Example Models and Libraries

Analytica Tutorial >

This chapter describes the example models and libraries that are provided with Analytica.

Congratulations on completing the Analytica Tutorial. You are now ready to begin creating your own models.

For more detailed information on Analytica, see the Analytica User Guide. It is a reference on all aspects of Analytica, including descriptions of all available functions.

Within the Analytica folder are folders titled Example Models and Libraries, which contain a variety of Analytica models, including the examples illustrated in the Analytica User Guide. These resources are useful to include when building your own models. Many of the example models were created by users just like you. These models contain a wealth of ideas on using Analytica for practical applications. You should investigate these examples to see some of the different ways in which models can be constructed.

If you create models that you feel would be helpful or interesting to others, please send them to us for inclusion in a future Example Models folder; see the end of this chapter.

To get to the Example Models folder, select File → Open, then press the Example Models button at the top left:

The Example Models folder is subdivided into these folders:

• Business Examples
• Data Analysis
• Decision Analysis
• Dynamic Models
• Engineering
• Function Examples
• Optimizer Examples
• Risk Analysis
• Tutorial Examples
• User Guide Examples

Business Examples

Bond Model: This model takes typical bond purchase inputs (purchase price, par value, interest rate, and life to maturity) and calculates bond cash flows, current yield, and yield to maturity.

Breakeven Analysis: This model is an example of a breakeven analysis of a set of revenue levels, when the fixed expenses are set at one amount and the variable expenses are a constant fraction of revenue.

Expected R&D Project Value: This model evaluates and compares the expected commercialization value of multiple proposed R&D projects.

Financial Statement Templates: This model contains a complete set of standard financial statements: a profit and loss statement, balance sheet, and cash flow statement. It provides a step-by-step guide to using these templates to generate your own financial statements. You can enter values into the existing template and modify the variable definitions to reflect your own accounting standards.

Market Model: This model explores a market for a new product, and the pricing and advertising budget decisions involved. This example also shows the use of “forms” for receiving input and presenting output for users of the model.

Plan_Schedule_ Control: This model takes input data for activity paths required to complete a project, and calculates vari- ous outputs describing the critical path, timing, and costs for project completion.

Project Portfolio Planner: This model evaluates and prioritizes a portfolio of projects based on either the estimated net present value or a multi-attribute score, based on strategy fit, staff development, the generation of public goodwill, and estimated net revenue.

Sales Effectiveness: This model evaluates the effects of unit price on salesmen head count and production capacity. The model contains an example of taking user estimates of uncertainty in a standard high- medium-low form, and transforming those inputs into a continuous distribution for propagation through the model. Derived from Principles of Systems by Jay W. Forrester, 1968, ISBN 0-915299-87-9.

Subscriber Pricing: This model determines the amount of revenue needed on a monthly basis from each subscriber of a service to just meet the weighted average cost of capital of the firm from the service release date to the end of the study horizon. In other words, it calculates the monthly unit revenue rate required from each subscriber of a service to give a return on investment at the end of the study horizon that is equal to the weighted average cost of capital of the firm.

Data Analysis

Bootstrapping: Bootstrapping is a technique for estimating the confidence in a statistical estimator. This model demonstrates how a distribution for an estimated statistic can be obtained by resampling repeat- edly.

Kmeans Clustering: This model shows an example of scatter plots in Analytica. A K-means clustering algorithm (where K is the number of clusters) is applied to some random data to partition points into groups (clusters) of similar points. This model also demonstrates the Iterate function.

Moving Average Example: This is a simple model that shows how to compute the moving average for a data stream. It defines a Moving Average function you can use.

Multidimensional Scaling: This model performs multidimensional scaling. It takes as input N, which is the dimensionality of the problem, and Distances, which is an NxN symmetric matrix of distances (or dissimilarities). It calculates and outputs a two-dimensional set of N points XY (or separately as Xcoord and Ycoord) that best approximates the spatial layout of points that could generate the input distances. Reference: Multivariate Analysis by K.V. Mardia, J.T. Kent, and J.M. Bibby, Academic Press, Lon- don, 1979, Section 14.2.2, page 400. Model supplied by Michael L. Thompson.

Principle Components : Principal components analysis (PCA) is a technique used to reduce multidimensional data sets to lower dimensions for analysis. PCA involves computing the eigenvalue decomposition or singular value decomposition of a data set. This model shows how to find the principle components in a uses an eigenvalue decomposition to compute the principle components of the covariance matrix of historical stock prices.

Regression Examples: This model demonstrates the use of generalized linear regression by best fit curves of various function forms to a set of (x,y) points. It includes:

• Linear regression
• Quadratic regression
• Polynomial regression
• Discrete Fourier series
• Regression with redundant basis
• Regression using a large arbitrary collection of terms (useful in the situation where you do not have any reason to prefer one functional form over another)
• An auto-regressive series

See Also

Sharing Models with Others <-

Example Models and Libraries

-> Glossary