1. Run the best subsets procedure. Identify the best model to use and why? 2. Run the regression procedure using the model you chose in #2 a. Identify the regression equation. b. Interpret all coefficients c. Identify and interpret the R-Square For parts d-e, use the p-value value approach for each test (use .05 for significance level) d. Perform an F Test for overall significance of the model e. Perform a T test for slope for the age variable f. Find a 95% confidence interval for the slope of the SqFeet variable g. Create a prediction, CI, and PI for 1 new set of x values (any valid numbers you want), and interpret each. h. Run the residual plots and indicate if they show any problems with the model. Price SqFeet Age Bedrooms Bathrooms Garage spaces Mtn View 110000 1000 28 3 1 1 1 133500 1400 23 3 1 1 1 112500 1248 58 3 4 1 1 141750 1106 12 2 1 1 1 195250 2112 78 2 6 2 1 132250 1078 33 2 1 1 1 136000 952 13 2 3 2 1 162750 1100 1 2 1 2 1 148500 1040 17 3 1 2 1 123500 1416 27 4 2 1 1 142250 1150 25 3 2 2 1 145500 1220 17 3 2 2 1 155250 1464 28 3 2 2 1 150750 1228 15 3 2 2 1 150900 1132 1 3 4 2 1 144000 1132 1 3 4 2 1 151900 1132 1 3 4 2 1 161500 2100 28 2 3 2 1 155750 1270 1 4 3 2 1 157250 1362 23 3 4 2 1 152900 1120 1 3 3 2 1 145250 1025 1 3 5 2 1 164750 1290 19 3 3 2 1 152500 1260 22 3 4 2 1 150750 1085 1 3 4 2 1 144750 1312 41 3 4 2 1 148250 1489 34 3 4 1 1 174500 1540 24 3 4 2 1 215500 2112 12 4 4 2 1 155750 1351 1 4 5 2 1 167900 1351 1 4 4 2 1 148500 1193 5 3 4 2 1 152500 1200 1 4 4 2 1 167200 1256 1 3 4 2 1 159500 1058 1 3 4 2 1 153600 1210 13 3 4 2 1 168000 1380 1 3 4 2 1 166100 1301 7 3 4 2 1 165200 1375 8 3 4 2 1 174900 1437 8 3 4 2 1 160250 1330 1 3 4 2 1 165100 1361 1 3 4 2 1 170500 1594 16 3 3 2 1 155900 1186 2 3 4 2 1 167700 1336 1 3 4 2 1 165400 1325 5 3 4 2 1 163500 1352 9 3 4 2 1 178500 1354 7 3 2 2 1 164000 1318 1 3 4 2 1 172750 1415 9 3 4 2 1 149000 1337 2 3 4 2 1 176000 2028 25 4 4 2 1 205500 1820 21 3 4 2 1 216750 2182 1 4 4 3 1 181250 2600 37 5 3 1 1 229500 1930 1 3 4 3 1 207500 1834 2 3 4 3 1 206250 1933 1 3 4 3 1 265400 2172 1 3 4 3 1 219250 2193 4 3 4 2 1 272100 2018 1 4 5 4 1 229600 2310 1 4 5 3 1 261000 2256 1 4 4 3 1 274600 2710 20 4 4 3 1 82200 1450 40 3 3 2 0 93300 1728 61 4 1 2 0 111400 1976 37 4 4 1 0 90250 1608 7 3 1 1 0 72500 1532 41 3 2 2 0 70600 1288 5 3 4 2 0 65950 1320 9 3 4 2 0 90100 1416 41 2 4 2 0 110200 2022 36 4 3 1 0 122500 1550 42 3 2 2 0 93900 1568 40 3 3 2 0 84800 1968 40 3 3 1 0 103600 1536 22 3 4 2 0 121000 1872 39 4 4 2 0 102150 2200 31 4 4 1 0 129600 1763 29 3 4 2 0 82950 2073 71 3 3 2 0 111700 1563 52 3 1 3 0 127150 2080 28 5 4 2 0 139300 1968 47 3 3 2 0 111900 1340 9 3 4 3 0 96400 1730 12 3 4 2 0 106250 2168 2 4 4 1 0 126100 2320 22 4 4 1 0 121950 1750 8 3 4 2 0 150950 2000 39 3 4 2 0 142100 2073 44 3 2 2 0 173200 2000 20 3 3 2 0 157850 1900 45 3 6 3 0 164700 2400 38 4 4 2 0 130100 3000 43 4 5 1 0 123900 2172 1 5 6 3 0 180500 2515 51 2 4 2 0 169900 2500 49 4 6 3 0 213000 2345 44 5 4 4 0
1. Run the best subsets procedure. Identify the best model to use and why? 2. Run the regression procedure using the model you chose in #2 a. Identify the regression equation. b. Interpret all coefficients c. Identify and interpret the R-Square For parts d-e, use the p-value value approach for each test (use .05 for significance level) d. Perform an F Test for overall significance of the model e. Perform a T test for slope for the age variable f. Find a 95% confidence interval for the slope of the SqFeet variable g. Create a prediction, CI, and PI for 1 new set of x values (any valid numbers you want), and interpret each. h. Run the residual plots and indicate if they show any problems with the model. Price SqFeet Age Bedrooms Bathrooms Garage spaces Mtn View 110000 1000 28 3 1 1 1 133500 1400 23 3 1 1 1 112500 1248 58 3 4 1 1 141750 1106 12 2 1 1 1 195250 2112 78 2 6 2 1 132250 1078 33 2 1 1 1 136000 952 13 2 3 2 1 162750 1100 1 2 1 2 1 148500 1040 17 3 1 2 1 123500 1416 27 4 2 1 1 142250 1150 25 3 2 2 1 145500 1220 17 3 2 2 1 155250 1464 28 3 2 2 1 150750 1228 15 3 2 2 1 150900 1132 1 3 4 2 1 144000 1132 1 3 4 2 1 151900 1132 1 3 4 2 1 161500 2100 28 2 3 2 1 155750 1270 1 4 3 2 1 157250 1362 23 3 4 2 1 152900 1120 1 3 3 2 1 145250 1025 1 3 5 2 1 164750 1290 19 3 3 2 1 152500 1260 22 3 4 2 1 150750 1085 1 3 4 2 1 144750 1312 41 3 4 2 1 148250 1489 34 3 4 1 1 174500 1540 24 3 4 2 1 215500 2112 12 4 4 2 1 155750 1351 1 4 5 2 1 167900 1351 1 4 4 2 1 148500 1193 5 3 4 2 1 152500 1200 1 4 4 2 1 167200 1256 1 3 4 2 1 159500 1058 1 3 4 2 1 153600 1210 13 3 4 2 1 168000 1380 1 3 4 2 1 166100 1301 7 3 4 2 1 165200 1375 8 3 4 2 1 174900 1437 8 3 4 2 1 160250 1330 1 3 4 2 1 165100 1361 1 3 4 2 1 170500 1594 16 3 3 2 1 155900 1186 2 3 4 2 1 167700 1336 1 3 4 2 1 165400 1325 5 3 4 2 1 163500 1352 9 3 4 2 1 178500 1354 7 3 2 2 1 164000 1318 1 3 4 2 1 172750 1415 9 3 4 2 1 149000 1337 2 3 4 2 1 176000 2028 25 4 4 2 1 205500 1820 21 3 4 2 1 216750 2182 1 4 4 3 1 181250 2600 37 5 3 1 1 229500 1930 1 3 4 3 1 207500 1834 2 3 4 3 1 206250 1933 1 3 4 3 1 265400 2172 1 3 4 3 1 219250 2193 4 3 4 2 1 272100 2018 1 4 5 4 1 229600 2310 1 4 5 3 1 261000 2256 1 4 4 3 1 274600 2710 20 4 4 3 1 82200 1450 40 3 3 2 0 93300 1728 61 4 1 2 0 111400 1976 37 4 4 1 0 90250 1608 7 3 1 1 0 72500 1532 41 3 2 2 0 70600 1288 5 3 4 2 0 65950 1320 9 3 4 2 0 90100 1416 41 2 4 2 0 110200 2022 36 4 3 1 0 122500 1550 42 3 2 2 0 93900 1568 40 3 3 2 0 84800 1968 40 3 3 1 0 103600 1536 22 3 4 2 0 121000 1872 39 4 4 2 0 102150 2200 31 4 4 1 0 129600 1763 29 3 4 2 0 82950 2073 71 3 3 2 0 111700 1563 52 3 1 3 0 127150 2080 28 5 4 2 0 139300 1968 47 3 3 2 0 111900 1340 9 3 4 3 0 96400 1730 12 3 4 2 0 106250 2168 2 4 4 1 0 126100 2320 22 4 4 1 0 121950 1750 8 3 4 2 0 150950 2000 39 3 4 2 0 142100 2073 44 3 2 2 0 173200 2000 20 3 3 2 0 157850 1900 45 3 6 3 0 164700 2400 38 4 4 2 0 130100 3000 43 4 5 1 0 123900 2172 1 5 6 3 0 180500 2515 51 2 4 2 0 169900 2500 49 4 6 3 0 213000 2345 44 5 4 4 0
Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)
6th Edition
ISBN:9781337111348
Author:Bruce Crauder, Benny Evans, Alan Noell
Publisher:Bruce Crauder, Benny Evans, Alan Noell
Chapter3: Straight Lines And Linear Functions
Section3.CR: Chapter Review Exercises
Problem 15CR: Life Expectancy The following table shows the average life expectancy, in years, of a child born in...
Related questions
Question
1. Run the best subsets procedure. Identify the best model to use and why?
2. Run the regression procedure using the model you chose in #2
a. Identify the regression equation.
b. Interpret all coefficients
c. Identify and interpret the R-Square
For parts d-e, use the p-value value approach for each test (use .05 for
significance level)
d. Perform an F Test for overall significance of the model
e. Perform a T test for slope for the age variable
f. Find a 95% confidence interval for the slope of the SqFeet variable
g. Create a prediction, CI, and PI for 1 new set of x values (any valid
numbers you want), and interpret each.
h. Run the residual plots and indicate if they show any problems with
the model.
| Price | SqFeet | Age | Bedrooms | Bathrooms | Garage spaces | Mtn View |
| 110000 | 1000 | 28 | 3 | 1 | 1 | 1 |
| 133500 | 1400 | 23 | 3 | 1 | 1 | 1 |
| 112500 | 1248 | 58 | 3 | 4 | 1 | 1 |
| 141750 | 1106 | 12 | 2 | 1 | 1 | 1 |
| 195250 | 2112 | 78 | 2 | 6 | 2 | 1 |
| 132250 | 1078 | 33 | 2 | 1 | 1 | 1 |
| 136000 | 952 | 13 | 2 | 3 | 2 | 1 |
| 162750 | 1100 | 1 | 2 | 1 | 2 | 1 |
| 148500 | 1040 | 17 | 3 | 1 | 2 | 1 |
| 123500 | 1416 | 27 | 4 | 2 | 1 | 1 |
| 142250 | 1150 | 25 | 3 | 2 | 2 | 1 |
| 145500 | 1220 | 17 | 3 | 2 | 2 | 1 |
| 155250 | 1464 | 28 | 3 | 2 | 2 | 1 |
| 150750 | 1228 | 15 | 3 | 2 | 2 | 1 |
| 150900 | 1132 | 1 | 3 | 4 | 2 | 1 |
| 144000 | 1132 | 1 | 3 | 4 | 2 | 1 |
| 151900 | 1132 | 1 | 3 | 4 | 2 | 1 |
| 161500 | 2100 | 28 | 2 | 3 | 2 | 1 |
| 155750 | 1270 | 1 | 4 | 3 | 2 | 1 |
| 157250 | 1362 | 23 | 3 | 4 | 2 | 1 |
| 152900 | 1120 | 1 | 3 | 3 | 2 | 1 |
| 145250 | 1025 | 1 | 3 | 5 | 2 | 1 |
| 164750 | 1290 | 19 | 3 | 3 | 2 | 1 |
| 152500 | 1260 | 22 | 3 | 4 | 2 | 1 |
| 150750 | 1085 | 1 | 3 | 4 | 2 | 1 |
| 144750 | 1312 | 41 | 3 | 4 | 2 | 1 |
| 148250 | 1489 | 34 | 3 | 4 | 1 | 1 |
| 174500 | 1540 | 24 | 3 | 4 | 2 | 1 |
| 215500 | 2112 | 12 | 4 | 4 | 2 | 1 |
| 155750 | 1351 | 1 | 4 | 5 | 2 | 1 |
| 167900 | 1351 | 1 | 4 | 4 | 2 | 1 |
| 148500 | 1193 | 5 | 3 | 4 | 2 | 1 |
| 152500 | 1200 | 1 | 4 | 4 | 2 | 1 |
| 167200 | 1256 | 1 | 3 | 4 | 2 | 1 |
| 159500 | 1058 | 1 | 3 | 4 | 2 | 1 |
| 153600 | 1210 | 13 | 3 | 4 | 2 | 1 |
| 168000 | 1380 | 1 | 3 | 4 | 2 | 1 |
| 166100 | 1301 | 7 | 3 | 4 | 2 | 1 |
| 165200 | 1375 | 8 | 3 | 4 | 2 | 1 |
| 174900 | 1437 | 8 | 3 | 4 | 2 | 1 |
| 160250 | 1330 | 1 | 3 | 4 | 2 | 1 |
| 165100 | 1361 | 1 | 3 | 4 | 2 | 1 |
| 170500 | 1594 | 16 | 3 | 3 | 2 | 1 |
| 155900 | 1186 | 2 | 3 | 4 | 2 | 1 |
| 167700 | 1336 | 1 | 3 | 4 | 2 | 1 |
| 165400 | 1325 | 5 | 3 | 4 | 2 | 1 |
| 163500 | 1352 | 9 | 3 | 4 | 2 | 1 |
| 178500 | 1354 | 7 | 3 | 2 | 2 | 1 |
| 164000 | 1318 | 1 | 3 | 4 | 2 | 1 |
| 172750 | 1415 | 9 | 3 | 4 | 2 | 1 |
| 149000 | 1337 | 2 | 3 | 4 | 2 | 1 |
| 176000 | 2028 | 25 | 4 | 4 | 2 | 1 |
| 205500 | 1820 | 21 | 3 | 4 | 2 | 1 |
| 216750 | 2182 | 1 | 4 | 4 | 3 | 1 |
| 181250 | 2600 | 37 | 5 | 3 | 1 | 1 |
| 229500 | 1930 | 1 | 3 | 4 | 3 | 1 |
| 207500 | 1834 | 2 | 3 | 4 | 3 | 1 |
| 206250 | 1933 | 1 | 3 | 4 | 3 | 1 |
| 265400 | 2172 | 1 | 3 | 4 | 3 | 1 |
| 219250 | 2193 | 4 | 3 | 4 | 2 | 1 |
| 272100 | 2018 | 1 | 4 | 5 | 4 | 1 |
| 229600 | 2310 | 1 | 4 | 5 | 3 | 1 |
| 261000 | 2256 | 1 | 4 | 4 | 3 | 1 |
| 274600 | 2710 | 20 | 4 | 4 | 3 | 1 |
| 82200 | 1450 | 40 | 3 | 3 | 2 | 0 |
| 93300 | 1728 | 61 | 4 | 1 | 2 | 0 |
| 111400 | 1976 | 37 | 4 | 4 | 1 | 0 |
| 90250 | 1608 | 7 | 3 | 1 | 1 | 0 |
| 72500 | 1532 | 41 | 3 | 2 | 2 | 0 |
| 70600 | 1288 | 5 | 3 | 4 | 2 | 0 |
| 65950 | 1320 | 9 | 3 | 4 | 2 | 0 |
| 90100 | 1416 | 41 | 2 | 4 | 2 | 0 |
| 110200 | 2022 | 36 | 4 | 3 | 1 | 0 |
| 122500 | 1550 | 42 | 3 | 2 | 2 | 0 |
| 93900 | 1568 | 40 | 3 | 3 | 2 | 0 |
| 84800 | 1968 | 40 | 3 | 3 | 1 | 0 |
| 103600 | 1536 | 22 | 3 | 4 | 2 | 0 |
| 121000 | 1872 | 39 | 4 | 4 | 2 | 0 |
| 102150 | 2200 | 31 | 4 | 4 | 1 | 0 |
| 129600 | 1763 | 29 | 3 | 4 | 2 | 0 |
| 82950 | 2073 | 71 | 3 | 3 | 2 | 0 |
| 111700 | 1563 | 52 | 3 | 1 | 3 | 0 |
| 127150 | 2080 | 28 | 5 | 4 | 2 | 0 |
| 139300 | 1968 | 47 | 3 | 3 | 2 | 0 |
| 111900 | 1340 | 9 | 3 | 4 | 3 | 0 |
| 96400 | 1730 | 12 | 3 | 4 | 2 | 0 |
| 106250 | 2168 | 2 | 4 | 4 | 1 | 0 |
| 126100 | 2320 | 22 | 4 | 4 | 1 | 0 |
| 121950 | 1750 | 8 | 3 | 4 | 2 | 0 |
| 150950 | 2000 | 39 | 3 | 4 | 2 | 0 |
| 142100 | 2073 | 44 | 3 | 2 | 2 | 0 |
| 173200 | 2000 | 20 | 3 | 3 | 2 | 0 |
| 157850 | 1900 | 45 | 3 | 6 | 3 | 0 |
| 164700 | 2400 | 38 | 4 | 4 | 2 | 0 |
| 130100 | 3000 | 43 | 4 | 5 | 1 | 0 |
| 123900 | 2172 | 1 | 5 | 6 | 3 | 0 |
| 180500 | 2515 | 51 | 2 | 4 | 2 | 0 |
| 169900 | 2500 | 49 | 4 | 6 | 3 | 0 |
| 213000 | 2345 | 44 | 5 | 4 | 4 | 0 |
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