1. Realtors and home buyers usually use appraised value to predict sale price of a home. Data on homes sold in 2006 near Tampa, FL are in the file hw7.xlsx. Run a regression analysis for the simple linear model, predicting Sale Price using Appraised Value. Both variables are in dollars. a. Make a scatter plot of the data. Find the least squares regression line. Add the line to the scatter plot. b. Report the least squares regression line and interpret the slope in the words of the problem. c. Find the coefficient of determination (R2) and interpret in context. d. Find the correlation coefficient (R) and interpret in context. e. Find and interpret a 95% Confidence Interval for the true slope. f. Find the residual for the home with Appraised Value = $418601 g. Graph the Standardized Residuals vs. predicted values. Comment on the graph h. Graph the predicted values vs. the actual values of Sale Price. Comment on the graph. i. Make a histogram of the standardized residuals with bin width of 1. Comment on the j. graph. Comment on the utility of the model and justify. Suggest any improvements you would want to make to the model. 1 AppraisedV SaleP 418601 2 3 1577919 697836 4 5 191620 6 1063901 1275000 7 253929 325000 8 338492 388500 9 243135 291900 397549 490000 250234 326000 515049 599000 303604 400000 364222 445000 10 11 12 13 14 15 16 23 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 3 40 42 43 4 45 46 7 48 49 50 41 44 47 296491 463000 306247 405000 363710 418000 446444 540000 467693 558000 170005 237000 461240 525000 303900 258126 513450 489900 1825000. 890000 250000 615000 520675 618900 305017 346000 500030 519300 410946 304235 206529 258000 182567 242000 485753 605000 486176 575000 270098 326000 285608 330000 289029 354900 281782 340000 203299 242500 297561 390000 358979 440000 1437767 1875000 450000 320351 481615 520000 454982 505000 202000 348500 573000 169023 300297 455743 480000 360000 268464 307500 315349 375000 311366 345000 245506 290000 51 52 53 54 55 56 57 58 59 60 204377 61 403342 62 174516 63 227659 64 181558 65 336156 360501 387368 251227 66 67 68 69 70 71 72 331718 349500 334515 395000 219439 292000 267002 355000 388901 430000 382121 620000 434748 385000 234297 305400 379005 455000 242500 494000 229000 350000 224500 350000 405000 385000 265000 408000 239000 322000 535000 579000 289900 325000 516000 309300 370000 580000 73 74 75 76 77 78 79 374014 139852 290249 449764 497474 272970 292702 407449 272275 347320 511359
