Study with the several resources on Docsity
Earn points by helping other students or get them with a premium plan
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
This document showcases a sas program that performs regression analysis on a dataset with two variables, time and humidity, to predict wtloss. The program creates two regression models, one with interaction terms and the other without. The analysis includes descriptive statistics, anova, and parameter estimates.
Typology: Study notes
1 / 6
This page cannot be seen from the preview
Don't miss anything!
evap; input
wtloss time humid; time=time-
humid=humid-
humidsq=humidhumid;timehumid=timehumid;timehumidsq=time*humidsq;datalines;4.3 4 .25.6 5 .26.4 6 .28.0 7 .24.0 4 .35.2 5 .36.6 6 .37.5 7 .33.2 4 .44.0 5 .45.6 6 .46.4 7 .4; run
proc
reg
model
wtloss= time humid humidsq timehumid timehumidsq/vif; model
wtloss= time humid humidsq; title
'Regressiom Models with and without Interaction Terms'; run
SAS Log 3
data
evap;
4
input
wtloss
time
humid;
time=time-5.5; 6
humid=humid-.3; 7
humidsq=humid*humid; 8
timehumid=time*humid; 9
timehumidsq=time*humidsq; 10
datalines; NOTE:
The
data
set
WORK.EVAP has 12 observations
and 6 variables.
statement
used (Total
process time):
real
time
0.09 seconds
cpu
time
0.01 seconds
run; 2526
proc
reg;
27
model
wtloss=
time
humid
humidsq
timehumid
timehumidsq/vif;
model
wtloss=
time
humid
humidsq;
title
'Regressiom
Models with and
without Interaction Terms';
run;
Parameter
Estimates
Parameter
Standard
Variance
Variable
Estimate
Error
t^ Value
Pr^
^ |t|
Inflation
Intercept
time
humid
humidsq
timehumid
timehumidsq
Regressiom
Models
with
and
without
Interaction
Terms
The
Procedure Model:
Dependent
Variable:
wtloss
Number
of
Observations
Read
Number
of
Observations
Used
Analysis
of VarianceSum^
of^
Mean
Source
Squares
Square
F^ Value
Pr^
Model
Error
Corrected
Total
Root
R-Square
Dependent
Mean
Adj
R-Sq
Coeff
Var
