# "How to set up multiple regression analysis?"

Member Posts: 1 Contributor I
edited June 2019 in Help
Hello everyone.  I'm very new to RM, and got here when I was looking for tools to help me run statistical inference tests on some data.  I've searched through previous posts on this topic but haven't quite found an answer.

Assume I have one "dependent" variable -- some measurement of interest, such as "age when death occurs".  Call this variable Y.
Assume I have  3 "independent" variables, that could be measures of "unhealthy" factors: X1=number of pounds overweight; X2=cholesterol level; X3=triglyceride level.  Assume I have 100 (deceased) people with measurements on all this variables.

I want to run a multiple regression analysis http://en.wikipedia.org/wiki/Regression_analysis to see how well the combination of 3 independent variables "predicts" the dependent variable, and end up with a value for F (and its significance level), a value for R squared, and the regression coefficients.

I see the Linear Regression and Vector Linear Regression operators.  I also see the ANOVA operator, and the ANOVA Matrix and Grouped ANOVA operators.  (I don't know how to obtain a Performance Vector, which is required for the ANOVA operator.)  But I don't see how to get what I need using some combination of these and other operators.  Is RM not intended for this kind of inferential significance testing?

Different but related question:  Assume I have one dependent variable Y, 2 independent variables A & B, and a 2x2 experimental design: Can RM calculate the 3 ANOVA F values to indicate whether variable A, and/or B, and or their AxB interaction, have statistically significant effects on Y?

Thanks for any and all tips!

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