# Statistical Advisor, Multiple Linear Relationships, Continuous Variables

Use GENERAL REGRESSION MODELS, GENERALIZED LINEAR MODELS, MULTIPLE REGRESSION, PARTIAL LEAST SQUARES, SURVIVAL ANALYSIS: These chapters discuss how to compute a standard multiple regression analysis, in which one dependent (criterion, endogenous) variable is related to multiple independent (predictor, exogenous) variables. A wide range of options is available for various specialized analyses (e.g., stepwise or best subset selection of independent variables by the program, extensive analyses/plotting of residuals, etc.).

GENERAL REGRESSION MODELS (GRM): This chapter discusses an implementation of the general linear model and how to compute a standard, stepwise, or best subset multiple regression analysis, in which one dependent (criterion, endogenous) variable is related to multiple independent (predictor, exogenous) variables. A wide range of options is available for various specialized analyses.

GENERALIZED LINEAR MODELS (GLZ): This chapter discusses an implementation of the generalized linear model and how to compute a standard, stepwise, or best subset multiple regression analysis, in which one (non-normally distributed) dependent (criterion, endogenous) variable is linearly or non-linearly (see Link Function) related to multiple independent (predictor, exogenous) variables.

MULTIPLE REGRESSION: This chapter discusses how to compute a standard multiple regression analysis, in which one dependent (criterion, endogenous) variable is related to multiple independent (predictor, exogenous) variables. A wide range of options is available for various specialized analyses (e.g., stepwise selection of independent variables by the program, extensive analyses/plotting of residuals, etc.).

PARTIAL LEAST SQUARES (PLS): This chapter discusses an implementation of partial least squares (PLS). PLS allow you to extract factors (components) from a data set that includes one or more predictor variables, and one or more dependent (response) variables. PLS is particularly suited for problems involving very many predictor variables (and possibly dependent variables), but relatively few cases.

SURVIVAL ANALYSIS: This chapter also discusses how to compute multiple linear (as well as nonlinear) correlations between continuous predictor variables, and censored or uncensored survival times. A popular nonparametric alternative to multiple linear correlation with survival/failure times, the so-called proportional hazard regression model, can also be computed.