Excel to Analytica Mappings/Statistical Functions

AVEDEV(x1,x2,...)

Analytica equivalents:

Mean(x-Mean(x))
Mean(x-Mean(x,I),I)

AVERAGE(x1,x2,...), AVERAGEA(x1,x2,...)

Analytica equivalent:

Average(x,I)

BETADIST(x,alpha,beta,A,B)

Analytica equivalents:

BetaI(x,alpha,beta)
BetaI((x-A)/(B-A),alpha,beta) 

To define a variable as a beta probability distribution, use:

Beta(alpha,beta,A,B)

BETAINV(p,alpha,beta,A,B)

Analytica equivalents:

BetaIInv(p,alpha,beta)
BetaIInv(p,alpha,beta) * (B-A) + A

To define a variable as a beta probability distribution, use:

Beta(alpha,beta,A,B)

BINOMDIST(x,n,p)

Analytica equivalent:

Prob_Binomial(x,n,p)

To use this function, add the Distribution Densities Library to your model.

To define a variable as binomially distributed, use:

Binomial(n,p)

For the cumulative binomial probability, BINOMDIST(x,n,p,TRUE), the Analytica equivalent is:

Probability(Binomial(n,p)<=x)

Note, however, that this is evaluated using sampling, so for small sample sizes there could be some sampling error in the result.

CHIDIST(x,dof)

Analytica equivalent:

Dens_ChiSquared(x,dof)

CHIINV(p,dof)

Analytica equivalent:

2 * GammaIInv(p,dof/2)

CHITEST(actual,expected)

For the 1-D case, where actual and expected are each 1-D, in Analytica these will have a common index, I, and the Analytica equivalent is:

Var n := Sum(1,I);
Var chi2 := Sum( (actual-expected)^2 / expected, I );
1-GammaI(chi2/2,(n-1)/2)

For the 2-D contingency table analysis, with indexes I and J, the equivalent is:

Var n := Sum(1,I,J);
Var chi2 := Sum( (actual-expected)^2 / expected, I, J );
1-GammaI(chi2/2,(n-1)/2)

CONFIDENCE(alpha,sd,n)

Analytica equivalent:

CumNormalInv( 0.5 + (1-alpha)/2, 0, sd ) / sqrt(n)

CORREL(x,y)

Analytica equivalents:

Correlation(x,y)
Correlation(x,y,I)

Use the first form, without the index, when measuring correlation across the Run index (e.g., the Monte Carlo uncertainty dimension).

COUNT(value1,value2,...), COUNTA(value1,value2,...)

Analytica equivalents, e.g.,:

Sum( IsNumber(x), I )
Sum( x<>Null, I )

COUNTBLANK

The notion of a blank Excel cell does not directly translate, but in Analytica we would generally consider a cell of an array to be blank when its value is null. Thus, the Analytica equivalent would be:

Sum( x=null, I )

COUNTIF(range,criteria)

The Analytica equivalent consists of expressing criteria as a boolean expression and then using:

Sum(criteria,I)

For example, to count the number of values greater than 55, use:

Sum(range>55,I)

COVAR(array1,array2)

Analytica equivalent:

CoVariance(array1,array2,I)

CRITBINOM

DEVSQ(number1,number2,...)

Analytica equivalents:

Sum( (X-Mean(X,I))^2, I )
Variance(X,I) * (Sum(1,I)-1)

where the numbers are in array X indexed by I.

EXPONDIST(x,lambda,cumulative)

If cumulative is false, the Analytica equivalent is:

Dens_Exponential(x,1/lambda)

When cumulative is true, the Analytica equivalent is:

CumExponential(x,1/lambda)

FDIST(x,dof1,dof2)

The Analytica equivalent is:

1-CumFDist(x,dof1,dof2)

FINV(p,dof1,dof2)

The Analytica equivalent is:

CumFDistInv(1-p,dof1,dof2)

FISHER

FISHERINV

FORECAST(x,known_y,known_x)

Analytica equivalent (Index I an the index in common with known_x and known_y):

Index K := ['b','m'];
Var B := Array(K,[1,known_x]);
Var Bx := Array(K,[1,x]);
Sum(Regression(known_y,B,I,K) * Bx, K )

FREQUENCY(data_array,bins_array)

Analytica equivalentd:

Frequency(data_array,bins_array)
Frequency(data_array,bins_array,I)

The second form is used when data_array is indexed by I. The first form takes the frequency of the uncertain sample, i.e., along the Run index.

FTEST(array1,array2)

The following User-Defined Function provides an Analytica equivalent:

Function FTest( A1 : ContextSamp[I], A2 : ContextSamp[J] ; I,J : Index = Run )
Definition:
   Var v1 := Variance(A1,I);
   Var v2 := Variance(A2,J);
   Var n1 := Sum(1,I);
   Var n2 := Sum(1,J);
   Var F := v2/v1;
   1 - CumFDist(F,n1-1,n2-1)

Add the above UDF to your model, then you can just use:

FTest(array1,array2,I,J)

where I and J are the indexes of array1 and array2 respectively.

GAMMADIST(x,a,b)

Analytica equivalent of GAMMADIST(x,a,b):

Dens_Gamma(x,a,b)

Analytica equivalent of GAMMADIST(x,a,b,TRUE):

GammaI(x,a,b)

To define a variable as uncertain with a gamma probability distribution, use:

Gamma(a,b)

GAMMAINV(p,a,b)

Analytica equivalent:

GammaIInv(p,a,b)

GAMMALN(x)

Analytica equivalent:

LGamma(x)

GEOMEAN

GROWTH

HARMEAN

HYPGEOMDIST(sample_s,number_sample,population_s,number_pop)

The Analytica equivalent is:

Prob_HyperGeometric(sample_s,number_sample,population,number_pop)

INTERCEPT(known_y,known_x)

Assume known_y and known_x share index I. The Analytica equivalent is:

Index K := ['b','m'];
Regression( known_y, Array(K,[1,known_x]), I, K ) [ K='b' ]

KURT(number1,number2,...)

Analytica equivalents:

Kurtosis(X)
Kurtosis(X,I)

LARGE(array,k)

Assume array is indexed by I. One possible Analytica equivalent is:

Var n := Sum(1,I);
X[I=ArgMax(Rank(array,I)=n+1-k,I)]

LINEST

LOGEST

LOGINV(p,lm,lsd)

The Analytica equivalent is:

CumLogNormalInv(p,Exp(lm),Exp(lsd))

LOGNORMDIST(x,lm,ls)

The Analytica equivalent is:

CumLogNormal(x,Exp(gm),Exp(gs))

MAX(number1,number2,...)

When MAX is applied in Excel to individual numbers, the Analytica equivalent is:

Max( [number1,number2,...] )

Take note of the square brackets.

When MAX is used in Excel by providing it with a range of cells, the Analytica equivalent is

Max(A,I)

where A is the array of values (analogous to Excel's range) and I is the index to take the max over. In the two-D case, where you want the Max over a 2-D region, this becomes:

Max(A,I,J)

MAXA

MEDIAN(number1,number2,...)

When MEDIAN is applied in Excel to individual numbers, the Analytica equivalent is:

Index x := [number1,number2,...];
GetFract( x,0.5,x )

In the more usual case, when MEDIAN is used in Excel by providing it with a range of cells, the Analytica equivalent is

GetFract(A,0.5,I)

where A is the array of values (analogous to Excel's range) and I is the index to take the median over.

MIN(number1,number2,...)

When MIN is applied in Excel to individual numbers, the Analytica equivalent is:

Min( [number1,number2,...] )

Take note of the square brackets.

When MIN is used in Excel by providing it with a range of cells, the Analytica equivalent is

Min(A,I)

where A is the array of values (analogous to Excel's range) and I is the index to take the minimum over. In the two-D case, where you want the minimum over a 2-D region, this becomes:

Min(A,I,J)

MINA

MODE(range)

Assuming the values in Analytica are in an array A indexed by I, the equivalent is:

Index V := Unique(Va1,Va1);
ArgMax(Frequency( Va1, V, Va1 ),V)

NEGBINOMDIST

NORMDIST(x,mean,standard_dev,cumulative)

If Cumulative=True, the Analytica equivalent is:

CumNormal(x,mean,standard_dev)

If Cumulative=False, the Analytica equivalent is:

Dens_Normal(x,mean,standard_dev)

Note that to use Dens_Normal, you must include the distribution densities library in your model.

NORMINV(p,mean,standard_dev)

Analytica equivalent:

CumNormalInv(p,mean,standard_dev)

NORMSDIST(z)

Analytica equivalent:

CumNormal(z)

NORMSINV(p)

Analytica equivalent:

 CumNormalInv(p)

PEARSON(array1,array2)

Analytica equivalent:

Correlation(array1,array2,I)

where the data points in array1 and array2 both are indexed by I.

PERCENTILE(array,p)

Assume that the array of values is indexed by I. Then the Analytica equivalent is:

GetFract(array,p,I)

PERCENTRANK

PERMUT(n,k)

Analytica equivalent:

Permutations(n,k)

POISSON(x,mean,cumulative)

When cumulative is false, the Analytica equivalent is

Prob_Poisson(x,mean)

When cumulative is true, the Analytica equivalent is

CumPoisson(x,mean)

In Analytica models, you'll often use the Poisson distribution function directly, rather than evaluating the probability or cumulative probability at a given point.

PROB(x_range,prob_range,lower_limit,upper_limit)

In Analytica, x_range and prob_range will share an index, call it I. When upper_limit is not specified, then the Analytica equivalent is:

Sum( prob_range * (lower_limit <= x_range ), I )

When upper_limit is specified, this becomes

Sum( prob_range * (lower_limit <= x_range and x_range <= upper_limit), I )

QUARTILE(array,quart)

Analytica equivalent, where array is assumed indexed by I:

GetFract(array,quart/4,I)

RANK(number,range,order)

Assume in Analytica that the array identified by range is indexed by I. Then when order is non-zero, the Analytica equivalent is:

Rank(range,I,type:-1)[I=number]

When order is omitted or zero in Excel, the Analytica equivalent is:

Rank(-range,I,type:1)[I=number]

RSQ(known_y,known_x)

Analytica equivalent:

Correlation(known_y,known_x,I)^2

SKEW(number1,number2,...)

Analytica equivalent (assume the numbers are in array X, indexed by I):

Skewness(X,I)

When X is an uncertain distribution, indexed by Run, the equivalent is:

Skewness(X)

SLOPE(known_y,known_x)

Assume known_y and known_x share index I. The Analytica equivalent is:

Index K := ['b','m'];
Regression( known_y, Array(K,[1,known_x]), I, K ) [ K='m' ]

SMALL(array,k)

Assume array is indexed by I. One possible Analytica equivalent is:

X[I=ArgMax(Rank(array,I)=k,I)]

STANDARDIZE(x,m,sd)

Analytica equivalent:

(x-m)/sd

STDEV(number1,number2,...)

Analytica equivalent (assume that the numbers are in array X indexed by I):

SDeviation(X,I)

STDEVP

Analytica equivalent (assume that the numbers are in array X indexed by I):

SDeviation(X,I) * (Sum(1,I)-1)/Sum(1,I)

STEYX

TDIST(x,dof,tails)

The Analytica equivalent is:

tails * (1-CumStudentT(x,dof))

TINV(p,dof)

Analytica equivalent:

CumStudentTInv( 1-p/2, dof )

Excel has a strange notion of a 2-tailed Student-T distribution, which basically multiplies the probability by 2. See its TDIST function. TINV seems to do the inverse of this 2-tailed case, which leads to this strange equivalence.

TREND(known_y,known_x,new_x,const)

Assume that the data in known_y and known_x are indexed by index I, and the points in new_x are indexed by J. Also assume that const is 1 (true) for a y=m*x+b curve, and 0 (false) for a y=m*b fit. Then the Analytica equivalent is:

Index K := ['b','m'];
Sum( Regression(known_y,Array(K,[const,known_x]),I,K) * Array(K,[const,new_x]), K )

The result is the set of predicted new_y values, indexed by J.

TRIMMEAN(array,percent)

Assume the array is indexed by I. The Analytica equivalent is

Var n := Sum(1,I);
Mean( A[I=SortIndex(A,I)], I, w:@I > percent*n/2 and @I < n+1-(percent*n/2) )

TTEST

VAR(number1,number2,...)

Analytica equivalent (assume numbers are in array X indexed by I):

Variance(X,I)

VARP

Analytica equivalent (assume numbers are in array X indexed by I):

Variance(X,I) * (Sum(1,I)-1)/Sum(1,I)

WEIBULL(x,alpha,beta,cumulative)

ZTEST