Part IV. Simple Regression Let's say that we wanted to be able to predict the selling price of a home based on its area in sq. ft. Using this sample data, perform a simple-linear regression to determine the line-of-best fit. Use the Area as your x (independent) variable and Selling Price as your y (response) variable. Use 3 places after the decimal in your answer. Refer to page 13 in the Stat Disk User's Manual. 13. Paste your results here: Answer the following questions related to this simple regression 14. What is the equation of the line-of-best fit? Insert the values for bo and b1 from above into y = bo + b1x. 15. What is the slope of the line? What does it tell you about the relationship between the Area and Selling Price data? Be sure to specify the proper units. 16. What is the y-intercept of the line? What does it tell you about the relationship between Area and Selling Price? 17. What would you predict the selling price of a house that is 3500 sq ft? Show your calculation and round to the nearest cent. 18. Let's say you want to pay $254,000 for a house. Based on the linear regression relationship between area and selling price found above, what size house could you afford? Round to the nearest whole number. 19. Find the coefficient of determination (R2 value) for this data. What does this tell you about this relationship? [Hint: see definition on Page 311.] \f1. Selling Price 2. List Price 3. 400000 414000 370000 379000 382500 389900 300000 299900 305000 319900 320000 319900 321000 328900 445000 450000 377500 385000 460000 479000 265000 275000 299000 299000 385000 379000 430000 435000 214900 219900 475000 485000 280000 289000 457000 499900 210000 224900 272500 274900 268000 275000 300000 319900 477000 479000 292000 294900 379000 383900 295000 299900 499000 499000 292000 299000 305000 299900 520000 529700 308000 320000 316000 310000 355500 362500 225000 229000 270000 290000 253000 259900 310000 314900 300000 309900 295000 295000 478000 479000 Area 4. Acres 5. Age 6. Taxes 7. Rooms 8. Bedrooms 2704 2.27 27 4920 9 2096 0.75 21 4113 8 2737 1 36 6072 9 1800 0.43 34 4024 8 1066 3.6 69 3562 6 1820 1.7 34 4672 7 2700 0.81 35 3645 8 2316 2 19 6256 9 2448 1.5 40 5469 9 3040 1.09 20 6740 10 1500 1.6 39 4046 6 1448 0.42 44 3481 7 2400 0.89 33 4411 9 2200 4.79 6 5714 8 1635 0.25 49 2560 5 2224 11.58 21 7885 7 1738 0.46 49 3011 8 3432 1.84 14 9809 11 1175 0.94 64 1367 7 1393 1.39 44 2317 6 1196 0.83 44 3360 4 1860 0.57 32 4294 9 3867 1.1 19 9135 10 1800 0.52 47 3690 8 2722 1 29 6283 10 2240 0.9 144 3286 6 2174 5.98 62 3894 6 1650 2.93 52 3476 7 2000 0.33 36 4146 8 3350 1.53 6 8350 11 1776 0.63 42 4584 8 1850 2 25 4380 7 2600 0.44 46 4009 10 1300 0.62 49 3047 6 1352 0.68 24 2801 6 1312 0.68 44 4048 6 1664 1.69 53 2940 6 1700 0.83 33 4281 8 1650 2.9 34 4299 6 2400 2.14 6 6688 8 9. Baths 3 4 4 4 3 3 3 4 4 4 2 3 4 4 3 3 3 4 3 3 2 3 4 2 4 3 3 3 3 4 4 3 5 3 3 2 3 4 2 4 3 2 2 2 2 2 1 2 3 2 2 1 3 2 1 2 2 3 1 1 1 2 4 1 3 1 2 1 3 2 2 2 2 1 1 1 2 2 2 2