1. Estimate a simple linear regression model predicting Oil Usage from Degree Da
ID: 3310601 • Letter: 1
Question
1. Estimate a simple linear regression model predicting Oil Usage from Degree Days. Is this model statistically significant?
Yes or no
2. How much of the variation in Oil Usage is explained by the variation in Degree Days?
A. 0.29% B. 150.36% C. 29.4% D. 54.3%
3. Based on the simple regression model, for each one unit increase in Degree Days, Oil Usage increases by __________ units
A. 0.25 B. 58.80 C. 29.4% D. 3.98
4. The Y-intercept of the simple regression model predicting Oil Usage from Degree Days is significantly different from 0 in the population
Yes or No
5. Predict Oil Usage for all 40 customers using all remaining varaibles as predictors. This regression model predicts _______ more variation in Oil Usage than that predicted by the simple regression model
A. 0.49% B. 34% C. 65% D. 49%
6. Each individual predictor in the multiple regression model has a significant effect on Oil Usage.
True or False
7. The Y-intercept of the multiple regression model is significantly different from 0 in the population.
Yes or No
8. The predicted Oil Usage of a customer for whom Degree Days equal 600, Home Index value is 3, and number of people living in the house is 4, is approximately
A. 665 B. 180 C. 229 D. 365
9. The predicted Oil Usage of a customer for whom Degree Days equal 458, Home Index value is 1, and number of people living in the house is 1, is approximately:
A. 1 B. -7 C. 242 D. 0
10. The multiple regression model suggests that about 78% of the variation in the predictors can be explained by Oil Usage
True or False
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regression of oil usage on degree days:
1)
as p vlaue is very less ; hence Yes
2)
variation in Oil Usage is explained by the variation in Degree Days =Rsquare =29.4% option C
3)
for each one unit increase in Degree Days, Oil Usage increases by 0.25 units option A
4)
as p value is high ; intercept not different from 0;
hence No
below is regression output with all variables:
5)
as differnce in R square =0.784-0.294 =0.49 hence 49%
option D is correct
6)
as p value is for number people is high ; therefore false
7)
Yes
8)
predicted Oil Usage =-218.31+0.275*600+86.989*3+5.267*4=229
option C
9)
predicted Oil Usage =-218.31+0.275*458+86.989*1+5.267*1 =0
option D
10)
true
Regression Statistics Multiple R 0.542663 R Square 0.294484 Adjusted R Square 0.275917 Standard Error 150.3603 Observations 40 ANOVA df SS MS F Significance F Regression 1 358595.1 358595.1 15.86126 0.000297 Residual 38 859112.8 22608.23 Total 39 1217708 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 58.80354 46.51918 1.264071 0.213904 -35.3696 152.9767 Degree 0.251425 0.063131 3.98262 0.000297 0.123624 0.379226Related Questions
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