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J. Scott Long

The following product is developed by J. Scott Long, a third party developer, for use with GAUSS. Technical support is provided directly through the developer.

Markov 2.5 - A Statistical Environment for GAUSS

The following product is developed by J. Scott Long, a third party developer, for use with GAUSS. Technical support is provided directly through the developer.

Markov is a statistical environment that makes it easy to do simple things without restricting your use of the full power of GAUSS. Experienced GAUSS programmers can work more efficiently. New users will find that Markov makes GAUSS easier and more fun to learn. A user writes: "Markov makes life a lot easier for the GAUSS user, even for people who have learned how to do things the harder way in GAUSS." Markov is ideal for classroom use where you want students to be able to get output quickly and easily, but also want them to have access to a matrix language.

How Markov Works

Markov has a simple command structure. For example, to run a multiple regression with collinearity diagnostics you would use the commands:

SET DSN MYDATA;
SET LHS YVAR;
SET RHS XVAR1 XVAR2;
OPT COLLIN ON;
GO REG;

After running an analysis the results are printed and returned to global variables that can be used in your own GAUSS programs or can be further analyzed by Markov. For example, to test the hypothesis that the coefficients for

XVAR1 and XVAR2 are both zero, you would simply enter the command:

TEST DELETE XVAR1 XVAR2

Complicated graphs can be computed just as easily, with impressive results.

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Features

Markov includes extensive on-line documentation and source code so that you can customize Markov.

Statistical procedures include descriptive statistics, cross-tabulation, log-linear models, multinomial logit, probit, Poisson regression, ordered logit and probit, tobit, switching regressions, simultaneous equation models, and regression analysis with collinearity diagnostics, residual analysis and powerful statistical tests such as White's information matrix test.

Statistical graphics include box and whisker plots, scatterplot matrices, quantile-quantile plots, and many more. Each plot is specified with a simple command language.

A Shell for Monte Carlo simulation is included. This allows GAUSS programmers to do complex simulations very simply. You program the procedure you want to simulate, and Markov keeps track of the results of the simulation and allows simulations to be suspended and resumed later.

Full data management capabilities including sorting, merging and updating. Variables in memory can be saved to GAUSS data files as simply as entering the command:

WRITE X Y Z TO NEWFILE

Variables from disk files can be brought into memory simply:

READ WEIGHT LENGTH FROM AUTO

Includes tobit and endogenous switching regression models

Enhancements to GAUSS's DATALOOP procedure make it easy to construct the types of variables most commonly used instatistical analysis.

Three commands aid in the interpretation of models for categorical and limited dependent variables. These are PREDICT which computes the predicted value of the outcome; PARTIAL which computes the partial derivative of the outcome; and DISCRETE which computes discrete changes in the outcome for given changes in the independent variables.