3 - 20 Points Data found in Assignment 3 Data excel file. Deliverable 1 (5 points): A manager is under pressure to increase daily net profits. Data has been collected for the past 50 days indicating yield, number of employees, number of orders, advertising expense, and net profits. 1. Perform regression analysis to determine how the four variables affect net profits. Write out the equation of the final model. Be sure to show the regression tables and decisions made to reach the final model. Use Alpha = .01 to determine variables to remove from model. 2. Would you use this model? Why or why not? 3. Given: Yield = 85.76%, number of employees = 21, number of orders = 355, and advertising expense = $505.00, calculate predicted net profits. 4. What advice would you give the manager? (Be brief, this is not an essay.) Deliverable 2 (5 points): A local community bank is interested in predicting the selling price of local dairy farms. The following data has been collected for 35 farms that have sold in the past few years: total acres, tillable acres, whether the farm has a pond, how many milking cows, and the selling price. Use Alpha = 0.05 to determine variables to remove from model. 1. Can the variables be used to predict the selling price of farms? 2. Write out the equation of the final model. Be sure to show the regression tables and decisions made to reach the final model. 3. A farmer owns a farm without a pond and is planning on selling the farm. A backhoe owner is willing to dig a pond at a cost of $49,995. Should the farmer hire the backhoe owner to dig the pond? Support you answer. Deliverable 3 (10 points): We wish to determine the impact of Specification Buying, X11, on Satisfaction Level, X10. To do so we will split the Hatco data file into two separate data sets based on the Specification Buying, X11. This variable has two categories: 1=employs total value analysis approach, evaluating each purchase separately; 0 = use of specification buying. Sort the entire Hatco data set based on Specification Buying. This will create two separate groups of records. Those records with X11 = 0 and those records with X11 = 1. Treat these as two distinct data sets. For the 2 data sets, X11 = 0 and X11 = 1, perform regression analysis on Satisfaction Level X10 as a function of the first seven variables (Delivery Speed, Price Level, Price Flexibility, Manufacturer Image, Service, Salesforce Image, Product Quality). Use Alpha = .01 to determine variables to remove from model. For X11 = 0 and X11 = 1: Compare the two models and explain the differences between the two models. From a business perspective, why do they differ? Instructions Submit your assignment using the drop box. The file name should follow the format: Your-last-name Assgn3.xlxs Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 Yield 93.58% 74.49% 74.39% 90.80% 70.28% 84.69% 57.90% 56.75% 75.82% 67.05% 96.35% 82.09% 89.18% 61.07% 61.09% 86.24% 95.91% 90.88% 92.03% 94.64% 96.70% 94.16% 97.65% 67.59% 89.88% 95.43% 72.06% 91.99% 65.20% 99.82% 60.28% 88.98% 74.85% 96.22% 78.84% 64.81% 91.46% 63.29% 60.64% 86.77% 91.44% 70.01% 69.12% 90.76% 75.19% 93.12% 92.71% # of Employees 16 17 15 20 20 19 20 15 19 15 18 17 19 15 16 18 16 17 15 18 19 18 17 16 17 19 18 16 18 18 17 18 20 19 19 18 18 16 16 15 15 19 20 15 16 19 15 # of Orders 345 315 270 351 324 358 220 255 299 287 397 331 358 221 269 352 404 365 397 411 387 354 411 284 337 417 279 341 237 393 229 374 288 408 312 277 385 285 270 383 353 263 262 389 318 382 383 Advertising $ 483 539 500 478 522 513 455 499 479 518 497 520 527 461 474 525 547 478 456 529 528 497 548 520 501 513 523 550 532 505 540 507 496 463 544 545 520 485 533 501 516 527 450 485 518 501 517 Total Net Profit $ 30,850.00 $ 26,292.00 $ 24,863.00 $ 31,277.00 $ 26,218.00 $ 29,496.00 $ 20,516.00 $ 20,812.00 $ 24,665.00 $ 25,094.00 $ 32,163.00 $ 28,938.00 $ 31,061.00 $ 21,269.00 $ 22,670.00 $ 30,038.00 $ 33,403.00 $ 29,911.00 $ 31,706.00 $ 32,962.00 $ 33,333.00 $ 31,052.00 $ 34,829.00 $ 22,496.00 $ 30,606.00 $ 34,623.00 $ 24,750.00 $ 31,342.00 $ 20,967.00 $ 33,372.00 $ 20,995.00 $ 32,383.00 $ 26,766.00 $ 33,165.00 $ 25,719.00 $ 22,922.00 $ 33,597.00 $ 22,044.00 $ 20,422.00 $ 30,918.00 $ 32,545.00 $ 22,991.00 $ 23,726.00 $ 32,306.00 $ 25,839.00 $ 33,826.00 $ 31,940.00 48 49 50 84.97% 96.43% 63.30% 19 17 16 360 425 272 454 506 450 $ $ $ 28,548.00 34,573.00 20,654.00 Total Acres 42 149 110 152 176 156 27 159 80 121 80 127 127 137 37 179 137 181 85 99 169 27 42 183 127 39 76 100 141 67 86 178 171 87 53 Tillable Acres 170 215 175 173 205 241 204 220 224 184 215 178 190 207 167 217 151 188 286 244 201 223 215 219 137 212 200 184 208 184 207 227 203 225 183 Pond (1-YES, 0-NO) 1 0 0 0 1 1 0 0 1 0 1 1 0 1 0 1 0 1 0 0 0 0 0 1 0 0 1 0 1 1 0 0 1 0 0 Milking Cows 49 50 48 48 49 69 50 60 63 45 51 48 41 57 37 61 31 52 66 67 55 64 46 52 24 59 52 50 43 37 46 58 47 46 43 Selling Price 1085151 1400854 1095138 1138518 1349662 1619233 1286390 1369171 1457636 1250047 1401943 1258047 1189372 1345573 1088905 1528205 984130 1202614 1874805 1522279 1309976 1516596 1409517 1468242 828779 1313937 1372339 1263520 1411687 1232068 1406330 1470129 1337255 1468803 1225213 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0 20.0 21.0 22.0 23.0 24.0 25.0 26.0 27.0 28.0 29.0 30.0 31.0 32.0 33.0 34.0 35.0 36.0 37.0 38.0 39.0 40.0 41.0 42.0 43.0 44.0 45.0 Delivery Speed 4.1 1.8 3.4 2.7 6.0 1.9 4.6 1.3 5.5 4.0 2.4 3.9 2.8 3.7 4.7 3.4 3.2 4.9 5.3 4.7 3.3 3.4 3.0 2.4 5.1 4.6 2.4 5.2 3.5 4.1 3.0 2.8 5.2 3.4 2.4 1.8 3.6 4.0 0.0 2.4 1.9 5.9 4.9 5.0 2.0 Price Price Level Flexibility 0.6 6.9 3.0 6.3 5.2 5.7 1.0 7.1 0.9 9.6 3.3 7.9 2.4 9.5 4.2 6.2 1.6 9.4 3.5 6.5 1.6 8.8 2.2 9.1 1.4 8.1 1.5 8.6 1.3 9.9 2.0 9.7 4.1 5.7 1.8 7.7 1.4 9.7 1.3 9.9 0.9 8.6 0.4 8.3 4.0 9.1 1.5 6.7 1.4 8.7 2.1 7.9 1.5 6.6 1.3 9.7 2.8 9.9 3.7 5.9 3.2 6.0 3.8 8.9 2.0 9.3 3.7 6.4 1.0 7.7 3.3 7.5 4.0 5.8 0.9 9.1 2.1 6.9 2.0 6.4 3.4 7.6 0.9 9.6 2.3 9.3 1.3 8.6 2.6 6.5 Manufact urer Image 4.7 6.6 6.0 5.9 7.8 4.8 6.6 5.1 4.7 6.0 4.8 4.6 3.8 5.7 6.7 4.7 5.1 4.3 6.1 6.7 4.0 2.5 7.1 4.8 4.8 5.8 4.8 6.1 3.5 5.5 5.3 6.9 5.9 5.7 3.4 4.5 5.8 5.4 5.4 4.5 4.6 7.8 4.5 4.7 3.7 Service 2.4 2.5 4.3 1.8 3.4 2.6 3.5 2.8 3.5 3.7 2.0 3.0 2.1 2.7 3.0 2.7 3.6 3.4 3.3 3.0 2.1 1.2 3.5 1.9 3.3 3.4 1.9 3.2 3.1 3.9 3.1 3.3 3.7 3.5 1.7 2.5 3.7 2.4 1.1 2.1 2.6 3.4 3.6 3.1 2.4 Salesforc e Image 2.3 4.0 2.7 2.3 4.6 1.9 4.5 2.2 3.0 3.2 2.8 2.5 1.4 3.7 2.6 1.7 2.9 1.5 3.9 2.6 1.8 1.7 3.4 2.5 2.6 2.8 2.5 3.9 1.7 3.0 3.0 3.2 2.4 3.4 1.1 2.4 2.5 2.6 2.6 2.2 2.5 4.6 1.3 2.5 1.7 Product Quality Firm Size 5.2 0 8.4 1 8.2 1 7.8 1 4.5 0 9.7 1 7.6 0 6.9 1 7.6 0 8.7 1 5.8 0 8.3 0 6.6 1 6.7 0 6.8 0 4.8 0 6.2 0 5.9 0 6.8 0 6.8 0 6.3 0 5.2 0 8.4 0 7.2 1 3.8 0 4.7 0 7.2 1 6.7 0 5.4 0 8.4 1 8.0 1 8.2 0 4.6 0 8.4 1 6.2 1 7.6 1 9.3 1 7.3 0 8.9 1 8.8 1 7.7 1 4.5 0 6.2 0 3.7 0 8.5 1 Usage Level 32.0 43.0 48.0 32.0 58.0 45.0 46.0 44.0 63.0 54.0 32.0 47.0 39.0 38.0 54.0 49.0 38.0 40.0 54.0 55.0 41.0 35.0 55.0 36.0 49.0 49.0 36.0 54.0 49.0 46.0 43.0 53.0 60.0 47.0 35.0 39.0 44.0 46.0 29.0 28.0 40.0 58.0 53.0 48.0 38.0 46.0 47.0 48.0 49.0 50.0 51.0 52.0 53.0 54.0 55.0 56.0 57.0 58.0 59.0 60.0 61.0 62.0 63.0 5.0 3.1 3.4 5.8 5.4 3.7 2.6 4.5 2.8 3.8 2.9 4.9 5.4 4.3 2.3 3.1 5.1 4.1 2.5 1.9 3.9 0.2 2.1 0.7 4.8 4.1 2.4 0.8 2.6 4.4 2.5 1.8 4.5 1.9 1.9 1.1 9.4 10.0 5.6 8.8 8.0 8.2 8.2 6.3 6.7 8.7 7.7 7.4 9.6 7.6 8.0 9.9 9.2 9.3 4.6 4.5 5.6 4.5 3.0 6.0 5.0 5.9 4.9 2.9 7.0 6.9 5.5 5.4 4.7 4.5 5.8 5.5 3.7 2.6 3.6 3.0 3.8 2.1 3.6 4.3 2.5 1.6 2.8 4.6 4.0 3.1 3.3 2.6 3.6 2.5 1.4 3.2 2.3 2.4 1.4 2.5 2.5 3.4 2.6 2.1 3.6 4.0 3.0 2.5 2.2 3.1 2.3 2.7 6.3 3.8 9.1 6.7 5.2 5.2 9.0 8.8 9.2 5.6 7.7 9.6 7.7 4.4 8.7 3.8 4.5 7.4 0 0 1 0 0 0 1 1 1 0 0 1 0 0 1 0 0 0 54.0 55.0 43.0 57.0 53.0 41.0 53.0 50.0 32.0 39.0 47.0 62.0 65.0 46.0 50.0 54.0 60.0 47.0 64.0 65.0 66.0 67.0 68.0 69.0 70.0 71.0 72.0 73.0 74.0 75.0 76.0 77.0 78.0 79.0 80.0 81.0 82.0 83.0 84.0 85.0 86.0 87.0 88.0 89.0 90.0 91.0 92.0 93.0 3.0 1.1 3.7 4.2 1.6 5.3 2.3 3.6 5.6 3.6 5.2 3.0 4.2 3.8 3.3 1.0 4.5 5.5 3.4 1.6 2.3 2.6 2.5 2.4 2.1 2.9 4.3 3.0 4.8 3.1 3.8 2.0 1.4 2.5 4.5 1.7 3.7 5.4 2.2 2.2 1.3 2.0 2.4 0.8 2.6 1.9 1.6 1.8 4.6 2.8 3.7 3.0 3.1 2.9 3.5 1.2 2.5 2.8 1.7 4.2 5.5 7.2 9.0 9.2 6.4 8.5 8.3 5.9 8.2 9.9 9.1 6.6 9.4 8.3 9.7 7.1 8.7 8.7 5.5 6.1 7.6 8.5 7.0 8.4 7.4 7.3 9.3 7.8 7.6 5.1 4.9 4.7 4.5 6.2 5.3 3.7 5.2 6.2 3.1 4.8 4.5 6.6 4.9 6.1 3.3 4.5 4.6 3.8 8.2 6.4 5.0 6.0 4.2 5.9 4.8 6.1 6.3 7.1 4.2 7.8 3.4 1.6 2.6 3.3 3.0 3.5 3.0 4.5 4.0 2.9 3.3 2.4 3.2 2.2 2.9 1.5 3.1 3.6 4.0 2.3 3.0 2.8 2.8 2.7 2.8 2.0 3.4 3.0 3.3 3.6 2.6 3.2 2.3 3.9 2.5 1.9 2.3 2.9 1.6 1.9 2.7 2.7 2.7 2.6 1.5 3.1 2.1 2.1 4.4 3.8 2.5 2.8 2.2 2.7 2.3 2.5 4.0 3.8 1.4 4.0 6.0 10.0 6.8 7.3 7.1 4.8 9.1 8.4 5.3 4.9 7.3 8.2 8.5 5.3 5.2 9.9 6.8 4.9 6.3 8.2 7.4 6.8 9.0 6.7 7.2 8.0 7.4 7.9 5.8 5.9 0 1 0 0 1 0 1 1 0 0 0 1 0 0 0 1 0 0 0 1 0 1 1 1 0 1 0 0 0 0 36.0 40.0 45.0 59.0 46.0 58.0 49.0 50.0 55.0 51.0 60.0 41.0 49.0 42.0 47.0 39.0 56.0 59.0 47.0 41.0 37.0 53.0 43.0 51.0 36.0 34.0 60.0 49.0 39.0 43.0 94.0 95.0 96.0 97.0 98.0 99.0 100.0 1.9 4.0 0.6 6.1 2.0 3.1 2.5 2.7 0.5 1.6 0.5 2.8 2.2 1.8 5.0 6.7 6.4 9.2 5.2 6.7 9.0 4.9 4.5 5.0 4.8 5.0 6.8 5.0 2.2 2.2 0.7 3.3 2.4 2.6 2.2 2.5 2.1 2.1 2.8 2.7 2.9 3.0 8.2 5.0 8.4 7.1 8.4 8.4 6.0 1 0 1 0 1 1 0 36.0 31.0 25.0 60.0 38.0 42.0 33.0 Satisfactio Specificati n Level on Buying 4.2 1 4.3 0 5.2 0 3.9 0 6.8 1 4.4 0 5.8 1 4.3 0 5.4 1 5.4 0 4.3 1 5.0 1 4.4 0 5.0 1 5.9 1 4.7 1 4.4 1 5.6 1 5.9 1 6.0 1 4.5 1 3.3 1 5.2 1 3.7 0 4.9 1 5.9 1 3.7 0 5.8 1 5.4 1 5.1 0 3.3 0 5.0 1 6.1 1 3.8 0 4.1 0 3.6 0 4.8 0 5.1 1 3.9 0 3.3 0 3.7 0 6.7 1 5.9 1 4.8 1 3.2 0 Structure of Procurem ent 0 1 1 1 0 1 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 1 1 1 0 1 1 1 1 0 1 1 1 0 0 0 1 Type of Type of Industry Buying (SIC) Situation 1 1 0 1 1 2 1 1 1 3 1 2 1 1 0 2 1 3 0 2 0 1 1 2 0 1 1 1 0 3 0 3 1 2 0 2 1 3 0 3 0 2 0 1 0 3 0 1 0 2 1 3 0 1 1 3 1 3 0 2 0 1 0 3 0 3 0 1 0 1 1 1 1 2 1 3 1 1 1 1 1 1 1 3 0 3 0 2 1 1 6.0 4.9 4.7 4.9 3.8 5.0 5.2 5.5 3.7 3.7 4.2 6.2 6.0 5.6 5.0 4.8 6.1 5.3 1 1 0 1 1 1 0 0 0 1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1 1 1 0 1 1 0 0 1 0 0 0 0 1 1 1 1 0 1 0 1 0 1 0 0 1 1 1 0 1 3 3 2 3 3 2 2 2 1 1 2 2 3 3 2 3 3 3 4.2 3.4 4.9 6.0 4.5 4.3 4.8 5.4 3.9 4.9 5.1 4.1 5.2 5.1 5.1 3.3 5.1 4.5 5.6 4.1 4.4 5.6 3.7 5.5 4.3 4.0 6.1 4.4 5.5 5.2 1 0 1 1 0 1 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 0 0 0 1 0 1 1 1 1 1 1 0 0 1 0 1 1 0 0 0 1 0 0 0 1 0 0 1 1 1 1 1 1 1 1 0 1 0 1 1 1 0 0 0 0 1 1 1 0 1 0 1 0 1 1 0 0 1 0 0 0 1 0 0 1 0 1 0 1 2 1 2 3 2 3 2 2 3 3 3 1 2 2 3 1 3 3 2 1 1 2 1 2 1 1 3 2 2 2 3.6 4.0 3.4 5.2 3.7 4.3 4.4 0 1 0 1 0 0 1 1 0 1 0 1 1 0 0 1 1 1 0 0 0 1 1 1 3 1 1 1 Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 Yield 93.58% 74.49% 74.39% 90.80% 70.28% 84.69% 57.90% 56.75% 75.82% 67.05% 96.35% 82.09% 89.18% 61.07% 61.09% 86.24% 95.91% 90.88% 92.03% 94.64% 96.70% 94.16% 97.65% 67.59% 89.88% 95.43% 72.06% 91.99% 65.20% 99.82% 60.28% 88.98% 74.85% 96.22% 78.84% 64.81% 91.46% 63.29% 60.64% 86.77% 91.44% 70.01% 69.12% 90.76% # of Employees 16 17 15 20 20 19 20 15 19 15 18 17 19 15 16 18 16 17 15 18 19 18 17 16 17 19 18 16 18 18 17 18 20 19 19 18 18 16 16 15 15 19 20 15 # of Orders 345 315 270 351 324 358 220 255 299 287 397 331 358 221 269 352 404 365 397 411 387 354 411 284 337 417 279 341 237 393 229 374 288 408 312 277 385 285 270 383 353 263 262 389 Advertising $ 483 539 500 478 522 513 455 499 479 518 497 520 527 461 474 525 547 478 456 529 528 497 548 520 501 513 523 550 532 505 540 507 496 463 544 545 520 485 533 501 516 527 450 485 Total Net Profit $ 30,850.00 $ 26,292.00 $ 24,863.00 $ 31,277.00 $ 26,218.00 $ 29,496.00 $ 20,516.00 $ 20,812.00 $ 24,665.00 $ 25,094.00 $ 32,163.00 $ 28,938.00 $ 31,061.00 $ 21,269.00 $ 22,670.00 $ 30,038.00 $ 33,403.00 $ 29,911.00 $ 31,706.00 $ 32,962.00 $ 33,333.00 $ 31,052.00 $ 34,829.00 $ 22,496.00 $ 30,606.00 $ 34,623.00 $ 24,750.00 $ 31,342.00 $ 20,967.00 $ 33,372.00 $ 20,995.00 $ 32,383.00 $ 26,766.00 $ 33,165.00 $ 25,719.00 $ 22,922.00 $ 33,597.00 $ 22,044.00 $ 20,422.00 $ 30,918.00 $ 32,545.00 $ 22,991.00 $ 23,726.00 $ 32,306.00 45 46 47 48 49 50 75.19% 93.12% 92.71% 84.97% 96.43% 63.30% 16 19 15 19 17 16 318 382 383 360 425 272 518 501 517 454 506 450 $ $ $ $ $ $ 25,839.00 33,826.00 31,940.00 28,548.00 34,573.00 20,654.00 SUMMARY OUTPUT Regression Statistics Multiple R 0.98 R Square 0.97 Adjusted R Square 0.96 Standard Error 901.84 Observations 50.00 ANOVA df Regression Residual Total Intercept Yield # of Employees # of Orders Advertising $ 4.00 45.00 49.00 SS 1,062,980,223.01 36,599,353.81 1,099,579,576.82 Coefficients (2,464.04) 23,052.73 20.90 27.73 4.62 Standard Error 2,751.33 2,816.17 79.25 6.43 4.59 MS 265,745,055.75 813,318.97 t Stat (0.90) 8.19 0.26 4.31 1.01 F Significance F 326.74 0.00 P-value Lower 95% Upper 95% 0.38 (8,005.50) 3,077.43 0.00 17,380.66 28,724.79 0.79 (138.72) 180.52 0.00 14.77 40.69 0.32 (4.63) 13.88 Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 Yield # of Orders 93.58% 345 74.49% 315 74.39% 270 90.80% 351 70.28% 324 84.69% 358 57.90% 220 56.75% 255 75.82% 299 67.05% 287 96.35% 397 82.09% 331 89.18% 358 61.07% 221 61.09% 269 86.24% 352 95.91% 404 90.88% 365 92.03% 397 94.64% 411 96.70% 387 94.16% 354 97.65% 411 67.59% 284 89.88% 337 95.43% 417 72.06% 279 91.99% 341 65.20% 237 99.82% 393 60.28% 229 88.98% 374 74.85% 288 96.22% 408 78.84% 312 64.81% 277 91.46% 385 63.29% 285 60.64% 270 86.77% 383 91.44% 353 70.01% 263 69.12% 262 90.76% 389 Total Net Profit $ 30,850.00 $ 26,292.00 $ 24,863.00 $ 31,277.00 $ 26,218.00 $ 29,496.00 $ 20,516.00 $ 20,812.00 $ 24,665.00 $ 25,094.00 $ 32,163.00 $ 28,938.00 $ 31,061.00 $ 21,269.00 $ 22,670.00 $ 30,038.00 $ 33,403.00 $ 29,911.00 $ 31,706.00 $ 32,962.00 $ 33,333.00 $ 31,052.00 $ 34,829.00 $ 22,496.00 $ 30,606.00 $ 34,623.00 $ 24,750.00 $ 31,342.00 $ 20,967.00 $ 33,372.00 $ 20,995.00 $ 32,383.00 $ 26,766.00 $ 33,165.00 $ 25,719.00 $ 22,922.00 $ 33,597.00 $ 22,044.00 $ 20,422.00 $ 30,918.00 $ 32,545.00 $ 22,991.00 $ 23,726.00 $ 32,306.00 45 46 47 48 49 50 75.19% 93.12% 92.71% 84.97% 96.43% 63.30% 318 382 383 360 425 272 $ $ $ $ $ $ 25,839.00 33,826.00 31,940.00 28,548.00 34,573.00 20,654.00 SUMMARY OUTPUT Regression Statistics Multiple R 0.98 R Square 0.97 Adjusted R Square 0.96 Standard Error 892.79 Observations 50.00 ANOVA df Regression Residual Total 2.00 47.00 49.00 SS 1,062,117,475.66 37,462,101.16 1,099,579,576.82 Intercept Yield # of Orders Coefficients 130.11 23,196.41 27.70 Standard Error 783.62 2,751.77 6.31 MS 531,058,737.83 797,065.98 t Stat 0.17 8.43 4.39 F Significance F 666.27 0.00 P-value Lower 95% 0.87 (1,446.34) 0.00 17,660.55 0.00 15.00 gnificance F Upper 95% 1,706.55 28,732.26 40.40 Total Acres 42 149 110 152 176 156 27 159 80 121 80 127 127 137 37 179 137 181 85 99 169 27 42 183 127 39 76 100 141 67 86 178 171 87 53 Tillable Acres 170 215 175 173 205 241 204 220 224 184 215 178 190 207 167 217 151 188 286 244 201 223 215 219 137 212 200 184 208 184 207 227 203 225 183 Pond (1-YES, 0-NO) 1 0 0 0 1 1 0 0 1 0 1 1 0 1 0 1 0 1 0 0 0 0 0 1 0 0 1 0 1 1 0 0 1 0 0 Milking Cows 49 50 48 48 49 69 50 60 63 45 51 48 41 57 37 61 31 52 66 67 55 64 46 52 24 59 52 50 43 37 46 58 47 46 43 Selling Price 1085151 1400854 1095138 1138518 1349662 1619233 1286390 1369171 1457636 1250047 1401943 1258047 1189372 1345573 1088905 1528205 984130 1202614 1874805 1522279 1309976 1516596 1409517 1468242 828779 1313937 1372339 1263520 1411687 1232068 1406330 1470129 1337255 1468803 1225213 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0 20.0 21.0 22.0 23.0 24.0 25.0 26.0 27.0 28.0 29.0 30.0 31.0 32.0 33.0 34.0 35.0 36.0 37.0 38.0 39.0 40.0 41.0 42.0 43.0 44.0 45.0 Delivery Speed 4.1 1.8 3.4 2.7 6.0 1.9 4.6 1.3 5.5 4.0 2.4 3.9 2.8 3.7 4.7 3.4 3.2 4.9 5.3 4.7 3.3 3.4 3.0 2.4 5.1 4.6 2.4 5.2 3.5 4.1 3.0 2.8 5.2 3.4 2.4 1.8 3.6 4.0 0.0 2.4 1.9 5.9 4.9 5.0 2.0 Price Price Level Flexibility 0.6 6.9 3.0 6.3 5.2 5.7 1.0 7.1 0.9 9.6 3.3 7.9 2.4 9.5 4.2 6.2 1.6 9.4 3.5 6.5 1.6 8.8 2.2 9.1 1.4 8.1 1.5 8.6 1.3 9.9 2.0 9.7 4.1 5.7 1.8 7.7 1.4 9.7 1.3 9.9 0.9 8.6 0.4 8.3 4.0 9.1 1.5 6.7 1.4 8.7 2.1 7.9 1.5 6.6 1.3 9.7 2.8 9.9 3.7 5.9 3.2 6.0 3.8 8.9 2.0 9.3 3.7 6.4 1.0 7.7 3.3 7.5 4.0 5.8 0.9 9.1 2.1 6.9 2.0 6.4 3.4 7.6 0.9 9.6 2.3 9.3 1.3 8.6 2.6 6.5 Manufact urer Image 4.7 6.6 6.0 5.9 7.8 4.8 6.6 5.1 4.7 6.0 4.8 4.6 3.8 5.7 6.7 4.7 5.1 4.3 6.1 6.7 4.0 2.5 7.1 4.8 4.8 5.8 4.8 6.1 3.5 5.5 5.3 6.9 5.9 5.7 3.4 4.5 5.8 5.4 5.4 4.5 4.6 7.8 4.5 4.7 3.7 Service 2.4 2.5 4.3 1.8 3.4 2.6 3.5 2.8 3.5 3.7 2.0 3.0 2.1 2.7 3.0 2.7 3.6 3.4 3.3 3.0 2.1 1.2 3.5 1.9 3.3 3.4 1.9 3.2 3.1 3.9 3.1 3.3 3.7 3.5 1.7 2.5 3.7 2.4 1.1 2.1 2.6 3.4 3.6 3.1 2.4 Salesforc e Image 2.3 4.0 2.7 2.3 4.6 1.9 4.5 2.2 3.0 3.2 2.8 2.5 1.4 3.7 2.6 1.7 2.9 1.5 3.9 2.6 1.8 1.7 3.4 2.5 2.6 2.8 2.5 3.9 1.7 3.0 3.0 3.2 2.4 3.4 1.1 2.4 2.5 2.6 2.6 2.2 2.5 4.6 1.3 2.5 1.7 Product Quality Firm Size 5.2 0 8.4 1 8.2 1 7.8 1 4.5 0 9.7 1 7.6 0 6.9 1 7.6 0 8.7 1 5.8 0 8.3 0 6.6 1 6.7 0 6.8 0 4.8 0 6.2 0 5.9 0 6.8 0 6.8 0 6.3 0 5.2 0 8.4 0 7.2 1 3.8 0 4.7 0 7.2 1 6.7 0 5.4 0 8.4 1 8.0 1 8.2 0 4.6 0 8.4 1 6.2 1 7.6 1 9.3 1 7.3 0 8.9 1 8.8 1 7.7 1 4.5 0 6.2 0 3.7 0 8.5 1 Usage Level 32.0 43.0 48.0 32.0 58.0 45.0 46.0 44.0 63.0 54.0 32.0 47.0 39.0 38.0 54.0 49.0 38.0 40.0 54.0 55.0 41.0 35.0 55.0 36.0 49.0 49.0 36.0 54.0 49.0 46.0 43.0 53.0 60.0 47.0 35.0 39.0 44.0 46.0 29.0 28.0 40.0 58.0 53.0 48.0 38.0 46.0 47.0 48.0 49.0 50.0 51.0 52.0 53.0 54.0 55.0 56.0 57.0 58.0 59.0 60.0 61.0 62.0 63.0 5.0 3.1 3.4 5.8 5.4 3.7 2.6 4.5 2.8 3.8 2.9 4.9 5.4 4.3 2.3 3.1 5.1 4.1 2.5 1.9 3.9 0.2 2.1 0.7 4.8 4.1 2.4 0.8 2.6 4.4 2.5 1.8 4.5 1.9 1.9 1.1 9.4 10.0 5.6 8.8 8.0 8.2 8.2 6.3 6.7 8.7 7.7 7.4 9.6 7.6 8.0 9.9 9.2 9.3 4.6 4.5 5.6 4.5 3.0 6.0 5.0 5.9 4.9 2.9 7.0 6.9 5.5 5.4 4.7 4.5 5.8 5.5 3.7 2.6 3.6 3.0 3.8 2.1 3.6 4.3 2.5 1.6 2.8 4.6 4.0 3.1 3.3 2.6 3.6 2.5 1.4 3.2 2.3 2.4 1.4 2.5 2.5 3.4 2.6 2.1 3.6 4.0 3.0 2.5 2.2 3.1 2.3 2.7 6.3 3.8 9.1 6.7 5.2 5.2 9.0 8.8 9.2 5.6 7.7 9.6 7.7 4.4 8.7 3.8 4.5 7.4 0 0 1 0 0 0 1 1 1 0 0 1 0 0 1 0 0 0 54.0 55.0 43.0 57.0 53.0 41.0 53.0 50.0 32.0 39.0 47.0 62.0 65.0 46.0 50.0 54.0 60.0 47.0 64.0 65.0 66.0 67.0 68.0 69.0 70.0 71.0 72.0 73.0 74.0 75.0 76.0 77.0 78.0 79.0 80.0 81.0 82.0 83.0 84.0 85.0 86.0 87.0 88.0 89.0 90.0 91.0 92.0 93.0 3.0 1.1 3.7 4.2 1.6 5.3 2.3 3.6 5.6 3.6 5.2 3.0 4.2 3.8 3.3 1.0 4.5 5.5 3.4 1.6 2.3 2.6 2.5 2.4 2.1 2.9 4.3 3.0 4.8 3.1 3.8 2.0 1.4 2.5 4.5 1.7 3.7 5.4 2.2 2.2 1.3 2.0 2.4 0.8 2.6 1.9 1.6 1.8 4.6 2.8 3.7 3.0 3.1 2.9 3.5 1.2 2.5 2.8 1.7 4.2 5.5 7.2 9.0 9.2 6.4 8.5 8.3 5.9 8.2 9.9 9.1 6.6 9.4 8.3 9.7 7.1 8.7 8.7 5.5 6.1 7.6 8.5 7.0 8.4 7.4 7.3 9.3 7.8 7.6 5.1 4.9 4.7 4.5 6.2 5.3 3.7 5.2 6.2 3.1 4.8 4.5 6.6 4.9 6.1 3.3 4.5 4.6 3.8 8.2 6.4 5.0 6.0 4.2 5.9 4.8 6.1 6.3 7.1 4.2 7.8 3.4 1.6 2.6 3.3 3.0 3.5 3.0 4.5 4.0 2.9 3.3 2.4 3.2 2.2 2.9 1.5 3.1 3.6 4.0 2.3 3.0 2.8 2.8 2.7 2.8 2.0 3.4 3.0 3.3 3.6 2.6 3.2 2.3 3.9 2.5 1.9 2.3 2.9 1.6 1.9 2.7 2.7 2.7 2.6 1.5 3.1 2.1 2.1 4.4 3.8 2.5 2.8 2.2 2.7 2.3 2.5 4.0 3.8 1.4 4.0 6.0 10.0 6.8 7.3 7.1 4.8 9.1 8.4 5.3 4.9 7.3 8.2 8.5 5.3 5.2 9.9 6.8 4.9 6.3 8.2 7.4 6.8 9.0 6.7 7.2 8.0 7.4 7.9 5.8 5.9 0 1 0 0 1 0 1 1 0 0 0 1 0 0 0 1 0 0 0 1 0 1 1 1 0 1 0 0 0 0 36.0 40.0 45.0 59.0 46.0 58.0 49.0 50.0 55.0 51.0 60.0 41.0 49.0 42.0 47.0 39.0 56.0 59.0 47.0 41.0 37.0 53.0 43.0 51.0 36.0 34.0 60.0 49.0 39.0 43.0 94.0 95.0 96.0 97.0 98.0 99.0 100.0 1.9 4.0 0.6 6.1 2.0 3.1 2.5 2.7 0.5 1.6 0.5 2.8 2.2 1.8 5.0 6.7 6.4 9.2 5.2 6.7 9.0 4.9 4.5 5.0 4.8 5.0 6.8 5.0 2.2 2.2 0.7 3.3 2.4 2.6 2.2 2.5 2.1 2.1 2.8 2.7 2.9 3.0 8.2 5.0 8.4 7.1 8.4 8.4 6.0 1 0 1 0 1 1 0 36.0 31.0 25.0 60.0 38.0 42.0 33.0 Satisfactio Specificati n Level on Buying 4.2 1 4.3 0 5.2 0 3.9 0 6.8 1 4.4 0 5.8 1 4.3 0 5.4 1 5.4 0 4.3 1 5.0 1 4.4 0 5.0 1 5.9 1 4.7 1 4.4 1 5.6 1 5.9 1 6.0 1 4.5 1 3.3 1 5.2 1 3.7 0 4.9 1 5.9 1 3.7 0 5.8 1 5.4 1 5.1 0 3.3 0 5.0 1 6.1 1 3.8 0 4.1 0 3.6 0 4.8 0 5.1 1 3.9 0 3.3 0 3.7 0 6.7 1 5.9 1 4.8 1 3.2 0 Structure of Procurem ent 0 1 1 1 0 1 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 1 1 1 0 1 1 1 1 0 1 1 1 0 0 0 1 Type of Type of Industry Buying (SIC) Situation 1 1 0 1 1 2 1 1 1 3 1 2 1 1 0 2 1 3 0 2 0 1 1 2 0 1 1 1 0 3 0 3 1 2 0 2 1 3 0 3 0 2 0 1 0 3 0 1 0 2 1 3 0 1 1 3 1 3 0 2 0 1 0 3 0 3 0 1 0 1 1 1 1 2 1 3 1 1 1 1 1 1 1 3 0 3 0 2 1 1 6.0 4.9 4.7 4.9 3.8 5.0 5.2 5.5 3.7 3.7 4.2 6.2 6.0 5.6 5.0 4.8 6.1 5.3 1 1 0 1 1 1 0 0 0 1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1 1 1 0 1 1 0 0 1 0 0 0 0 1 1 1 1 0 1 0 1 0 1 0 0 1 1 1 0 1 3 3 2 3 3 2 2 2 1 1 2 2 3 3 2 3 3 3 4.2 3.4 4.9 6.0 4.5 4.3 4.8 5.4 3.9 4.9 5.1 4.1 5.2 5.1 5.1 3.3 5.1 4.5 5.6 4.1 4.4 5.6 3.7 5.5 4.3 4.0 6.1 4.4 5.5 5.2 1 0 1 1 0 1 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 0 0 0 1 0 1 1 1 1 1 1 0 0 1 0 1 1 0 0 0 1 0 0 0 1 0 0 1 1 1 1 1 1 1 1 0 1 0 1 1 1 0 0 0 0 1 1 1 0 1 0 1 0 1 1 0 0 1 0 0 0 1 0 0 1 0 1 0 1 2 1 2 3 2 3 2 2 3 3 3 1 2 2 3 1 3 3 2 1 1 2 1 2 1 1 3 2 2 2 3.6 4.0 3.4 5.2 3.7 4.3 4.4 0 1 0 1 0 0 1 1 0 1 0 1 1 0 0 1 1 1 0 0 0 1 1 1 3 1 1 1 Assignment 3 - 20 Points Data found in Assignment 3 Data excel file. Deliverable 1 (5 points): A manager is under pressure to increase daily net profits. Data has been collected for the past 50 days indicating yield, number of employees, number of orders, advertising expense, and net profits. 1. Perform regression analysis to determine how the four variables affect net profits. Write out the equation of the final model. Be sure to show the regression tables and decisions made to reach the final model. Use Alpha = .01 to determine variables to remove from model. Solution: Below is the summary output when I used all the four independent variable in the model: Model 1 SUMMARY OUTPUT Regression Statistics Multiple R 0.98 R Square 0.97 Adjusted R Square 0.96 Standard Error 901.84 Observations 50.00 ANOVA df SS MS F 326.74 Regression 4.00 1,062,980,223.01 265,745,055.75 Residual 45.00 36,599,353.81 813,318.97 Total 49.00 Coefficient 1,099,579,576.82 Standard Error t Stat P-value Significanc eF 0.00 Lower 95% Upper s 95% Intercept (2,464.04) 2,751.33 (0.90) 0.38 (8,005.50) Yield 23,052.73 2,816.17 8.19 0.00 17,380.66 # of Employees 20.90 79.25 0.26 0.79 (138.72) # of Orders 27.73 6.43 4.31 0.00 14.77 Advertising $ 4.62 4.59 1.01 0.32 (4.63) 3,077.43 28,724.79 180.52 40.69 13.88 On the basis of the p-value of the slope co-efficient of # of employees and Advertising $, I can conclude that these two independent variable are not relevant to the model. Below is the summary of my next model after removing # of employees and Advertising $, Model 2 SUMMARY OUTPUT Regression Statistics Multiple R 0.98 R Square Adjusted R Square 0.97 Standard Error 892.79 Observations 50.00 0.96 ANOVA df Regression 2.00 SS MS F 1,062,117,475.66 531,058,737.8 3 666.27 Significanc eF 0.00 Residual 47.00 37,462,101.16 Total 49.00 Coefficient s 1,099,579,576.82 Intercept 130.11 783.62 Yield 23,196.41 # of Orders 27.70 797,065.98 Standard Error t Stat P-value Lower 95% 0.17 0.87 (1,446.34) 2,751.77 8.43 0.00 17,660.55 6.31 4.39 0.00 15.00 Upper 95% 1,706.55 28,732.26 40.40 In the above model summary, we can see that the co-efficient of yield and # of orders is significant at 1% level of significance. The intercept term is coming insignificant in the model, but still I want to use this term in the model, otherwise the interpretation of the co-efficient will change and I can't validate the other assumptions (no-autocorrelation) in the model. Thus the final equation of the model would be: Total Net Profit = 130.11 + 23,196.41xYield + 27.70x(# of orders) 2. Would you use this model? Why or why not? Solution I would use the Model 2 for my analysis. This is because in this model, both the variable Yield and # of order are coming significant. The coefficient of determination value is around 97% which means that around 97% of the variation in the total net profit has been explained with the help of yield and # of orders variables. 3. Given: Yield = 85.76%, number of employees = 21, number of orders = 355, and advertising expense = $505.00, calculate predicted net profits. Solution Using the model 1, the predicted net profit would be: Predicted net profit = -2464.03 + 23052.73x85.76 + 20.90x21 + 27.73x355 + 4.62x505 ------------------------- Model (1) = $ 1,987,154.24 But as per suggestion, manager have to use Model 2, thus the predicted net profits would be: Predicted net profit = 130.11 + 23,196.41x85.76 + 27.70x355--------------------------------------Model (2) = $ 1,999,287.73 4. What advice would you give the manager? (Be brief, this is not an essay.) Solution I checked both Model 1 and Model 2 and found that model 2 which have only 2 independent variable yield and # of orders are significant. It is able to explain around 97% of the variation in total net profits. I would recommend you to use Model 2 for predicting total net profits. Deliverable 2 (5 points): A local community bank is interested in predicting the selling price of local dairy farms. The following data has been collected for 35 farms that have sold in the past few years: total acres, tillable acres, whether the farm has a pond, how many milking cows, and the selling price. Use Alpha = 0.05 to determine variables to remove from model. 1. Can the variables be used to predict the selling price of farms? 2. Write out the equation of the final model. Be sure to show the regression tables and decisions made to reach the final model. 3. A farmer owns a farm without a pond and is planning on selling the farm. A backhoe owner is willing to dig a pond at a cost of $49,995. Should the farmer hire the backhoe owner to dig the pond? Support you answer. Deliverable 3 (10 points): We wish to determine the impact of Specification Buying, X11, on Satisfaction Level, X10. To do so we will split the Hatco data file into two separate data sets based on the Specification Buying, X11. This variable has two categories: 1=employs total value analysis approach, evaluating each purchase separately; 0 = use of specification buying. Sort the entire Hatco data set based on Specification Buying. This will create two separate groups of records. Those records with X11 = 0 and those records with X11 = 1. Treat these as two distinct data sets. For the 2 data sets, X11 = 0 and X11 = 1, perform regression analysis on Satisfaction Level X10 as a function of the first seven variables (Delivery Speed, Price Level, Price Flexibility, Manufacturer Image, Service, Salesforce Image, Product Quality). Use Alpha = .01 to determine variables to remove from model. For X11 = 0 and X11 = 1: Compare the two models and explain the differences between the two models. From a business perspective, why do they differ? Instructions Submit your assignment using the drop box. The file name should follow the format: Your-last-name Assgn3.xlxs Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 Yield 93.58% 74.49% 74.39% 90.80% 70.28% 84.69% 57.90% 56.75% 75.82% 67.05% 96.35% 82.09% 89.18% 61.07% 61.09% 86.24% 95.91% 90.88% 92.03% 94.64% 96.70% 94.16% 97.65% 67.59% 89.88% 95.43% 72.06% 91.99% 65.20% 99.82% 60.28% 88.98% 74.85% 96.22% 78.84% 64.81% 91.46% 63.29% 60.64% 86.77% 91.44% 70.01% 69.12% 90.76% # of Employees 16 17 15 20 20 19 20 15 19 15 18 17 19 15 16 18 16 17 15 18 19 18 17 16 17 19 18 16 18 18 17 18 20 19 19 18 18 16 16 15 15 19 20 15 # of Orders 345 315 270 351 324 358 220 255 299 287 397 331 358 221 269 352 404 365 397 411 387 354 411 284 337 417 279 341 237 393 229 374 288 408 312 277 385 285 270 383 353 263 262 389 Advertising $ 483 539 500 478 522 513 455 499 479 518 497 520 527 461 474 525 547 478 456 529 528 497 548 520 501 513 523 550 532 505 540 507 496 463 544 545 520 485 533 501 516 527 450 485 Total Net Profit $ 30,850.00 $ 26,292.00 $ 24,863.00 $ 31,277.00 $ 26,218.00 $ 29,496.00 $ 20,516.00 $ 20,812.00 $ 24,665.00 $ 25,094.00 $ 32,163.00 $ 28,938.00 $ 31,061.00 $ 21,269.00 $ 22,670.00 $ 30,038.00 $ 33,403.00 $ 29,911.00 $ 31,706.00 $ 32,962.00 $ 33,333.00 $ 31,052.00 $ 34,829.00 $ 22,496.00 $ 30,606.00 $ 34,623.00 $ 24,750.00 $ 31,342.00 $ 20,967.00 $ 33,372.00 $ 20,995.00 $ 32,383.00 $ 26,766.00 $ 33,165.00 $ 25,719.00 $ 22,922.00 $ 33,597.00 $ 22,044.00 $ 20,422.00 $ 30,918.00 $ 32,545.00 $ 22,991.00 $ 23,726.00 $ 32,306.00 45 46 47 48 49 50 75.19% 93.12% 92.71% 84.97% 96.43% 63.30% 16 19 15 19 17 16 318 382 383 360 425 272 518 501 517 454 506 450 $ $ $ $ $ $ 25,839.00 33,826.00 31,940.00 28,548.00 34,573.00 20,654.00 SUMMARY OUTPUT Regression Statistics Multiple R 0.98 R Square 0.97 Adjusted R Square 0.96 Standard Error 901.84 Observations 50.00 ANOVA df Regression Residual Total Intercept Yield # of Employees # of Orders Advertising $ 4.00 45.00 49.00 SS 1,062,980,223.01 36,599,353.81 1,099,579,576.82 Coefficients (2,464.04) 23,052.73 20.90 27.73 4.62 Standard Error 2,751.33 2,816.17 79.25 6.43 4.59 MS 265,745,055.75 813,318.97 t Stat F Significance F 326.74 0.00 P-value Lower 95% Upper 95% (0.90) 0.38 (8,005.50) 3,077.43 8.19 0.00 17,380.66 28,724.79 0.26 0.79 (138.72) 180.52 4.31 0.00 14.77 40.69 1.01 0.32 (4.63) 13.88 Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 Yield # of Orders 93.58% 345 74.49% 315 74.39% 270 90.80% 351 70.28% 324 84.69% 358 57.90% 220 56.75% 255 75.82% 299 67.05% 287 96.35% 397 82.09% 331 89.18% 358 61.07% 221 61.09% 269 86.24% 352 95.91% 404 90.88% 365 92.03% 397 94.64% 411 96.70% 387 94.16% 354 97.65% 411 67.59% 284 89.88% 337 95.43% 417 72.06% 279 91.99% 341 65.20% 237 99.82% 393 60.28% 229 88.98% 374 74.85% 288 96.22% 408 78.84% 312 64.81% 277 91.46% 385 63.29% 285 60.64% 270 86.77% 383 91.44% 353 70.01% 263 69.12% 262 90.76% 389 45 46 47 48 49 50 75.19% 93.12% 92.71% 84.97% 96.43% 63.30% 318 382 383 360 425 272 Total Net Profit $ 30,850.00 $ 26,292.00 $ 24,863.00 $ 31,277.00 $ 26,218.00 $ 29,496.00 $ 20,516.00 $ 20,812.00 $ 24,665.00 $ 25,094.00 $ 32,163.00 $ 28,938.00 $ 31,061.00 $ 21,269.00 $ 22,670.00 $ 30,038.00 $ 33,403.00 $ 29,911.00 $ 31,706.00 $ 32,962.00 $ 33,333.00 $ 31,052.00 $ 34,829.00 $ 22,496.00 $ 30,606.00 $ 34,623.00 $ 24,750.00 $ 31,342.00 $ 20,967.00 $ 33,372.00 $ 20,995.00 $ 32,383.00 $ 26,766.00 $ 33,165.00 $ 25,719.00 $ 22,922.00 $ 33,597.00 $ 22,044.00 $ 20,422.00 $ 30,918.00 $ 32,545.00 $ 22,991.00 $ 23,726.00 $ 32,306.00 SUMMARY OUTPUT Regression Statistics Multiple R 0.98 R Square 0.97 Adjusted R Square 0.96 Standard Error 892.79 Observations 50.00 ANOVA df Regression Residual Total 2.00 47.00 49.00 SS 1,062,117,475.66 37,462,101.16 1,099,579,576.82 Intercept Yield # of Orders Coefficients 130.11 23,196.41 27.70 Standard Error 783.62 2,751.77 6.31 MS 531,058,737.83 797,065.98 t Stat 0.17 8.43 4.39 $ $ $ $ $ $ 25,839.00 33,826.00 31,940.00 28,548.00 34,573.00 20,654.00 F Significance F 666.27 0.00 P-value Lower 95% Upper 95% 0.87 (1,446.34) 1,706.55 0.00 17,660.55 28,732.26 0.00 15.00 40.40 Total Acres 42 149 110 152 176 156 27 159 80 121 80 127 127 137 37 179 137 181 85 99 169 27 42 183 127 39 76 100 141 67 86 178 171 87 53 Tillable Acres 170 215 175 173 205 241 204 220 224 184 215 178 190 207 167 217 151 188 286 244 201 223 215 219 137 212 200 184 208 184 207 227 203 225 183 Pond (1-YES, 0-NO) 1 0 0 0 1 1 0 0 1 0 1 1 0 1 0 1 0 1 0 0 0 0 0 1 0 0 1 0 1 1 0 0 1 0 0 Milking Cows 49 50 48 48 49 69 50 60 63 45 51 48 41 57 37 61 31 52 66 67 55 64 46 52 24 59 52 50 43 37 46 58 47 46 43 Selling Price 1085151 1400854 1095138 1138518 1349662 1619233 1286390 1369171 1457636 1250047 1401943 1258047 1189372 1345573 1088905 1528205 984130 1202614 1874805 1522279 1309976 1516596 1409517 1468242 828779 1313937 1372339 1263520 1411687 1232068 1406330 1470129 1337255 1468803 1225213 SUMMARY OUTPUT Regression Statistics Multiple R 0.98 R Square 0.95 Adjusted R Square 0.94 Standard Error 45,379.88 Observations 35.00 ANOVA df Regression Residual Total Intercept Total Acres Tillable Acres Pond (1-YES, 0-NO) Milking Cows 4.00 30.00 34.00 SS 1,192,838,269,902.19 61,780,004,540.79 1,254,618,274,442.97 Coefficients (24,090.57) (1.95) 6,698.73 36,954.99 (354.27) Standard Error 59,897.03 161.25 476.15 16,501.22 1,357.60 MS 298,209,567,475.55 2,059,333,484.69 t Stat (0.40) (0.01) 14.07 2.24 (0.26) F 144.81 Significance F 0.00 P-value 0.69 0.99 0.00 0.03 0.80 Lower 95% (146,416.63) (331.27) 5,726.29 3,255.00 (3,126.85) Upper 95% 98,235.49 327.37 7,671.17 70,654.97 2,418.32 Tillable Acres Pond (1-YES, 0-NO) Selling Price 170 1 1085151 215 0 1400854 175 0 1095138 173 0 1138518 205 1 1349662 241 1 1619233 204 0 1286390 220 0 1369171 224 1 1457636 184 0 1250047 215 1 1401943 178 1 1258047 190 0 1189372 207 1 1345573 167 0 1088905 217 1 1528205 151 0 984130 188 1 1202614 286 0 1874805 244 0 1522279 201 0 1309976 223 0 1516596 215 0 1409517 219 1 1468242 137 0 828779 212 0 1313937 200 1 1372339 184 0 1263520 208 1 1411687 184 1 1232068 207 0 1406330 227 0 1470129 203 1 1337255 225 0 1468803 183 0 1225213 SUMMARY OUTPUT Regression Statistics Multiple R 0.98 R Square 0.95 Adjusted R Square 0.95 Standard Error 43,989.12 Observations 35.00 ANOVA df Regression Residual Total Intercept Tillable Acres Pond (1-YES, 0-NO) 2.00 32.00 34.00 SS 1,192,696,910,028.26 61,921,364,414.72 1,254,618,274,442.97 Coefficients (21,404.93) 6,597.66 36,186.99 Standard Error 54,903.25 268.82 15,200.83 MS 596,348,455,014.13 1,935,042,637.96 t Stat F 308.18 P-value (0.39) 0.70 24.54 0.00 2.38 0.02 Significance F 0.00 Lower 95% Upper 95% (133,239.20) 90,429.34 6,050.10 7,145.22 5,223.92 67,150.07 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0 20.0 21.0 22.0 23.0 24.0 25.0 26.0 27.0 28.0 29.0 30.0 31.0 32.0 33.0 34.0 35.0 36.0 37.0 38.0 39.0 40.0 41.0 42.0 43.0 44.0 45.0 Delivery Speed 4.1 1.8 3.4 2.7 6.0 1.9 4.6 1.3 5.5 4.0 2.4 3.9 2.8 3.7 4.7 3.4 3.2 4.9 5.3 4.7 3.3 3.4 3.0 2.4 5.1 4.6 2.4 5.2 3.5 4.1 3.0 2.8 5.2 3.4 2.4 1.8 3.6 4.0 0.0 2.4 1.9 5.9 4.9 5.0 2.0 Price Price Level Flexibility 0.6 6.9 3.0 6.3 5.2 5.7 1.0 7.1 0.9 9.6 3.3 7.9 2.4 9.5 4.2 6.2 1.6 9.4 3.5 6.5 1.6 8.8 2.2 9.1 1.4 8.1 1.5 8.6 1.3 9.9 2.0 9.7 4.1 5.7 1.8 7.7 1.4 9.7 1.3 9.9 0.9 8.6 0.4 8.3 4.0 9.1 1.5 6.7 1.4 8.7 2.1 7.9 1.5 6.6 1.3 9.7 2.8 9.9 3.7 5.9 3.2 6.0 3.8 8.9 2.0 9.3 3.7 6.4 1.0 7.7 3.3 7.5 4.0 5.8 0.9 9.1 2.1 6.9 2.0 6.4 3.4 7.6 0.9 9.6 2.3 9.3 1.3 8.6 2.6 6.5 Manufact urer Image 4.7 6.6 6.0 5.9 7.8 4.8 6.6 5.1 4.7 6.0 4.8 4.6 3.8 5.7 6.7 4.7 5.1 4.3 6.1 6.7 4.0 2.5 7.1 4.8 4.8 5.8 4.8 6.1 3.5 5.5 5.3 6.9 5.9 5.7 3.4 4.5 5.8 5.4 5.4 4.5 4.6 7.8 4.5 4.7 3.7 Service 2.4 2.5 4.3 1.8 3.4 2.6 3.5 2.8 3.5 3.7 2.0 3.0 2.1 2.7 3.0 2.7 3.6 3.4 3.3 3.0 2.1 1.2 3.5 1.9 3.3 3.4 1.9 3.2 3.1 3.9 3.1 3.3 3.7 3.5 1.7 2.5 3.7 2.4 1.1 2.1 2.6 3.4 3.6 3.1 2.4 Salesforc e Image 2.3 4.0 2.7 2.3 4.6 1.9 4.5 2.2 3.0 3.2 2.8 2.5 1.4 3.7 2.6 1.7 2.9 1.5 3.9 2.6 1.8 1.7 3.4 2.5 2.6 2.8 2.5 3.9 1.7 3.0 3.0 3.2 2.4 3.4 1.1 2.4 2.5 2.6 2.6 2.2 2.5 4.6 1.3 2.5 1.7 Product Quality Firm Size 5.2 0 8.4 1 8.2 1 7.8 1 4.5 0 9.7 1 7.6 0 6.9 1 7.6 0 8.7 1 5.8 0 8.3 0 6.6 1 6.7 0 6.8 0 4.8 0 6.2 0 5.9 0 6.8 0 6.8 0 6.3 0 5.2 0 8.4 0 7.2 1 3.8 0 4.7 0 7.2 1 6.7 0 5.4 0 8.4 1 8.0 1 8.2 0 4.6 0 8.4 1 6.2 1 7.6 1 9.3 1 7.3 0 8.9 1 8.8 1 7.7 1 4.5 0 6.2 0 3.7 0 8.5 1 Usage Level 32.0 43.0 48.0 32.0 58.0 45.0 46.0 44.0 63.0 54.0 32.0 47.0 39.0 38.0 54.0 49.0 38.0 40.0 54.0 55.0 41.0 35.0 55.0 36.0 49.0 49.0 36.0 54.0 49.0 46.0 43.0 53.0 60.0 47.0 35.0 39.0 44.0 46.0 29.0 28.0 40.0 58.0 53.0 48.0 38.0 46.0 47.0 48.0 49.0 50.0 51.0 52.0 53.0 54.0 55.0 56.0 57.0 58.0 59.0 60.0 61.0 62.0 63.0 5.0 3.1 3.4 5.8 5.4 3.7 2.6 4.5 2.8 3.8 2.9 4.9 5.4 4.3 2.3 3.1 5.1 4.1 2.5 1.9 3.9 0.2 2.1 0.7 4.8 4.1 2.4 0.8 2.6 4.4 2.5 1.8 4.5 1.9 1.9 1.1 9.4 10.0 5.6 8.8 8.0 8.2 8.2 6.3 6.7 8.7 7.7 7.4 9.6 7.6 8.0 9.9 9.2 9.3 4.6 4.5 5.6 4.5 3.0 6.0 5.0 5.9 4.9 2.9 7.0 6.9 5.5 5.4 4.7 4.5 5.8 5.5 3.7 2.6 3.6 3.0 3.8 2.1 3.6 4.3 2.5 1.6 2.8 4.6 4.0 3.1 3.3 2.6 3.6 2.5 1.4 3.2 2.3 2.4 1.4 2.5 2.5 3.4 2.6 2.1 3.6 4.0 3.0 2.5 2.2 3.1 2.3 2.7 6.3 3.8 9.1 6.7 5.2 5.2 9.0 8.8 9.2 5.6 7.7 9.6 7.7 4.4 8.7 3.8 4.5 7.4 0 0 1 0 0 0 1 1 1 0 0 1 0 0 1 0 0 0 54.0 55.0 43.0 57.0 53.0 41.0 53.0 50.0 32.0 39.0 47.0 62.0 65.0 46.0 50.0 54.0 60.0 47.0 64.0 65.0 66.0 67.0 68.0 69.0 70.0 71.0 72.0 73.0 74.0 75.0 76.0 77.0 78.0 79.0 80.0 81.0 82.0 83.0 84.0 85.0 86.0 87.0 88.0 89.0 90.0 91.0 92.0 93.0 3.0 1.1 3.7 4.2 1.6 5.3 2.3 3.6 5.6 3.6 5.2 3.0 4.2 3.8 3.3 1.0 4.5 5.5 3.4 1.6 2.3 2.6 2.5 2.4 2.1 2.9 4.3 3.0 4.8 3.1 3.8 2.0 1.4 2.5 4.5 1.7 3.7 5.4 2.2 2.2 1.3 2.0 2.4 0.8 2.6 1.9 1.6 1.8 4.6 2.8 3.7 3.0 3.1 2.9 3.5 1.2 2.5 2.8 1.7 4.2 5.5 7.2 9.0 9.2 6.4 8.5 8.3 5.9 8.2 9.9 9.1 6.6 9.4 8.3 9.7 7.1 8.7 8.7 5.5 6.1 7.6 8.5 7.0 8.4 7.4 7.3 9.3 7.8 7.6 5.1 4.9 4.7 4.5 6.2 5.3 3.7 5.2 6.2 3.1 4.8 4.5 6.6 4.9 6.1 3.3 4.5 4.6 3.8 8.2 6.4 5.0 6.0 4.2 5.9 4.8 6.1 6.3 7.1 4.2 7.8 3.4 1.6 2.6 3.3 3.0 3.5 3.0 4.5 4.0 2.9 3.3 2.4 3.2 2.2 2.9 1.5 3.1 3.6 4.0 2.3 3.0 2.8 2.8 2.7 2.8 2.0 3.4 3.0 3.3 3.6 2.6 3.2 2.3 3.9 2.5 1.9 2.3 2.9 1.6 1.9 2.7 2.7 2.7 2.6 1.5 3.1 2.1 2.1 4.4 3.8 2.5 2.8 2.2 2.7 2.3 2.5 4.0 3.8 1.4 4.0 6.0 10.0 6.8 7.3 7.1 4.8 9.1 8.4 5.3 4.9 7.3 8.2 8.5 5.3 5.2 9.9 6.8 4.9 6.3 8.2 7.4 6.8 9.0 6.7 7.2 8.0 7.4 7.9 5.8 5.9 0 1 0 0 1 0 1 1 0 0 0 1 0 0 0 1 0 0 0 1 0 1 1 1 0 1 0 0 0 0 36.0 40.0 45.0 59.0 46.0 58.0 49.0 50.0 55.0 51.0 60.0 41.0 49.0 42.0 47.0 39.0 56.0 59.0 47.0 41.0 37.0 53.0 43.0 51.0 36.0 34.0 60.0 49.0 39.0 43.0 94.0 95.0 96.0 97.0 98.0 99.0 100.0 1.9 4.0 0.6 6.1 2.0 3.1 2.5 2.7 0.5 1.6 0.5 2.8 2.2 1.8 5.0 6.7 6.4 9.2 5.2 6.7 9.0 4.9 4.5 5.0 4.8 5.0 6.8 5.0 2.2 2.2 0.7 3.3 2.4 2.6 2.2 2.5 2.1 2.1 2.8 2.7 2.9 3.0 8.2 5.0 8.4 7.1 8.4 8.4 6.0 1 0 1 0 1 1 0 36.0 31.0 25.0 60.0 38.0 42.0 33.0 Satisfactio Specificati n Level on Buying 4.2 1 4.3 0 5.2 0 3.9 0 6.8 1 4.4 0 5.8 1 4.3 0 5.4 1 5.4 0 4.3 1 5.0 1 4.4 0 5.0 1 5.9 1 4.7 1 4.4 1 5.6 1 5.9 1 6.0 1 4.5 1 3.3 1 5.2 1 3.7 0 4.9 1 5.9 1 3.7 0 5.8 1 5.4 1 5.1 0 3.3 0 5.0 1 6.1 1 3.8 0 4.1 0 3.6 0 4.8 0 5.1 1 3.9 0 3.3 0 3.7 0 6.7 1 5.9 1 4.8 1 3.2 0 Structure of Procurem ent 0 1 1 1 0 1 0 1 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 1 1 1 0 1 1 1 1 0 1 1 1 0 0 0 1 Type of Type of Industry Buying (SIC) Situation 1 1 0 1 1 2 1 1 1 3 1 2 1 1 0 2 1 3 0 2 0 1 1 2 0 1 1 1 0 3 0 3 1 2 0 2 1 3 0 3 0 2 0 1 0 3 0 1 0 2 1 3 0 1 1 3 1 3 0 2 0 1 0 3 0 3 0 1 0 1 1 1 1 2 1 3 1 1 1 1 1 1 1 3 0 3 0 2 1 1 6.0 4.9 4.7 4.9 3.8 5.0 5.2 5.5 3.7 3.7 4.2 6.2 6.0 5.6 5.0 4.8 6.1 5.3 1 1 0 1 1 1 0 0 0 1 1 0 1 1 0 1 1 1 0 0 1 0 0 0 1 1 1 0 1 1 0 0 1 0 0 0 0 1 1 1 1 0 1 0 1 0 1 0 0 1 1 1 0 1 3 3 2 3 3 2 2 2 1 1 2 2 3 3 2 3 3 3 4.2 3.4 4.9 6.0 4.5 4.3 4.8 5.4 3.9 4.9 5.1 4.1 5.2 5.1 5.1 3.3 5.1 4.5 5.6 4.1 4.4 5.6 3.7 5.5 4.3 4.0 6.1 4.4 5.5 5.2 1 0 1 1 0 1 0 0 1 1 1 0 1 1 1 0 1 1 1 0 1 0 0 0 1 0 1 1 1 1 1 1 0 0 1 0 1 1 0 0 0 1 0 0 0 1 0 0 1 1 1 1 1 1 1 1 0 1 0 1 1 1 0 0 0 0 1 1 1 0 1 0 1 0 1 1 0 0 1 0 0 0 1 0 0 1 0 1 0 1 2 1 2 3 2 3 2 2 3 3 3 1 2 2 3 1 3 3 2 1 1 2 1 2 1 1 3 2 2 2 3.6 4.0 3.4 5.2 3.7 4.3 4.4 0 1 0 1 0 0 1 1 0 1 0 1 1 0 0 1 1 1 0 0 0 1 1 1 3 1 1 1 Assignment 3 - 20 Points Data found in Assignment 3 Data excel file. Deliverable 1 (5 points): A manager is under pressure to increase daily net profits. Data has been collected for the past 50 days indicating yield, number of employees, number of orders, advertising expense, and net profits. 1. Perform regression analysis to determine how the four variables affect net profits. Write out the equation of the final model. Be sure to show the regression tables and decisions made to reach the final model. Use Alpha = .01 to determine variables to remove from model. Solution: Below is the summary output when I used all the four independent variable in the model: Model 1 SUMMARY OUTPUT Regression Statistics Multiple R 0.98 R Square 0.97 Adjusted R Square 0.96 Standard Error 901.84 Observations 50.00 ANOVA df SS MS F 326.74 Regression 4.00 1,062,980,223.01 265,745,055.75 Residual 45.00 36,599,353.81 813,318.97 Total 49.00 Coefficient 1,099,579,576.82 Standard Error t Stat P-value Significanc eF 0.00 Lower 95% Upper s 95% Intercept (2,464.04) 2,751.33 (0.90) 0.38 (8,005.50) Yield 23,052.73 2,816.17 8.19 0.00 17,380.66 # of Employees 20.90 79.25 0.26 0.79 (138.72) # of Orders 27.73 6.43 4.31 0.00 14.77 Advertising $ 4.62 4.59 1.01 0.32 (4.63) 3,077.43 28,724.79 180.52 40.69 13.88 On the basis of the p-value of the slope co-efficient of # of employees and Advertising $, I can conclude that these two independent variable are not relevant to the model. Below is the summary of my next model after removing # of employees and Advertising $, Model 2 SUMMARY OUTPUT Regression Statistics Multiple R 0.98 R Square Adjusted R Square 0.97 Standard Error 892.79 Observations 50.00 0.96 ANOVA df Regression 2.00 SS MS F 1,062,117,475.66 531,058,737.8 3 666.27 Significanc eF 0.00 Residual 47.00 37,462,101.16 Total 49.00 Coefficient s 1,099,579,576.82 Intercept 130.11 783.62 Yield 23,196.41 # of Orders 27.70 797,065.98 Standard Error t Stat P-value Lower 95% 0.17 0.87 (1,446.34) 2,751.77 8.43 0.00 17,660.55 6.31 4.39 0.00 15.00 Upper 95% 1,706.55 28,732.26 40.40 In the above model summary, we can see that the co-efficient of yield and # of orders is significant at 1% level of significance. The intercept term is coming insignificant in the model, but still I want to use this term in the model, otherwise the interpretation of the co-efficient will change and I can't validate the other assumptions (no-autocorrelation) in the model. Thus the final equation of the model would be: Total Net Profit = 130.11 + 23,196.41xYield + 27.70x(# of orders) 2. Would you use this model? Why or why not? Solution I would use the Model 2 for my analysis. This is because in this model, both the variable Yield and # of order are coming significant. The coefficient of determination value is around 97% which means that around 97% of the variation in the total net profit has been explained with the help of yield and # of orders variables. 3. Given: Yield = 85.76%, number of employees = 21, number of orders = 355, and advertising expense = $505.00, calculate predicted net profits. Solution Using the model 1, the predicted net profit would be: Predicted net profit = -2464.03 + 23052.73x85.76 + 20.90x21 + 27.73x355 + 4.62x505 ------------------------- Model (1) = $ 1,987,154.24 But as per suggestion, manager have to use Model 2, thus the predicted net profits would be: Predicted net profit = 130.11 + 23,196.41x85.76 + 27.70x355--------------------------------------Model (2) = $ 1,999,287.73 4. What advice would you give the manager? (Be brief, this is not an essay.) Solution I checked both Model 1 and Model 2 and found that model 2 which have only 2 independent variable yield and # of orders are significant. It is able to explain around 97% of the variation in total net profits. I would recommend you to use Model 2 for predicting total net profits. Deliverable 2 (5 points): A local community bank is interested in predicting the selling price of local dairy farms. The following data has been collected for 35 farms that have sold in the past few years: total acres, tillable acres, whether the farm has a pond, how many milking cows, and the selling price. Use Alpha = 0.05 to determine variables to remove from model. 1. Can the variables be used to predict the selling price of farms? Solution Yes, all the variables are related to dairy farms subject and can be helpful to predict the selling price of farms. 2. Write out the equation of the final model. Be sure to show the regression tables and decisions made to reach the final model. Solution Below is the my Model 1 summary in which I used all the four variables Model 1 SUMMARY OUTPUT Regression Statistics Multiple R 0.98 R Square 0.95 Adjusted R Square 0.94 Standard Error 45,379.88 Observations 35.00 ANOVA df SS MS F Regression 4.00 1,192,838,269,902.19 298,209,567,475.55 Residual 30.00 61,780,004,540.79 2,059,333,484.69 Total 34.00 Coefficients 1,254,618,274,442.97 Standard Error Intercept (24,090.57) 59,897.03 (0.40) 0.69 (146,416.63) 98,235.49 Total Acres (1.95) 161.25 (0.01) 0.99 (331.27) 327.37 Tillable Acres 6,698.73 476.15 14.07 0.00 5,726.29 7,671.17 Pond (1-YES, 0-NO) 36,954.99 16,501.22 2.24 0.03 3,255.00 70,654.97 Milking Cows (354.27) 1,357.60 (0.26) 0.80 (3,126.85) 2,418.32 t Stat 144.81 Significance F P-value 0.00 Lower 95% Upper 95% In the model1 summary, I can see that the variable Total Acres and milking cows' co-efficient value is not significant. The p-value of these coefficient is greater than 0.05 which is the critical value for checking the parameter significance. Thus, I have decided to use only Tillable acres and Pond variable to predict the selling price of local dairy farms. And below is the model 2 summary in which I have used only 2 independent variables e.g. tillable acres and pond. SUMMARY OUTPUT Regression Statistics Multiple R 0.98 R Square 0.95 Adjusted R Square 0.95 Standard Error 43,989.12 Observations 35.00 ANOVA df Significanc eF SS MS F 308.18 0.00 P-value Lower 95% Upper 95% Regression 2.00 1,192,696,910,028.26 596,348,455,014.13 Residual 32.00 61,921,364,414.72 1,935,042,637.96 Total 34.00 Coefficient s 1,254,618,274,442.97 Standard Error t Stat Intercept (21,404.93 ) 54,903.25 (0.39) 0.70 (133,239.20 ) 90,429.34 Tillable Acres 6,597.66 268.82 24.54 0.00 6,050.10 7,145.22 Pond (1-YES, 0-NO) 36,186.99 15,200.83 2.38 0.02 5,223.92 67,150.07 From the model 2 summary, we can see that the co-efficient of both tillable acres and pond are significant at 5% level of significance and the overall model co-efficient of determination is also around 95% which is equal to the model 1 co-efficient of determination. Thus, Model 2 would be my final model and below is the equation of the same: Selling price of local dairy farms = -21,404.93 + 6597.66xTillable acres + 36186.99 x Pond (1-YES, 0-NO) 3. A farmer owns a farm without a pond and is planning on selling the farm. A backhoe owner is willing to dig a pond at a cost of $49,995. Should the farmer hire the backhoe owner to dig the pond? Support you answer. Solution If you would check the value of Pond co-efficient, it is coming 36,186.99 which means that compare to without pond the value of farm will increase by $36,186.99 if the farm have a pond in it. The cost of digging a pond is $49,995 and with the pond the value of farm will increase only by $36,189.99. Thus according to my model 2, farmer don't need to dig the pond in his farm. Deliverable 3 (10 points): We wish to determine the impact of Specification Buying, X11, on Satisfaction Level, X10. To do so we will split the Hatco data file into two separate data sets based on the Specification Buying, X11. This variable has two categories: 1=employs total value analysis approach, evaluating each purchase separately; 0 = use of specification buying. Sort the entire Hatco data set based on Specification Buying. This will create two separate groups of records. Those records with X11 = 0 and those records with X11 = 1. Treat these as two distinct data sets. For the 2 data sets, X11 = 0 and X11 = 1, perform regression analysis on Satisfaction Level X10 as a function of the first seven variables (Delivery Speed, Price Level, Price Flexibility, Manufacturer Image, Service, Salesforce Image, Product Quality). Use Alpha = .01 to determine variables to remove from model. For X11 = 0 and X11 = 1: Compare the two models and explain the differences between the two models. From a business perspective, why do they differ? Instructions Submit your assignment using the drop box. The file name should follow the format: Your-last-name Assgn3.xlxs