Customer Oil Usage Degree Days Home Index Number People 1 381 888 3 3 2 171 176

Question

Customer Oil Usage Degree Days Home Index Number People 1 381 888 3 3 2 171 176 5 7 3 644 1073 5 4 4 19 126 2 4 5 394 645 5 5 6 153 326 4 6 7 7 1229 1 3 8 319 1218 2 4 9 40 570 2 1 10 121 334 1 7 11 243 738 3 3 12 200 1464 1 5 13 402 880 4 5 14 118 1134 1 5 15 319 1019 3 4 16 185 460 2 3 17 209 257 5 4 18 467 779 5 4 19 50 128 2 4 20 153 371 2 5 21 94 178 3 6 22 574 933 5 3 23 191 295 3 5 24 679 1358 4 5 25 305 626 4 5 26 85 237 2 7 27 87 813 1 6 28 170 385 3 5 29 92 678 1 4 30 35 54 2 3 31 60 314 1 5 32 507 898 4 3 33 148 966 1 6 34 83 84 5 3 35 318 919 3 4 36 85 379 1 4 37 245 512 3 4 38 56 355 2 3 39 303 759 3 3 40 10 777 1 4 QUESTION 4 [4] The Y-intercept of the simple regression model predicting Oil Usage from Degree Days is significantly different from 0 in the population. No Yes QUESTION 5 [5] Predict Oil Usage for all 40 customers using all remaining varaibles as predictors. This regression model predicts that predicted by the simple regression model more variation in Oil Usage than 65% O 34% O 0.49% 49% QUESTION 6 [6] Each individual predictor in the multiple regression model has a significant effect on Oil Usage. False O True

Explanation / Answer

below is regression output for above data with degree days and total paramters:

with degree days:

with total variables"

4)

as y intercept has p value very high therefore No

5) for difference in R square =0.784-0.294 =0.49 ~ 49%

threfore option 49% is correct

6)

as p vlaue for Number people is very high ; therfore False

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 0.379226

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