Image:Placement.png]]

Date format, custom selected

[[Image:Custom_date.png]]

[Expanded in release 4.0]

Number format

2007-08-23T23:05:09Z

Sswamy:

[[Category:Ana: Status M]] 
[[What's new in Analytica 4.0?]] >

You can use the Number format dialog to set format for numbers in tables, graphs, and input and output nodes. If you set the number format for an Index, for example, it will use that format for that index in every graph or table that uses that index.

To set the number format for a variable:
# Select its node in a diagram, or display its result or edit table window. (You can also set number format for several variables, if you select their nodes.)
# Select '''Number format...''' from the '''Result''' menu, or press ''control-b'', to open the Number format dialog.
# Select the option you want from the Formats list.
# Select options such as Decimal digits, Show trailing zeroes, or Thousands separators depending on what you want and what's available. See below for details.
# Check the example at the top of the dialog to see if it's what you want.
# If so, click '''Apply''' button.

For details on date formats see [[Date Functions]]

[[Image:Number format.png]]

[[Image:Fixed_point_currency.png]]

[[Image:Symbol.png]]

[[Image:Placement.png]]

[[Image:Custom_date.png]]

[Expanded in release 4.0]

2007-08-21T00:17:50Z

2007-08-21T00:08:15Z

Sswamy: /* Chart Type tab */

[[What's new in Analytica 4.0?]] >

Analytica 4.0 offers a rich array of new styles and options for graphs. If you are familiar only with previous releases, you will find it helpful at least to skim through these options.

= Overview =

When you display the Result of a variable, it shows it as a table or graph, according to how you last viewed it. The first time you view a Result, it shows as a Graph, unless you changed the '''Default result view''' in the '''Preferences''' dialog.

When displaying a graph, Analytica uses the default graphing settings, unless you have selected other settings for it. You can modify these with the Graph setup dialog.

[Analytica 4.0 no longer offers direct graphing in Excel, since almost all those options are now available from within Analytica.]

== Graph setup dialog ==

To open the '''Graph setup''' dialog for a graph, select '''Graph Setup...''' from the '''Result''' menu, or simply double click on the graph window.

The graph setup dialog has six tabs. All tabs show the template panel and these three buttons:

'''Apply:''' Apply any changes to settings to the current graph, and close the dialog.

'''Set Default:''' Save any changed settings on the current tab as the default for all graphs, and close the dialog. It does not affect any settings that you have not changed since you opened the Graph setup dialog. Changing a default will affect all graphs that use the default, but not graphs for you override the default (in the past or future).

'''Cancel:''' Close the dialog without changing or saving anything.

=== Chart Type tab ===

[[Image:Chart_type.png]]

This tab shows options for modifying the style and arrangement of the graph:

Line style:
* Straight line segments join the data points
* Straight line segments, with a symbol at each data point.
* No lines, with a symbol at each data point.
* No lines, with a pixel at each data point.
* A vertical line and horizontal line from each data point to the next.
* A bar for each discrete x value, with height showing the y value.

Swap horizontal and vertical axes: Check this box to exchange the x and y axes, so that x axis is vertical and y axis is horizontal. For bar graphs with long labels for each x element, it is often helpful to swap axes so that these labels will fit horizontally.

3-D effects: Check to use three-dimensional style to view graphs. for bar graph line style, it will offer the choice of Box or Cylindrical shapes for the bars.

Line style settings:
* Area fill: Check to fill in the area beneath each line with a solid color. If there are multiple lines, the Graph will have a Key index. The fill areas are drawn from last to first element of the Key index -- which works well if the y values are sorted from smallest to largest over the key index. Otherwise, some values will obscure others.
* Transparency: Drag the cursor to change transparency of fill colors between opaque and transparent. Some transparency lets you see fill lines and areas that might be behind others.
* Line thickness: Select the thickness of lines to display. (Does not apply for styles without lines.)
* Use separate color/symbol keys: Check if to get two key indexes, one indicated by color and the second by symbol type or size.
* Allow variable symbol size: Check to have symbol size vary with y value.
* Symbol size: Enter a number in typographic points to specify size of symbols.
* Min symbol size and Max symbol size: If you check Allow variable symbol size, specify the range of symbol sizes (in typographic points) for the smallest and largest.

Bar graph settings:
* Stacked bars: Check to show bars stacked one on top of the other over the Key index, instead of side by side. The values for each bar are cumulated over the Key index.
* Variable origin: Check if you want to set the origin (starting point) for each bar other than zero (the default). The graph will then display a Bar Origin menu to let you select the Bar origin.
* Bar overlap: With stacked bars, they overlap 100%. You can specify partial overlap between 0 and 100%.

=== Axis Ranges tab ===

[[Image:Axis_Ranges.png]]

This tab lets you control the appearance of each axis.
* '''Autoscale:''' Uncheck this box if you want to specify the range for the axis, instead of letting Analytica select the range automatically to include all values.
* '''Max and Min:''' The maximum and minimum values of the range to use when you have unchecked Autoscale.
* '''Include zero''': Check if you want to include the origin (zero) in the range.
* '''Approx. # ticks''': Specify the number of tick marks displayed along the axis. Analytica may not match the number exactly, in the interests of clarity.
* '''Reverse order''': Check if you want to show the values ordered from large to small instead of the default small to large.
* '''Categorical''': Treat this axis as categorical. Usually, Analytica figures out the quantity is categorical without help. Occasionally, if the values are numerical, you may want to control it yourself.
* '''Log scale''': Check if you want to display this on a log scale. This is useful for numbers that vary by several orders of magnitude. It uses a "double log" scale with zero if the values include negative and positive numbers.

'''Set default''': If you have changed settings for an axis that is an index (not the main variable), clicking this button will apply these settings for that index for all graphs that use that index. For example, if the scale is the Index Time, you can use this to change the Time scale (e.g. start and end time) for all graphs that display a value over Time.

=== Style tab ===

[[Image:Style.png]]

Grid: Select the radio button to control the display of the grid over the graphing area. You can also select the color.

Frame: Select the radio button to control the display of the lines framing the graphing area. You can also select the color for the frame. It usually looks best to have the Frame the same color as the Grid, or a darker shade of the same color.

Tick marks: The top radio buttons control where to show tick marks. The lower ones control how they are displayed.

Display key: Select radio button to control where to display the Key on the graph. Select the Border check box to display an outline rectangle around the key.

=== Fonts tab ===

[[Image:Text.png]]

[[Image:Font_image3.png]]

This tab lets you select the font, size, style, and color for four types of text on the graph, the Axis titles, the Axis labels (i.e. text labeling along the graph), the Key title and the key labels (i.e. text appearing in the key). Graphic designers recommend that you use the same font and color for all text on a graph, and vary only the size.

'''Axis Label Rotation:''' Specify degrees of rotation from -90 to 90 to rotate the labels for either axis. For example, for a bar graph with long labels along its horizontal axis, you can rotate them so that they fit without getting truncated.

=== Background tab ===

[[Image:Background.png]]

This tab lets you control the color or pattern that appears on the graph background. The main area covers the entire graph window (exclusive of slicer and index selections). The plot area is the rectangle showing the graph values. If you specify a fill color or pattern for the Main area, the main fill will also be the background for the Plot area and Key if they are set to "None".

Fill: The options are
* None: No fill. Default to white background.
* Solid: Use a solid fill with the Color selected.
* Gradient: Use a gradient of color, going from Color 1 to Color 2, in the direction you select from Gradient style.
* Hatch: Use a hatched fill using the selected Hatch Style with Color 1 and Color 2.

Good graphic designers recommend avoiding Hatch backgrounds, and using solid or gradient backgrounds with mild colors, if at all. The data lines, points, and text should not be overwhelmed by the background.

=== Preview tab ===

[[Image:Preview.png]]

The '''Preview''' tab shows the graph with the current settings so that you can easily see the effects of settings for this graph.

== [[XY comparison]] or XY Coordinate sources ==

[[XY comparison]] lets you plot one variable on vertical (Y) axis against the other on the horizontal (X) axis.

== [[Graph Setting Associations]] ==
Graph settings now have a richer set of [[Graph Setting Associations|associations]], designed to allow intelligent transitions when multi-dimensional results are pivoted and when the graphing mode (Mid, Bands, PDF, etc) is changed.

For example, axis range settings are associated with a particular index, so if the graph is pivoted, the setting follows the pivot and isn't suddenly applied to a horizontal axis where it no longer makes sense.

Line style settings are associated with a combination of graph view mode and categorical / continuous distinction. Thus, it is possible to have a probability mass plot draw as a bar graph, a sample as a scatter plot, and bands as a line plot, all with a single graph setting.

Associations also impact how how settings transfer when "Set Default" is used.

== [[Graphing Dimensions and Roles]] ==

A general and flexible system of graphing dimensions and graphing roles allows a very rich space of chart types to be created using only a few elementary building blocks. The mechanism also allows many dimensions to be reflected on a single graph.

A computed value or an index can serve as a graphing dimension. Graphing dimensions are then assigned to graphing roles, and the user can easily pivot the graphing roles to alter the assignment of dimensions to roles. By assigning graphing dimensions to roles, a user can view many dimensions at once, and compare multiple values on the same graph.

Selectable graphing roles include X-axis, Y-axis, combined Color/Symbol Key, and [[Bar Origin]], and may soon include Symbol Size and separate Color and Symbol Keys.

While Analytica 3.x allowed a single external X-value to be plotted against a result, the new system allows any number of external variables to be included and compared in the same graph.

== [[Selecting Data for Graphing]] ==

The structure of data used to create a plot is now much more flexible. A [[Coordinate Indexes|Coordinate Index]] can be used to plot data that is organized in columns, without having to break the data into multiple variables. Multiple external variables can be merge into the plot as graphing dimensions. In a [[Scatter Plots|Scatter Plot]], you can pivot both X and Y axes to explore multi-dimensional data from many "angles".

== [[Categorical and Continuous Plots]] ==

Analytica 4.0 gives much more attention and consistency to the treatment of Categorical, Continuous and Discrete results.

The Discrete vs. Continuous distinction is determined by [[the Domain Attribute]], and determines whether probability plots are density and cumulative density plots (continuous) or probability mass and cumulative probability (discrete) plots.

The [[Categorical vs. Continuous distinction]] determines how a graphing axis is laid out. Continuous dimensions require numeric values. The determination of whether a graphing dimension is categorical or continuous is partially determined by the domain attribute, but the values actually occuring in the dimension, by the chart type (bar or non-bar chart), and by the Categorical checbox in the axis range setting.

Analytica maintains separate line-style settings for categorical and continuous plots. (The running axis of a plot, usually x-axis, determines whether the plot is continuous or categorical). Thus, by pivoting a continuous dimension to the x-axis to replace what was a categorical dimension, a graph may change from a bar graph to line graph, for example.

== Richer [[Plot Style Examples|Plots Types]] ==

Using combinations of the above named features, a wide variety of chart styles can be created, which were not possible previously. These include:
;; Bar Chart variations
* Stacked Bars
* Segmented Bar charts
* Horizontal [[Tornado Plots]]
* [[Gantt Charts]]
* [[Candle bars]] (High-close-open-low stock charts)
;; Scatter plot variations
* Multi-D depiction of data in columns
* [[Bubble plots]]
;; Line plot variations
* Standard (continuous) line plots
* XY parametric plots
* [[Log Plots]] (axis log scaling)
* Sideways plots

== [[Graph Appearance Settings]] ==

Analytica 4.0 exposes many appearance settings to user control, allowing the production of "board room quality" charts. These include:

* Control of background fills/patterns (solid, gradient, hatch).
* Full control of fonts (color, face, size, bold, etc)
* Location of Key.
* Grid and tic styles
* Axis label rotations
* Three-D effects (solid bars, ribbons)
* Filled line graphs (w/ transparency control)

== [[Graph Style Templates]] ==

[[Graph Style Templates|Graph style templates]] let you save the settings for a graph into a named template, so you can reuse it for other graphs. For example, can create a set of templates to provide a consistent visual style for a model, or for all models created by your organization.

== Export graph image type ==

You can export a graph as an image file in most common formats, including BMP, JPEG, TFF, PNG, and Enhanced Windows Metafile (EMF):

# Display the graph the way you want.
# Select '''Export...''' from the '''File''' menu, to open a file browser dialog '''Save Graph Image as...'''.
# If you want to change the defaults, edit the '''File name''' and select the '''Save as type''' -- i.e. the file format.
# Click '''Save'''.

Graph settings

2007-08-20T18:22:24Z

Sswamy: /* Fonts tab */

[[What's new in Analytica 4.0?]] >

Analytica 4.0 offers a rich array of new styles and options for graphs. If you are familiar only with previous releases, you will find it helpful at least to skim through these options.

= Overview =

When you display the Result of a variable, it shows it as a table or graph, according to how you last viewed it. The first time you view a Result, it shows as a Graph, unless you changed the '''Default result view''' in the '''Preferences''' dialog.

When displaying a graph, Analytica uses the default graphing settings, unless you have selected other settings for it. You can modify these with the Graph setup dialog.

[Analytica 4.0 no longer offers direct graphing in Excel, since almost all those options are now available from within Analytica.]

== Graph setup dialog ==

To open the '''Graph setup''' dialog for a graph, select '''Graph Setup...''' from the '''Result''' menu, or simply double click on the graph window.

The graph setup dialog has six tabs. All tabs show the template panel and these three buttons:

'''Apply:''' Apply any changes to settings to the current graph, and close the dialog.

'''Set Default:''' Save any changed settings on the current tab as the default for all graphs, and close the dialog. It does not affect any settings that you have not changed since you opened the Graph setup dialog. Changing a default will affect all graphs that use the default, but not graphs for you override the default (in the past or future).

'''Cancel:''' Close the dialog without changing or saving anything.

=== Chart Type tab ===

[[Image:Graph_setup.png]]

This tab shows options for modifying the style and arrangement of the graph:

Line style:
* Straight line segments join the data points
* Straight line segments, with a symbol at each data point.
* No lines, with a symbol at each data point.
* No lines, with a pixel at each data point.
* A vertical line and horizontal line from each data point to the next.
* A bar for each discrete x value, with height showing the y value.

Swap horizontal and vertical axes: Check this box to exchange the x and y axes, so that x axis is vertical and y axis is horizontal. For bar graphs with long labels for each x element, it is often helpful to swap axes so that these labels will fit horizontally.

3-D effects: Check to use three-dimensional style to view graphs. for bar graph line style, it will offer the choice of Box or Cylindrical shapes for the bars.

Line style settings:
* Area fill: Check to fill in the area beneath each line with a solid color. If there are multiple lines, the Graph will have a Key index. The fill areas are drawn from last to first element of the Key index -- which works well if the y values are sorted from smallest to largest over the key index. Otherwise, some values will obscure others.
* Transparency: Drag the cursor to change transparency of fill colors between opaque and transparent. Some transparency lets you see fill lines and areas that might be behind others.
* Line thickness: Select the thickness of lines to display. (Does not apply for styles without lines.)
* Use separate color/symbol keys: Check if to get two key indexes, one indicated by color and the second by symbol type or size.
* Allow variable symbol size: Check to have symbol size vary with y value.
* Symbol size: Enter a number in typographic points to specify size of symbols.
* Min symbol size and Max symbol size: If you check Allow variable symbol size, specify the range of symbol sizes (in typographic points) for the smallest and largest.

Bar graph settings:
* Stacked bars: Check to show bars stacked one on top of the other over the Key index, instead of side by side. The values for each bar are cumulated over the Key index.
* Variable origin: Check if you want to set the origin (starting point) for each bar other than zero (the default). The graph will then display a Bar Origin menu to let you select the Bar origin.
* Bar overlap: With stacked bars, they overlap 100%. You can specify partial overlap between 0 and 100%.

=== Axis Ranges tab ===

[[Image:Axis_Ranges.png]]

This tab lets you control the appearance of each axis.
* '''Autoscale:''' Uncheck this box if you want to specify the range for the axis, instead of letting Analytica select the range automatically to include all values.
* '''Max and Min:''' The maximum and minimum values of the range to use when you have unchecked Autoscale.
* '''Include zero''': Check if you want to include the origin (zero) in the range.
* '''Approx. # ticks''': Specify the number of tick marks displayed along the axis. Analytica may not match the number exactly, in the interests of clarity.
* '''Reverse order''': Check if you want to show the values ordered from large to small instead of the default small to large.
* '''Categorical''': Treat this axis as categorical. Usually, Analytica figures out the quantity is categorical without help. Occasionally, if the values are numerical, you may want to control it yourself.
* '''Log scale''': Check if you want to display this on a log scale. This is useful for numbers that vary by several orders of magnitude. It uses a "double log" scale with zero if the values include negative and positive numbers.

'''Set default''': If you have changed settings for an axis that is an index (not the main variable), clicking this button will apply these settings for that index for all graphs that use that index. For example, if the scale is the Index Time, you can use this to change the Time scale (e.g. start and end time) for all graphs that display a value over Time.

=== Style tab ===

[[Image:Style.png]]

Grid: Select the radio button to control the display of the grid over the graphing area. You can also select the color.

Frame: Select the radio button to control the display of the lines framing the graphing area. You can also select the color for the frame. It usually looks best to have the Frame the same color as the Grid, or a darker shade of the same color.

Tick marks: The top radio buttons control where to show tick marks. The lower ones control how they are displayed.

Display key: Select radio button to control where to display the Key on the graph. Select the Border check box to display an outline rectangle around the key.

=== Fonts tab ===

[[Image:Text.png]]

[[Image:Font_image3.png]]

This tab lets you select the font, size, style, and color for four types of text on the graph, the Axis titles, the Axis labels (i.e. text labeling along the graph), the Key title and the key labels (i.e. text appearing in the key). Graphic designers recommend that you use the same font and color for all text on a graph, and vary only the size.

'''Axis Label Rotation:''' Specify degrees of rotation from -90 to 90 to rotate the labels for either axis. For example, for a bar graph with long labels along its horizontal axis, you can rotate them so that they fit without getting truncated.

=== Background tab ===

[[Image:Background.png]]

This tab lets you control the color or pattern that appears on the graph background. The main area covers the entire graph window (exclusive of slicer and index selections). The plot area is the rectangle showing the graph values. If you specify a fill color or pattern for the Main area, the main fill will also be the background for the Plot area and Key if they are set to "None".

Fill: The options are
* None: No fill. Default to white background.
* Solid: Use a solid fill with the Color selected.
* Gradient: Use a gradient of color, going from Color 1 to Color 2, in the direction you select from Gradient style.
* Hatch: Use a hatched fill using the selected Hatch Style with Color 1 and Color 2.

Good graphic designers recommend avoiding Hatch backgrounds, and using solid or gradient backgrounds with mild colors, if at all. The data lines, points, and text should not be overwhelmed by the background.

=== Preview tab ===

[[Image:Preview.png]]

The '''Preview''' tab shows the graph with the current settings so that you can easily see the effects of settings for this graph.

== [[XY comparison]] or XY Coordinate sources ==

[[XY comparison]] lets you plot one variable on vertical (Y) axis against the other on the horizontal (X) axis.

== [[Graph Setting Associations]] ==
Graph settings now have a richer set of [[Graph Setting Associations|associations]], designed to allow intelligent transitions when multi-dimensional results are pivoted and when the graphing mode (Mid, Bands, PDF, etc) is changed.

For example, axis range settings are associated with a particular index, so if the graph is pivoted, the setting follows the pivot and isn't suddenly applied to a horizontal axis where it no longer makes sense.

Line style settings are associated with a combination of graph view mode and categorical / continuous distinction. Thus, it is possible to have a probability mass plot draw as a bar graph, a sample as a scatter plot, and bands as a line plot, all with a single graph setting.

Associations also impact how how settings transfer when "Set Default" is used.

== [[Graphing Dimensions and Roles]] ==

A general and flexible system of graphing dimensions and graphing roles allows a very rich space of chart types to be created using only a few elementary building blocks. The mechanism also allows many dimensions to be reflected on a single graph.

A computed value or an index can serve as a graphing dimension. Graphing dimensions are then assigned to graphing roles, and the user can easily pivot the graphing roles to alter the assignment of dimensions to roles. By assigning graphing dimensions to roles, a user can view many dimensions at once, and compare multiple values on the same graph.

Selectable graphing roles include X-axis, Y-axis, combined Color/Symbol Key, and [[Bar Origin]], and may soon include Symbol Size and separate Color and Symbol Keys.

While Analytica 3.x allowed a single external X-value to be plotted against a result, the new system allows any number of external variables to be included and compared in the same graph.

== [[Selecting Data for Graphing]] ==

The structure of data used to create a plot is now much more flexible. A [[Coordinate Indexes|Coordinate Index]] can be used to plot data that is organized in columns, without having to break the data into multiple variables. Multiple external variables can be merge into the plot as graphing dimensions. In a [[Scatter Plots|Scatter Plot]], you can pivot both X and Y axes to explore multi-dimensional data from many "angles".

== [[Categorical and Continuous Plots]] ==

Analytica 4.0 gives much more attention and consistency to the treatment of Categorical, Continuous and Discrete results.

The Discrete vs. Continuous distinction is determined by [[the Domain Attribute]], and determines whether probability plots are density and cumulative density plots (continuous) or probability mass and cumulative probability (discrete) plots.

The [[Categorical vs. Continuous distinction]] determines how a graphing axis is laid out. Continuous dimensions require numeric values. The determination of whether a graphing dimension is categorical or continuous is partially determined by the domain attribute, but the values actually occuring in the dimension, by the chart type (bar or non-bar chart), and by the Categorical checbox in the axis range setting.

Analytica maintains separate line-style settings for categorical and continuous plots. (The running axis of a plot, usually x-axis, determines whether the plot is continuous or categorical). Thus, by pivoting a continuous dimension to the x-axis to replace what was a categorical dimension, a graph may change from a bar graph to line graph, for example.

== Richer [[Plot Style Examples|Plots Types]] ==

Using combinations of the above named features, a wide variety of chart styles can be created, which were not possible previously. These include:
;; Bar Chart variations
* Stacked Bars
* Segmented Bar charts
* Horizontal [[Tornado Plots]]
* [[Gantt Charts]]
* [[Candle bars]] (High-close-open-low stock charts)
;; Scatter plot variations
* Multi-D depiction of data in columns
* [[Bubble plots]]
;; Line plot variations
* Standard (continuous) line plots
* XY parametric plots
* [[Log Plots]] (axis log scaling)
* Sideways plots

== [[Graph Appearance Settings]] ==

Analytica 4.0 exposes many appearance settings to user control, allowing the production of "board room quality" charts. These include:

* Control of background fills/patterns (solid, gradient, hatch).
* Full control of fonts (color, face, size, bold, etc)
* Location of Key.
* Grid and tic styles
* Axis label rotations
* Three-D effects (solid bars, ribbons)
* Filled line graphs (w/ transparency control)

== [[Graph Style Templates]] ==

[[Graph Style Templates|Graph style templates]] let you save the settings for a graph into a named template, so you can reuse it for other graphs. For example, can create a set of templates to provide a consistent visual style for a model, or for all models created by your organization.

== Export graph image type ==

You can export a graph as an image file in most common formats, including BMP, JPEG, TFF, PNG, and Enhanced Windows Metafile (EMF):

# Display the graph the way you want.
# Select '''Export...''' from the '''File''' menu, to open a file browser dialog '''Save Graph Image as...'''.
# If you want to change the defaults, edit the '''File name''' and select the '''Save as type''' -- i.e. the file format.
# Click '''Save'''.

File:Text.png

2007-08-20T18:21:26Z

Sswamy:

Graph settings

2007-08-07T22:59:31Z

Sswamy: /* Fonts tab */

[[What's new in Analytica 4.0?]] >

Analytica 4.0 offers a rich array of new styles and options for graphs. If you are familiar only with previous releases, you will find it helpful at least to skim through these options.

= Overview =

When you display the Result of a variable, it shows it as a table or graph, according to how you last viewed it. The first time you view a Result, it shows as a Graph, unless you changed the '''Default result view''' in the '''Preferences''' dialog.

When displaying a graph, Analytica uses the default graphing settings, unless you have selected other settings for it. You can modify these with the Graph setup dialog.

[Analytica 4.0 no longer offers direct graphing in Excel, since almost all those options are now available from within Analytica.]

== Graph setup dialog ==

To open the '''Graph setup''' dialog for a graph, select '''Graph Setup...''' from the '''Result''' menu, or simply double click on the graph window.

The graph setup dialog has six tabs. All tabs show the template panel and these three buttons:

'''Apply:''' Apply any changes to settings to the current graph, and close the dialog.

'''Set Default:''' Save any changed settings on the current tab as the default for all graphs, and close the dialog. It does not affect any settings that you have not changed since you opened the Graph setup dialog. Changing a default will affect all graphs that use the default, but not graphs for you override the default (in the past or future).

'''Cancel:''' Close the dialog without changing or saving anything.

=== Chart Type tab ===

[[Image:Graph_setup.png]]

This tab shows options for modifying the style and arrangement of the graph:

Line style:
* Straight line segments join the data points
* Straight line segments, with a symbol at each data point.
* No lines, with a symbol at each data point.
* No lines, with a pixel at each data point.
* A vertical line and horizontal line from each data point to the next.
* A bar for each discrete x value, with height showing the y value.

Swap horizontal and vertical axes: Check this box to exchange the x and y axes, so that x axis is vertical and y axis is horizontal. For bar graphs with long labels for each x element, it is often helpful to swap axes so that these labels will fit horizontally.

3-D effects: Check to use three-dimensional style to view graphs. for bar graph line style, it will offer the choice of Box or Cylindrical shapes for the bars.

Line style settings:
* Area fill: Check to fill in the area beneath each line with a solid color. If there are multiple lines, the Graph will have a Key index. The fill areas are drawn from last to first element of the Key index -- which works well if the y values are sorted from smallest to largest over the key index. Otherwise, some values will obscure others.
* Transparency: Drag the cursor to change transparency of fill colors between opaque and transparent. Some transparency lets you see fill lines and areas that might be behind others.
* Line thickness: Select the thickness of lines to display. (Does not apply for styles without lines.)
* Use separate color/symbol keys: Check if to get two key indexes, one indicated by color and the second by symbol type or size.
* Allow variable symbol size: Check to have symbol size vary with y value.
* Symbol size: Enter a number in typographic points to specify size of symbols.
* Min symbol size and Max symbol size: If you check Allow variable symbol size, specify the range of symbol sizes (in typographic points) for the smallest and largest.

Bar graph settings:
* Stacked bars: Check to show bars stacked one on top of the other over the Key index, instead of side by side. The values for each bar are cumulated over the Key index.
* Variable origin: Check if you want to set the origin (starting point) for each bar other than zero (the default). The graph will then display a Bar Origin menu to let you select the Bar origin.
* Bar overlap: With stacked bars, they overlap 100%. You can specify partial overlap between 0 and 100%.

=== Axis Ranges tab ===

[[Image:Axis_Ranges.png]]

This tab lets you control the appearance of each axis.
* '''Autoscale:''' Uncheck this box if you want to specify the range for the axis, instead of letting Analytica select the range automatically to include all values.
* '''Max and Min:''' The maximum and minimum values of the range to use when you have unchecked Autoscale.
* '''Include zero''': Check if you want to include the origin (zero) in the range.
* '''Approx. # ticks''': Specify the number of tick marks displayed along the axis. Analytica may not match the number exactly, in the interests of clarity.
* '''Reverse order''': Check if you want to show the values ordered from large to small instead of the default small to large.
* '''Categorical''': Treat this axis as categorical. Usually, Analytica figures out the quantity is categorical without help. Occasionally, if the values are numerical, you may want to control it yourself.
* '''Log scale''': Check if you want to display this on a log scale. This is useful for numbers that vary by several orders of magnitude. It uses a "double log" scale with zero if the values include negative and positive numbers.

'''Set default''': If you have changed settings for an axis that is an index (not the main variable), clicking this button will apply these settings for that index for all graphs that use that index. For example, if the scale is the Index Time, you can use this to change the Time scale (e.g. start and end time) for all graphs that display a value over Time.

=== Style tab ===

[[Image:Style.png]]

Grid: Select the radio button to control the display of the grid over the graphing area. You can also select the color.

Frame: Select the radio button to control the display of the lines framing the graphing area. You can also select the color for the frame. It usually looks best to have the Frame the same color as the Grid, or a darker shade of the same color.

Tick marks: The top radio buttons control where to show tick marks. The lower ones control how they are displayed.

Display key: Select radio button to control where to display the Key on the graph. Select the Border check box to display an outline rectangle around the key.

=== Fonts tab ===

[[Image:Fonts.png]]

[[Image:Font_image3.png]]

This tab lets you select the font, size, style, and color for four types of text on the graph, the Axis titles, the Axis labels (i.e. text labeling along the graph), the Key title and the key labels (i.e. text appearing in the key). Graphic designers recommend that you use the same font and color for all text on a graph, and vary only the size.

'''Axis Label Rotation:''' Specify degrees of rotation from -90 to 90 to rotate the labels for either axis. For example, for a bar graph with long labels along its horizontal axis, you can rotate them so that they fit without getting truncated.

=== Background tab ===

[[Image:Background.png]]

This tab lets you control the color or pattern that appears on the graph background. The main area covers the entire graph window (exclusive of slicer and index selections). The plot area is the rectangle showing the graph values. If you specify a fill color or pattern for the Main area, the main fill will also be the background for the Plot area and Key if they are set to "None".

Fill: The options are
* None: No fill. Default to white background.
* Solid: Use a solid fill with the Color selected.
* Gradient: Use a gradient of color, going from Color 1 to Color 2, in the direction you select from Gradient style.
* Hatch: Use a hatched fill using the selected Hatch Style with Color 1 and Color 2.

Good graphic designers recommend avoiding Hatch backgrounds, and using solid or gradient backgrounds with mild colors, if at all. The data lines, points, and text should not be overwhelmed by the background.

=== Preview tab ===

[[Image:Preview.png]]

The '''Preview''' tab shows the graph with the current settings so that you can easily see the effects of settings for this graph.

== [[XY comparison]] or XY Coordinate sources ==

[[XY comparison]] lets you plot one variable on vertical (Y) axis against the other on the horizontal (X) axis.

== [[Graph Setting Associations]] ==
Graph settings now have a richer set of [[Graph Setting Associations|associations]], designed to allow intelligent transitions when multi-dimensional results are pivoted and when the graphing mode (Mid, Bands, PDF, etc) is changed.

For example, axis range settings are associated with a particular index, so if the graph is pivoted, the setting follows the pivot and isn't suddenly applied to a horizontal axis where it no longer makes sense.

Line style settings are associated with a combination of graph view mode and categorical / continuous distinction. Thus, it is possible to have a probability mass plot draw as a bar graph, a sample as a scatter plot, and bands as a line plot, all with a single graph setting.

Associations also impact how how settings transfer when "Set Default" is used.

== [[Graphing Dimensions and Roles]] ==

A general and flexible system of graphing dimensions and graphing roles allows a very rich space of chart types to be created using only a few elementary building blocks. The mechanism also allows many dimensions to be reflected on a single graph.

A computed value or an index can serve as a graphing dimension. Graphing dimensions are then assigned to graphing roles, and the user can easily pivot the graphing roles to alter the assignment of dimensions to roles. By assigning graphing dimensions to roles, a user can view many dimensions at once, and compare multiple values on the same graph.

Selectable graphing roles include X-axis, Y-axis, combined Color/Symbol Key, and [[Bar Origin]], and may soon include Symbol Size and separate Color and Symbol Keys.

While Analytica 3.x allowed a single external X-value to be plotted against a result, the new system allows any number of external variables to be included and compared in the same graph.

== [[Selecting Data for Graphing]] ==

The structure of data used to create a plot is now much more flexible. A [[Coordinate Indexes|Coordinate Index]] can be used to plot data that is organized in columns, without having to break the data into multiple variables. Multiple external variables can be merge into the plot as graphing dimensions. In a [[Scatter Plots|Scatter Plot]], you can pivot both X and Y axes to explore multi-dimensional data from many "angles".

== [[Categorical and Continuous Plots]] ==

Analytica 4.0 gives much more attention and consistency to the treatment of Categorical, Continuous and Discrete results.

The Discrete vs. Continuous distinction is determined by [[the Domain Attribute]], and determines whether probability plots are density and cumulative density plots (continuous) or probability mass and cumulative probability (discrete) plots.

The [[Categorical vs. Continuous distinction]] determines how a graphing axis is laid out. Continuous dimensions require numeric values. The determination of whether a graphing dimension is categorical or continuous is partially determined by the domain attribute, but the values actually occuring in the dimension, by the chart type (bar or non-bar chart), and by the Categorical checbox in the axis range setting.

Analytica maintains separate line-style settings for categorical and continuous plots. (The running axis of a plot, usually x-axis, determines whether the plot is continuous or categorical). Thus, by pivoting a continuous dimension to the x-axis to replace what was a categorical dimension, a graph may change from a bar graph to line graph, for example.

== Richer [[Plot Style Examples|Plots Types]] ==

Using combinations of the above named features, a wide variety of chart styles can be created, which were not possible previously. These include:
;; Bar Chart variations
* Stacked Bars
* Segmented Bar charts
* Horizontal [[Tornado Plots]]
* [[Gantt Charts]]
* [[Candle bars]] (High-close-open-low stock charts)
;; Scatter plot variations
* Multi-D depiction of data in columns
* [[Bubble plots]]
;; Line plot variations
* Standard (continuous) line plots
* XY parametric plots
* [[Log Plots]] (axis log scaling)
* Sideways plots

== [[Graph Appearance Settings]] ==

Analytica 4.0 exposes many appearance settings to user control, allowing the production of "board room quality" charts. These include:

* Control of background fills/patterns (solid, gradient, hatch).
* Full control of fonts (color, face, size, bold, etc)
* Location of Key.
* Grid and tic styles
* Axis label rotations
* Three-D effects (solid bars, ribbons)
* Filled line graphs (w/ transparency control)

== [[Graph Style Templates]] ==

[[Graph Style Templates|Graph style templates]] let you save the settings for a graph into a named template, so you can reuse it for other graphs. For example, can create templates for your organization to provide a consistent style. Future beta releases of 4.0 will include templates offering a range of selected styles.

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