print out of its regression results: Beer = Bo + BIEDUC + BAAGE + BAAGE + BAGENDER + BRACE+ BIGENDER'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 ??? 0.4684 Standard Error ??? Observations 40 ANOVA df MS 319.3 Regression Residual Total 64.8 8.43 ??? ??? ??? ??? ??? 597.5 Coefficients Standard Error 107.397 Intercept EDUC -150.254 -16.7755 8.4579 AGE 75.45905 37.3261 AGE -1.72456 238.9424 0.5397 GEN 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 (B6) 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.
print out of its regression results: Beer = Bo + BIEDUC + BAAGE + BAAGE + BAGENDER + BRACE+ BIGENDER'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 ??? 0.4684 Standard Error ??? Observations 40 ANOVA df MS 319.3 Regression Residual Total 64.8 8.43 ??? ??? ??? ??? ??? 597.5 Coefficients Standard Error 107.397 Intercept EDUC -150.254 -16.7755 8.4579 AGE 75.45905 37.3261 AGE -1.72456 238.9424 0.5397 GEN 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 (B6) 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.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Concept explainers
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
Question
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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