Simple linear regression
Regression involves the calculation of terms in an equation to predict a response Y from one
or more predictor variables X1, X2, etc. In linear regression, the equation has the form Y = bo
+ b1X1 + b2X2 + ... + bkXk + e, where e represents a random error term which is assumed to
be normally distributed with mean 0 and constant variance which does not depend on the
value of any other observation.
PROC REG is the main SAS procedure for linear regression, but it can also be done with PROC
GLM. PROC NLIN is used for nonlinear regression. SAS has other regression routines for
special types of data.
In the cheese study, we may want to develop an equation to predict taste scores from the
various chemical constituents. For example, we may want to know how hydrogen sulfide
affects taste. The plot above showed that these two variables are positively associated with
each other, and the linear correlation between the two variables was large. The following SAS
code fits the regression line.
Simple linear regression
Regression involves the calculation of terms in an equation to predict a response Y from one
or more predictor variables X1, X2, etc. In linear regression, the equation has the form Y = bo
+ b1X1 + b2X2 + ... + bkXk + e, where e represents a random error term which is assumed to
be normally distributed with mean 0 and constant variance which does not depend on the
value of any other observation.
PROC REG is the main SAS procedure for linear regression, but it can also be done with PROC
GLM. PROC NLIN is used for nonlinear regression. SAS has other regression routines for
special types of data.
In the cheese study, we may want to develop an equation to predict taste scores from the
various chemical constituents. For example, we may want to know how hydrogen sulfide
affects taste. The plot above showed that these two variables are positively associated with
each other, and the linear correlation between the two variables was large. The following SAS
code fits the regression line.
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