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MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
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
Transcribed Image Text:IConsider the following multiple linear regression model and the Excel
print out of its regression results:
Beer = Bo + BIEDUC + B2AGE + BAGE? + BAGENDER + BERACE+ BEGENDER*RACE+ E, where
Beer is monthly beer consumption (ounces), EDUC is years of education. We have 2 qualitative variables:
gender and race. Gender takes 2 values, GEN=1 if the person is male and GEN=0 for females. The
variable race also takes 2 values, RACE=1 if the person is white and RACE=0 if the person is not white.
SUMMARY OUTPUT
Regression Statistics
R Square
Adjusted R Square
Standard Error
Observations
???
0.4684
???
40
ANOVA
df
MS
Regression
Residual
???
319.3
64.8
???
???
???
8.43
Total
???
597.5
Coefficients
Standard Error
Intercept
-150.254
107.397
EDUC
-16.7755
8.4579
75.45905
-1.72456
AGE
37.3261
AGE
0.5397
GEN
238.9424
81.6054
RACE
123.7404
103.1804
GEN. RACE
76.4308
51.0670
a. Calculate the missing numbers (???).
b.Interpret the parameter of RACE (123.74).
c. Is the parameter of RACE (Bs) significant? Use a = 0.05 and interpret your results.
d. Interpret the adjusted R?.
e.Conduct a test of Global Usefulness of the model (test of goodness of fit) using a=0.05. Interpret the
results of the test. Make sure to specify the null and the alternative hypotheses, test statistics and its
distribution, and the critical value, and interpret the results.
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