The Advanced Statistics Module of StatPac gives you more power and control than any other statistical analysis software on the market today. It provides state-of-the-art sophistication and capabilities. Whether you're an experienced statistician or a beginner, StatPac is the solution.

The Advanced Statistics Module contains:

Analysis of Variance

Analysis of variance is used to compare variances from more than two groups. The ANOVA program in StatPac offers impressive versatility. There are eleven ANOVA models to handle practically any kind of experiment you design (e.g., repeated-measures, split-plot, randomized block, complete block, nested and Latin square models). The output selection includes an analysis of means, classical ANOVA table and post-hoc least significant difference t-tests. The Kruskal-Wallis test is available for non-parametric analysis.

Linear and Non-Linear Regression

Regression analysis in StatPac is sophisticated and remarkably easy to use. An automatic curve-fitting option makes non-linear regression an effortless procedure. Another special robust technique is available to reduce distortion caused by extreme data points. StatPac can produce a rich variety of tables for regression, autocorrelation and residual analysis.

Stepwise Multiple Regression

The multiple regression program in StatPac has been given top ratings by reviewers for its speed, accuracy and completeness. The stepwise method is forward inclusion with backward elimination. Output includes all the regression statistics and matrices. You can even switch to interactive prediction to try the regression equation on new data, or save the model for future use.

Probit and Logistic Regression

Probit and logistic regression are similar to multiple regression except they are used when the dependent variable is dichotomous (can take on only two values). A banker might use these methods to determine the probability that a person will pay back a loan, or a medical researcher might use them to determine the probability that an experimental drug would be successful. Both techniques use accurate non-linear algorithms.

Canonical Correlation

Canonical correlation is a powerful multivariate technique to study the intercorrelational structure between two sets of variables. One set is usually regarded as dependent and the other as independent. For example, a set of "buying behavior" variables might be considered dependent, while a set of "personality characteristics" variables could be thought of as independent. Canonical correlation provides a convenient way to understand the complex relationships that might exist between the variables.

Principal Components Analysis

Principal components analysis is often used in conjunction with multiple regression in an attempt to reduce the number of predictor variables. This is important because it helps to reduce future data collection costs. Usually, most of the variation in a large group of variables can be captured with only a few principal components. StatPac also contains a complete selection of collinearity diagnostics that measure relationships between predictor variables and how they affect the stability and variance of the regression coefficients.

Factor Analysis

Factor analysis is used to identify and group variables by their common dimensions. It is often used with newly designed questionnaires to examine the cohesiveness of variables. The factor analysis program in StatPac picks up where others leave off. There are two methods of extraction, three types of rotation and several different ways to control the exit criteria. Every parameter is adjustable to give you complete control of the analysis.

Cluster Analysis

Cluster analysis is used to identify and group respondents that are similar. It is frequently used in marketing research to identify and target segments of the population for an advertising campaign. StatPac contains six outstanding clustering techniques. A hierarchical tree diagram provides a visual summary that makes it easy to identify the clusters. The cluster membership can be saved for inclusion in additional analyses.

Stepwise Discriminant Function Analysis

Discriminant function analysis is used to predict a categorical variable. Marketing researchers often use this procedure to understand the factors that determine why consumers choose one brand over another. StatPac offers a complete output selection including canonical variable analysis.

Perceptual Mapping

Multiple correspondence analysis (perceptual mapping) is a very powerful and easy to use technique for studying the relationships between two or more categorical variables. It is frequently used in marketing research to understand consumer perceptions of a product and to determine the effectiveness of an advertising campaign designed to modify their perceptions.


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