PACKAGE |STAT Data Manipulation and Analysis, by Gary Perlman
NAME regress - multivariate linear regression and correlation
SYNOPSIS regress [-ceprs] [column names]
WEB FORM regress can be run from an online web form.
DESCRIPTION regress performs a general linear correlation and regression analysis for up to 20 variables. Input is a series of lines, each containing an equal number of numerical fields. Names for these fields can be supplied, but if none are given, REG, A, B, C, etc. are used.

For regression analysis, the first column is predicted with all the others (see dm or colex to reorder columns).

OPTIONS
-c
Print the covariance matrix.
-e
Save the regression equation in the file regress.eqn. This file is designed for use with the data manipulator dm. Suppose the input to regress is in regress.in. Then, 
regress -e < regress.in
can be followed by 
dm Eregress.eqn < regress.in | pair -p
to plot the obtained against the predicted values. The residuals can be obtained with an extra pass through dm: 
dm  Eregress.eqn  <  regress.in  |  dm  x2  x1-x2  |  pair  -p
-p
Do a partial correlation analysis to determine the contribution of individual predictors after the others have been included. regress reports, for each predictor, the regression weight (b) and the standardized regression weight (beta). The Rsq value is the squared multiple correlation of the predictor with all other predictors; if there is only one predictor, this will be zero, and if there is only one other, both Rsq's will be identical. Also reported is the standard error of the regression weight (b). The significance test answers the question: ``After all the other variables have been taken into account, does this variable significantly improve prediction?''
-r
Do no regression analysis. Only print the correlation matrix and summary statistics.
-s
Print the matrix of raw sums of squares and cross products.
The following standard help options are supported. The program exits after displaying the help.
-L
Display limits
-O
Display options and values
-V
Display version number and date
DIAGNOSTICS regress will complain about a singular correlation matrix if variables are perfectly correlated.
ALGORITHM Chapter 4 of Kerlinger and Pedhazur (1973) Multiple Regression in Behavioral Research. New York: Holt, Rinehart & Winston.
LIMITS Use the -L option to determine the program limits.
MISSING VALUES Cases with missing data values (NA) are counted but not included in the analysis.
SEE ALSO pair, for simpler paired data analysis.
rankrel, for non-parametric analysis of rank ordinal data.
BUGS The SEest is not calculated correctly, but it is not used in the calculation of F or p (reported 2020-05-18).
UPDATED Jan 27, 1987 - see BUGS