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Posted on Jul 14, 2024

how would you interpret the data(regressions) in order to answer the following questions about the relations. please specify: Examine the Bivariate Relationships: e.g. How is

how would you interpret the data(regressions) in order to answer the following questions about the relations. please specify:

Examine the Bivariate Relationships: e.g. How is the size of the customer base around each store related to our expected sales?

Use the data to determine the relationship between SALES and FAMILIES (the size of the customer base around each store).
Use the data to determine the relationship between SALES and SQFT (the area each store occupies).
Use the data to determine the relationship between SALES and Inventory (the amount of items in each store).
Use the data to determine the relationship between SALES and Advertising (the amount of money each store spends on promotion).
Use the data to determine the relationship between SALES and Stores (the number of competing stores near by each store in our franchise).there are a couple of regressions missing. however, I would like to get help interpreting the data for the report.

+O 10 SALES SQFT INVENTORY ADVERTISING FAMILIES STORES 231 3.00 294 8.20 8.20 11 156 2.20 232 6.90 4.10 12 10 0.50 149 3.00 4.30 15 519 5.50 600 12.00 16.10 1 437 4.40 567 10.60 14.10 5 487 4.80 571 11.80 12.70 4 299 3.10 512 8.10 10.10 195 2.50 347 7.70 8.40 12 20 1.20 212 3.30 2.10 15 68 0.60 102 4.90 4.70 8 570 5.40 788 17.40 12.30 1 428 4.20 577 10.50 14.00 7 464 4.70 535 11.30 15.00 3 15 0.60 163 2.50 2.50 14 65 1.20 168 4.70 3.30 11 98 1.60 151 4.60 2.70 10 398 4.30 342 5.50 16.00 4 161 2.60 196 7.20 6.30 13 397 3.80 453 10.40 13.90 7 497 5.30 518 11.50 16.30 1 528 5.60 615 12.30 16.00 0 99 0.80 278 2.80 6.50 14 0.5 1.10 142 3.10 1.60 12 347 3.60 461 9.60 11.30 6 341 3.50 382 9.80 11.50 5 507 5.10 590 12.00 15.70 0 400 8.60 517 7.00 12.00 8 O 00 Ouro The data are for each franchise store. SALES = annual net sales/$1000 SQFT = number sq. ft./1000 INVENTORY = inventory/$1000 ADVERTISING = amount spent on advertizing/$1000 FAMILIES = size of sales district/1000 families STORES = number of competing stores in district Regression Statistics Multiple R 0.89409208 R Square 0.79940065 Adjusted R S 0.79137667 Standard Erro 87.7247733 Observations 27 ANOVA df Regression Residual Total SS MS Significance F 1 766689.456 766689.456 99.6265248 3.3297E-10 25 192390.896 7695.63585 26 959080.352 Intercept SQFT Coefficients Standard Error t Stat 2.57700629 33.084595 0.07789143 85.3888733 8.55487744 9.98130877 P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% 0.9385345 -65.5619926 70.7160052 -65.5619926 70.7160052 3.3297E-10 67.7697734 103.007973 67.7697734 103.007973 RESIDUAL OUTPUT PROBABILITY OUTPUT SQFT Residual Plot y=C+b*x c= intercept 200 SALES Residuals 0 0.00 -200 2.00 4.00 6.00 8.00 10.00 -400 SQFT SQFT Line Fit Plot 1000 SALES 500 SALES Observation redicted SALE Residuals indard Residuals 1 258.743626 -27.7436261 -0.32252073 2 190.432532 -34.4325316 -0.40027952 3 45.2714429 -35.2714429 -0.4100319 4 472.215809 46.7841907 0.54386804 5 378.288057 58.7119432 0.68252862 6 412.443614 74.5563856 0.86672088 7 267.282505 31.7174946 0.36871711 8 216.049189 -21.0491895 -0.24469765 9 105.043658 -85.0436583 -0.98863584 10 53.8103323 14.1896677 0.16495544 11 463.67693 106.32307 1.23600982 12 361.210258 66.7897422 0.77643335 13 403.904694 60.0953056 0.69861027 14 53.8103323 -38.8103323 -0.45117162 15 105.043658 -40.0436583 -0.46550909 16 139.199206 -41.1992056 -0.47894237 17 369.749178 28.2508223 0.32841691 18 224.588069 -63.5880687 -0.73921377 19 327.054721 69.9452793 0.81311659 20 455.138051 41.861949 0.48664679 21 480.754689 47.2453114 0.54922858 22 70.8881059 28.1118941 0.32680186 23 96.504769 -96.004769 -1.11605917 24 309.976942 37.023058 0.43039449 25 301.438063 39.5619372 0.45990906 26 438.060252 68.9397481 0.80142725 27 736.921317 -336.921317 -3.91672341 Percentile 1.85185185 5.55555556 9.25925926 12.962963 16.6666667 20.3703704 24.0740741 27.7777778 31.4814815 35.1851852 38.8888889 42.5925926 46.2962963 50 53.7037037 57.4074074 61.1111111 64.8148148 68.5185185 72.2222222 75.9259259 79.6296296 83.3333333 87.037037 90.7407407 94.4444444 98.1481481 SALES 0.5 10 15 20 65 68 98 99 156 161 195 231 299 341 347 397 398 400 428 437 464 487 497 507 519 528 570 SQFT 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 0 0.00 Predicted SALES 2.00 4.00 6.00 8.00 10.00 3.00 2.20 0.50 5.50 4.40 4.80 3.10 2.50 1.20 0.60 5.40 4.20 4.70 0.60 1.20 1.60 4.30 2.60 3.80 5.30 5.60 0.80 1.10 3.60 3.50 5.10 SQFT Normal Probability Plot 600 400 200 0 20 40 80 100 120 60 Sample Percentile Regression Statistics Multiple R 0.94550363 R Square 0.89397711 Adjusted RS 0.88973619 Standard Erre 63.7760064 Observations 27 ANOVA df Regression Residual Total SS MS F Significance 1 857395.877 857395.877 210.798128 1.0929E-13 25 101684.475 4067.37899 26 959080.352 Coefficients Standard Erro Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -81.504352 28.1665061 -2.8936621 0.00778405 -2.8936621 0.00778405 -139.51436 -23.494347 -139.51436 -23.494347 INVENTORY 0.94992521 0.06542685 14.5188887 1.0929E-13 0.81517608 1.0929E-13 0.81517608 1.08467433 0.81517608 1.08467433 INVENTORY Residual Plot RESIDUAL OUTPUT PROBABILITY OUTPUT 200 100 SALES 0 -1000 100 200 200 800 800400 509 600 700 800 900 -200 INVENTORY INVENTORY Line Fit Plot 1000 500 SALES 0 Predicted SALES Observation edicted SALE Residuals indard Residuals 1 197.773658 33.2263415 0.53130252 2 138.878296 17.1217043 0.27378291 3 3 60.0345035 -50.034504 -0.8000718 4 4 488.450772 30.5492283 0.4884944 5 5 457.10324 -20.10324 -0.3214589 6 460.902941 26.0970593 0.41730244 7 404.857354 -105.85735 -1.6927015 8 248.119694 -53.119694 -0.8494052 9 9 119.879792 -99.879792 -1.5971179 10 15.3880188 52.6119812 0.84128667 11 667.036711 -97.036711 -1.5516559 12 466.602492 -38.602492 0.6172693 13 426.705633 37.2943667 0.59635187 14 73.3334564 -58.333456 -0.9327753 15 78.0830824 -13.083082 -0.2092037 16 61.9343539 36.0656461 0.57670414 17 243.370068 154.629932 2.47259458 18 104.680988 56.3190118 0.90056357 19 348.811766 48.1882337 0.77054917 20 410.556905 86.4430953 1.38225974 21 502.69965 25.3003502 0.40456274 22 182.574855 -83.574855 -1.3363954 23 53.3850271 -52.885027 -0.8456528 24 356.411168 -9.411168 -0.1504883 25 281.367077 59.6329234 0.95355434 26 478.95152 28.0484804 0.44850644 27 409.60698 -9.6069795 -0.1536195 Percentie 1.85185185 5.55555556 9.25925926 12.962963 16.6666667 20.3703704 24.0740741 27.7777778 31.4814815 35.1851852 38.8888889 42.5925926 46.2962963 50 53.7037037 57,4074074 61.1111111 64.8148148 68.5185185 72.2222222 75.9259259 79.6296296 83.3333333 87.037037 90.7407407 94.4444444 98.1481481 SALES 0.5 10 15 20 65 68 98 99 156 161 195 231 299 341 347 397 398 400 428 437 464 487 497 507 519 528 570 INVENTORY 231 294 156 232 10 149 519 600 437 567 487 571 299 512 195 347 20 212 68 102 570 788 428 577 464 535 15 163 65 168 98 151 398 342 161 196 397 453 497 518 528 615 99 278 0.5 142 347 461 341 382 507 590 400 517 0 200 800 1000 400 600 INVENTORY Normal Probability Plot 600 400 200 0 0 20 40 60 80 100 120 Sample Percentile Regression Statistics Multiple R 0.91402407 R Square 0.83544001 Adjusted RS 0.82885761 Standard Erre 79.4547051 Observations 27 ANOVA df Regression Residual Total SS MS F Significance 1 801254.098 801254.098 126.920281 2.7454E-11 25 157826.254 6313.05016 26 959080.352 Intercept ADVERTISIN Coefficients Standard Erro. Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% -90.149619 36.7696169 -2.4517421 0.02154721 -165.87806 -14.421176 -165.87806 -14.421176 46.509098 4.12831099 11.2658901 2.7454E-11 38.0066824 55.01 15137 38.0066824 55.0115137 ADVERTISING Residual Plot 400 RESIDUAL OUTPUT PROBABILITY OUTPUT 200 Residuals SALES 0 0.bo -200 5.00 15.00 20.00 10.00 ADVERTISING ADVERTISING Line Fit Plot 1000 500 0 0.00 SALES Predicted SALES 5.00 15.00 20.00 Observation edicted SALE Residuals indard Residuals 1 291.224976 -60.224976 -0.7729897 2 230.763162 -74.763162 -0.9595878 3 49.3776749 39.377675 -0.5054138 4 467.959557 51.0404427 0.65510586 5 402.846838 34.1531623 0.43835703 6 458.657746 28.3422535 0.36377381 7 286.574075 12.4259251 0.15948718 8 267.970427 -72.970427 -0.936578 9 9 63.3304021 43.330402 -0.5561472 10 137.744966 69.744966 -0.8951791 11 719.108669 -149.10867 -1.913815 12 398.19591 29.8040898 0.38253653 13 435.403197 28.5968025 0.36704095 14 26.1231259 -11.123126 -0.1427657 15 128.443133 63.443133 -0.8142948 16 123.792227 -25.792227 -0.3310441 17 165.65042 232.34958 2.98221494 18 244.715878 -83.715878 -1.0744962 19 393.544983 3.45501728 0.04434527 20 444.705008 52.2949918 0.67120804 21 481.912296 46.0877045 0.59153729 22 40.0758531 58.9241469 0.75629347 23 54.0285803 -53.52858 -0.6870412 24 356.337722 -9.337722 -0.11985 25 365.63955 -24.63955 -0.3162495 26 467.959557 39.0404427 0.50108544 27 235.414067 164.585933 2.11246617 Percentile 1.85185185 5.55555556 9.25925926 12.962963 16.6666667 20.3703704 24.0740741 27.7777778 31.4814815 35.1851852 38.8888889 42.5925926 46.2962963 50 53.7037037 57.4074074 61.1111111 64.8148148 68.5185185 72.2222222 75.9259259 79.6296296 83.3333333 87.037037 90.7407407 94.4444444 98.1481481 SALES 0.5 10 15 20 65 68 98 99 156 161 195 231 299 341 347 397 398 400 428 437 464 487 497 507 519 528 570 ADVERTISING 231 8.20 156 6.90 10 3.00 519 12.00 437 10.60 487 11.80 299 8.10 195 7.70 20 3.30 68 4.90 570 17.40 428 10.50 464 11.30 15 2.50 65 4.70 98 4.60 398 5.50 161 7.20 397 10.40 497 11.50 528 12.30 99 2.80 0.5 3.10 347 9.60 341 9.80 507 12.00 400 7.00 10.00 ADVERTISING Normal Probability Plot 600 400 200 0 0 20 40 60 80 100 120 Sample Percentile +O 10 SALES SQFT INVENTORY ADVERTISING FAMILIES STORES 231 3.00 294 8.20 8.20 11 156 2.20 232 6.90 4.10 12 10 0.50 149 3.00 4.30 15 519 5.50 600 12.00 16.10 1 437 4.40 567 10.60 14.10 5 487 4.80 571 11.80 12.70 4 299 3.10 512 8.10 10.10 195 2.50 347 7.70 8.40 12 20 1.20 212 3.30 2.10 15 68 0.60 102 4.90 4.70 8 570 5.40 788 17.40 12.30 1 428 4.20 577 10.50 14.00 7 464 4.70 535 11.30 15.00 3 15 0.60 163 2.50 2.50 14 65 1.20 168 4.70 3.30 11 98 1.60 151 4.60 2.70 10 398 4.30 342 5.50 16.00 4 161 2.60 196 7.20 6.30 13 397 3.80 453 10.40 13.90 7 497 5.30 518 11.50 16.30 1 528 5.60 615 12.30 16.00 0 99 0.80 278 2.80 6.50 14 0.5 1.10 142 3.10 1.60 12 347 3.60 461 9.60 11.30 6 341 3.50 382 9.80 11.50 5 507 5.10 590 12.00 15.70 0 400 8.60 517 7.00 12.00 8 O 00 Ouro The data are for each franchise store. SALES = annual net sales/$1000 SQFT = number sq. ft./1000 INVENTORY = inventory/$1000 ADVERTISING = amount spent on advertizing/$1000 FAMILIES = size of sales district/1000 families STORES = number of competing stores in district Regression Statistics Multiple R 0.89409208 R Square 0.79940065 Adjusted R S 0.79137667 Standard Erro 87.7247733 Observations 27 ANOVA df Regression Residual Total SS MS Significance F 1 766689.456 766689.456 99.6265248 3.3297E-10 25 192390.896 7695.63585 26 959080.352 Intercept SQFT Coefficients Standard Error t Stat 2.57700629 33.084595 0.07789143 85.3888733 8.55487744 9.98130877 P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% 0.9385345 -65.5619926 70.7160052 -65.5619926 70.7160052 3.3297E-10 67.7697734 103.007973 67.7697734 103.007973 RESIDUAL OUTPUT PROBABILITY OUTPUT SQFT Residual Plot y=C+b*x c= intercept 200 SALES Residuals 0 0.00 -200 2.00 4.00 6.00 8.00 10.00 -400 SQFT SQFT Line Fit Plot 1000 SALES 500 SALES Observation redicted SALE Residuals indard Residuals 1 258.743626 -27.7436261 -0.32252073 2 190.432532 -34.4325316 -0.40027952 3 45.2714429 -35.2714429 -0.4100319 4 472.215809 46.7841907 0.54386804 5 378.288057 58.7119432 0.68252862 6 412.443614 74.5563856 0.86672088 7 267.282505 31.7174946 0.36871711 8 216.049189 -21.0491895 -0.24469765 9 105.043658 -85.0436583 -0.98863584 10 53.8103323 14.1896677 0.16495544 11 463.67693 106.32307 1.23600982 12 361.210258 66.7897422 0.77643335 13 403.904694 60.0953056 0.69861027 14 53.8103323 -38.8103323 -0.45117162 15 105.043658 -40.0436583 -0.46550909 16 139.199206 -41.1992056 -0.47894237 17 369.749178 28.2508223 0.32841691 18 224.588069 -63.5880687 -0.73921377 19 327.054721 69.9452793 0.81311659 20 455.138051 41.861949 0.48664679 21 480.754689 47.2453114 0.54922858 22 70.8881059 28.1118941 0.32680186 23 96.504769 -96.004769 -1.11605917 24 309.976942 37.023058 0.43039449 25 301.438063 39.5619372 0.45990906 26 438.060252 68.9397481 0.80142725 27 736.921317 -336.921317 -3.91672341 Percentile 1.85185185 5.55555556 9.25925926 12.962963 16.6666667 20.3703704 24.0740741 27.7777778 31.4814815 35.1851852 38.8888889 42.5925926 46.2962963 50 53.7037037 57.4074074 61.1111111 64.8148148 68.5185185 72.2222222 75.9259259 79.6296296 83.3333333 87.037037 90.7407407 94.4444444 98.1481481 SALES 0.5 10 15 20 65 68 98 99 156 161 195 231 299 341 347 397 398 400 428 437 464 487 497 507 519 528 570 SQFT 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 0 0.00 Predicted SALES 2.00 4.00 6.00 8.00 10.00 3.00 2.20 0.50 5.50 4.40 4.80 3.10 2.50 1.20 0.60 5.40 4.20 4.70 0.60 1.20 1.60 4.30 2.60 3.80 5.30 5.60 0.80 1.10 3.60 3.50 5.10 SQFT Normal Probability Plot 600 400 200 0 20 40 80 100 120 60 Sample Percentile Regression Statistics Multiple R 0.94550363 R Square 0.89397711 Adjusted RS 0.88973619 Standard Erre 63.7760064 Observations 27 ANOVA df Regression Residual Total SS MS F Significance 1 857395.877 857395.877 210.798128 1.0929E-13 25 101684.475 4067.37899 26 959080.352 Coefficients Standard Erro Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -81.504352 28.1665061 -2.8936621 0.00778405 -2.8936621 0.00778405 -139.51436 -23.494347 -139.51436 -23.494347 INVENTORY 0.94992521 0.06542685 14.5188887 1.0929E-13 0.81517608 1.0929E-13 0.81517608 1.08467433 0.81517608 1.08467433 INVENTORY Residual Plot RESIDUAL OUTPUT PROBABILITY OUTPUT 200 100 SALES 0 -1000 100 200 200 800 800400 509 600 700 800 900 -200 INVENTORY INVENTORY Line Fit Plot 1000 500 SALES 0 Predicted SALES Observation edicted SALE Residuals indard Residuals 1 197.773658 33.2263415 0.53130252 2 138.878296 17.1217043 0.27378291 3 3 60.0345035 -50.034504 -0.8000718 4 4 488.450772 30.5492283 0.4884944 5 5 457.10324 -20.10324 -0.3214589 6 460.902941 26.0970593 0.41730244 7 404.857354 -105.85735 -1.6927015 8 248.119694 -53.119694 -0.8494052 9 9 119.879792 -99.879792 -1.5971179 10 15.3880188 52.6119812 0.84128667 11 667.036711 -97.036711 -1.5516559 12 466.602492 -38.602492 0.6172693 13 426.705633 37.2943667 0.59635187 14 73.3334564 -58.333456 -0.9327753 15 78.0830824 -13.083082 -0.2092037 16 61.9343539 36.0656461 0.57670414 17 243.370068 154.629932 2.47259458 18 104.680988 56.3190118 0.90056357 19 348.811766 48.1882337 0.77054917 20 410.556905 86.4430953 1.38225974 21 502.69965 25.3003502 0.40456274 22 182.574855 -83.574855 -1.3363954 23 53.3850271 -52.885027 -0.8456528 24 356.411168 -9.411168 -0.1504883 25 281.367077 59.6329234 0.95355434 26 478.95152 28.0484804 0.44850644 27 409.60698 -9.6069795 -0.1536195 Percentie 1.85185185 5.55555556 9.25925926 12.962963 16.6666667 20.3703704 24.0740741 27.7777778 31.4814815 35.1851852 38.8888889 42.5925926 46.2962963 50 53.7037037 57,4074074 61.1111111 64.8148148 68.5185185 72.2222222 75.9259259 79.6296296 83.3333333 87.037037 90.7407407 94.4444444 98.1481481 SALES 0.5 10 15 20 65 68 98 99 156 161 195 231 299 341 347 397 398 400 428 437 464 487 497 507 519 528 570 INVENTORY 231 294 156 232 10 149 519 600 437 567 487 571 299 512 195 347 20 212 68 102 570 788 428 577 464 535 15 163 65 168 98 151 398 342 161 196 397 453 497 518 528 615 99 278 0.5 142 347 461 341 382 507 590 400 517 0 200 800 1000 400 600 INVENTORY Normal Probability Plot 600 400 200 0 0 20 40 60 80 100 120 Sample Percentile Regression Statistics Multiple R 0.91402407 R Square 0.83544001 Adjusted RS 0.82885761 Standard Erre 79.4547051 Observations 27 ANOVA df Regression Residual Total SS MS F Significance 1 801254.098 801254.098 126.920281 2.7454E-11 25 157826.254 6313.05016 26 959080.352 Intercept ADVERTISIN Coefficients Standard Erro. Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% -90.149619 36.7696169 -2.4517421 0.02154721 -165.87806 -14.421176 -165.87806 -14.421176 46.509098 4.12831099 11.2658901 2.7454E-11 38.0066824 55.01 15137 38.0066824 55.0115137 ADVERTISING Residual Plot 400 RESIDUAL OUTPUT PROBABILITY OUTPUT 200 Residuals SALES 0 0.bo -200 5.00 15.00 20.00 10.00 ADVERTISING ADVERTISING Line Fit Plot 1000 500 0 0.00 SALES Predicted SALES 5.00 15.00 20.00 Observation edicted SALE Residuals indard Residuals 1 291.224976 -60.224976 -0.7729897 2 230.763162 -74.763162 -0.9595878 3 49.3776749 39.377675 -0.5054138 4 467.959557 51.0404427 0.65510586 5 402.846838 34.1531623 0.43835703 6 458.657746 28.3422535 0.36377381 7 286.574075 12.4259251 0.15948718 8 267.970427 -72.970427 -0.936578 9 9 63.3304021 43.330402 -0.5561472 10 137.744966 69.744966 -0.8951791 11 719.108669 -149.10867 -1.913815 12 398.19591 29.8040898 0.38253653 13 435.403197 28.5968025 0.36704095 14 26.1231259 -11.123126 -0.1427657 15 128.443133 63.443133 -0.8142948 16 123.792227 -25.792227 -0.3310441 17 165.65042 232.34958 2.98221494 18 244.715878 -83.715878 -1.0744962 19 393.544983 3.45501728 0.04434527 20 444.705008 52.2949918 0.67120804 21 481.912296 46.0877045 0.59153729 22 40.0758531 58.9241469 0.75629347 23 54.0285803 -53.52858 -0.6870412 24 356.337722 -9.337722 -0.11985 25 365.63955 -24.63955 -0.3162495 26 467.959557 39.0404427 0.50108544 27 235.414067 164.585933 2.11246617 Percentile 1.85185185 5.55555556 9.25925926 12.962963 16.6666667 20.3703704 24.0740741 27.7777778 31.4814815 35.1851852 38.8888889 42.5925926 46.2962963 50 53.7037037 57.4074074 61.1111111 64.8148148 68.5185185 72.2222222 75.9259259 79.6296296 83.3333333 87.037037 90.7407407 94.4444444 98.1481481 SALES 0.5 10 15 20 65 68 98 99 156 161 195 231 299 341 347 397 398 400 428 437 464 487 497 507 519 528 570 ADVERTISING 231 8.20 156 6.90 10 3.00 519 12.00 437 10.60 487 11.80 299 8.10 195 7.70 20 3.30 68 4.90 570 17.40 428 10.50 464 11.30 15 2.50 65 4.70 98 4.60 398 5.50 161 7.20 397 10.40 497 11.50 528 12.30 99 2.80 0.5 3.10 347 9.60 341 9.80 507 12.00 400 7.00 10.00 ADVERTISING Normal Probability Plot 600 400 200 0 0 20 40 60 80 100 120 Sample Percentile