.TH REGRESS 1 "January 27, 1987" "\(co 1980 Gary Perlman" "|STAT" "UNIX User's Manual"
.SH NAME
regress \- multivariate linear regression and correlation
.SH SYNOPSIS
.B regress
[-ceprs] [column names]
.SH DESCRIPTION
.I 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.
.PP
For regression analysis,
the first column is predicted with all the others
(see
.I dm
or
.I colex
to reorder columns).
.SH OPTIONS
.de OP
.TP
.B -\\$1 \\$2
..
.OP c
Print the covariance matrix.
.OP e
Save the regression equation in the file
.I regress.eqn.
This file is designed for use with the data manipulator
.I dm.
Suppose the input to
.I regress
is in
.I regress.in.
Then,
.ce
regress -e < regress.in
can be followed by
.ce
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
.I dm:
.ce
dm  Eregress.eqn  <  regress.in  |  dm  x2  x1-x2  |  pair  -p
.OP p
Do a partial correlation analysis to determine the
contribution of individual predictors after the others have been included.
.I 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?''
.OP r
Do no regression analysis.
Only print the correlation matrix and summary statistics.
.OP s
Print the matrix of raw sums of squares and cross products.
.SH DIAGNOSTICS
.I regress
will complain about a singular correlation matrix
if variables are perfectly correlated.
.SH ALGORITHM
Chapter 4 of
Kerlinger and Pedhazur (1973)
.I "Multiple Regression in Behavioral Research."
New York: Holt, Rinehart & Winston.
.SH LIMITS
Use the -L option to determine the program limits.
.SH "MISSING VALUES
Cases with missing data values (NA) are counted but not included in the analysis.