Run a multiple regression for the Y and all the X variables. Interpret the regression results. 1234567
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Run a multiple regression for the Y and all the X variables. Interpret the regression results.
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1234567 00 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 annual net sales/$1000 231 156 10 519 437 487 299 195 20 68 570 428 464 15 65 98 398 161 397 497 528 99 0.5 347 341 507 400 B size of sales district/1000 families 8 4 4 16 14 13 10 8 2 aawwwUSNUN 5 12 14 15 3 16 6 14 16 16 7 2 11 12 16 12 6 U 3 2.2 0.5 5.5 4.4 number sq. ft./1000 y/$1000 4.8 3.1 2.5 1.2 0.6 5.4 4.2 4.7 0.6 1.2 26 1.6 4.3 2.6 3.8 5.3 5.6 D 0.8 1.1 3.6 3.5 5.1 8.6 amount spent on inventor advertizing/$ 1000 294 232 149 600 567 571 512 347 212 102 788 577 535 163 168 151 342 196 453 518 615 278 142 461 382 590 E 517 773w00 8 12 11 12 8 00 min t 8 3 5 17 HESSEN ONN MOT 11 11 3 5 5 6 7 10. 12 12 3 3 10 10 12 7 F number of competing stores in distric 12515 4 10 12 15 8 1 7 3 14 11 10 S 4 13 7 1 LOVN65000 14 12 8 G 1234567 00 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 annual net sales/$1000 231 156 10 519 437 487 299 195 20 68 570 428 464 15 65 98 398 161 397 497 528 99 0.5 347 341 507 400 B size of sales district/1000 families 8 4 4 16 14 13 10 8 2 aawwwUSNUN 5 12 14 15 3 16 6 14 16 16 7 2 11 12 16 12 6 U 3 2.2 0.5 5.5 4.4 number sq. ft./1000 y/$1000 4.8 3.1 2.5 1.2 0.6 5.4 4.2 4.7 0.6 1.2 26 1.6 4.3 2.6 3.8 5.3 5.6 D 0.8 1.1 3.6 3.5 5.1 8.6 amount spent on inventor advertizing/$ 1000 294 232 149 600 567 571 512 347 212 102 788 577 535 163 168 151 342 196 453 518 615 278 142 461 382 590 E 517 773w00 8 12 11 12 8 00 min t 8 3 5 17 HESSEN ONN MOT 11 11 3 5 5 6 7 10. 12 12 3 3 10 10 12 7 F number of competing stores in distric 12515 4 10 12 15 8 1 7 3 14 11 10 S 4 13 7 1 LOVN65000 14 12 8 G
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The multiple regression results indicate that the overall model is significant with an Adjusted Rsquared value of 0634 This suggests that the model is ... View the full answer
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