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a. State the null and alternative hypothesis in this global test for linear model utility. b. Give the p-value and your conclusion.

a. State the null and alternative hypothesis in this global test for linear model utility. b. Give the p-value and your conclusion.

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)
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
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a. State the null and alternative hypothesis in this global test for linear model utility. b. Give the p-value and your conclusion. c. Conduct t-tests on each of the beta parameters. What is your conclusion in each case? d. What percentage of the variation in the price is explained by these independent variables? Based on this, is a multiple linear regression model a good model for these data? Explain.
Transcribed Image Text:a. State the null and alternative hypothesis in this global test for linear model utility. b. Give the p-value and your conclusion. c. Conduct t-tests on each of the beta parameters. What is your conclusion in each case? d. What percentage of the variation in the price is explained by these independent variables? Based on this, is a multiple linear regression model a good model for these data? Explain.
B D E G H K M 1 Square Feet Age of the Home Sales Price Square Feet Residual Plot Square Feet Line Fit Plot 2 1,610 70 227,900 3 2,146 284,900 149,900 59 100000 800,000 4 816 70 700,000 50000 600,000 5 2,183 48 309,900 500,000 6 1,046 64 134,900 400,000 5,183 21 440,000 1,000 2,000 3,000 4,000 5,000 6.000 300,000 -50000 8 1,150 62 150,000 200,000 100,000 9 1,068 70 154,900 -100000 10 5,570 50 700,000 -150000 1,000 2,000 3,000 4,000 5,000 6,000 11 2,449 53 257,000 Square Feet 12 1,950 59 239,900 Square Feet 2,630 2,732 13 73 349,900 14 20 339,900 Age of the Home Residual Plot 15 1,908 Age of the Home Line Fit Plot 289,000 399,900 46 100000 16 3,666 17 800,000 17 1,878 19 290,000 50000 18 2,172 62 278,000 600,000 19 20 MMARY OUTPUT 10 20 30 40 50 60. 70 80 400,000 -50000 21 200,000 -100000 ression Statistics 23 Multiple R 0.94020507 -150000 10 20 30 40 50 60 70 80 Age of the Home R Square 25 ljusted R Squa 0.867412084 26 tandard Erro 24 0.883985573 Age of the Home 49698.41817 27 Observations 17 28 Normal Probability Plot 29 ANOVA 800000 30 df S MS Significance F 31 Regression 2 2.63479E+11 1.3174E+11 53.33732344 2.82868E-07 600000 32 Residual 14 34579058765 2469932769 400000 33 Total 16 2.98058E+11 34 200000 Upper 95% 159992.9769 Standard Error t Stat Lower 95.0% -86656.451 35 Coefficients P-value Lower 95% Upper 95.0% 36 Intercept 36668.26295 57499.75727 0.63771161 0.533953161 -86656.451 159992.9769 20 40 60 80 100 120 37 Square Feet 38 ge of the Hon 99.16082372 10.96828321 9.04068776 3.20882E-07 75.6361959 122.6854515 75.6361959 122.6854515 Sample Percentile 452.6516011 747.433283 0.60560803 0.554466659 -1150.433354 2055.736556 -1150.433354 2055.736556 39 40 41 42 SIDUAL OUTPUT PROBABILITY OUTPUT 43 44 Observation redicted Sales Pric Residuals indard Residuals Percentile Sales Price 45 1 228002.8012 -102.801216 -0.0022113 2.941176471 134900 46 2 276173.8351 8726.164883 0.18770542 8.823529412 149900 47 3 149269.1072 630.8928168 0.01357091 14.70588235 150000 48 4 274863.618 35036.38202 0.75365513 20.58823529 154900 49 5 169360.187 -34460.187 -0.7412608 26.47058824 227900 50 6 560124.4959 -120124.496 -2,5839552 32.35294118 239900 51 7 178767.6095 -28767.6095 -0.6188098 38.23529412 257000 52 8 174257.6348 -19357.6348 -0.4163952 44.11764706 278000 53 9 611626.6311 88373.36888 1.90096805 50 284900 54 10 303503.6551 -46503.6551 -1.0003236 55.88235294 289000 55 11 256738.3137 -16838.3137 -0.362203 61.76470588 290000 56 12 330504.7962 19395.20379 0.41720332 67.64705882 309900 57 13 316628.6654 23271.33463 0.50058139 73.52941176 339900 58 14 246689.0883 42310.91174 0.91013495 79.41176471 349900 59 15 407886.9199 -7986.91992 -0.1718038 85.29411765 399900 60 16 231492.6703 58507.32968 1.25853032 91.17647059 440000 61 17 280109.9713 -2109.97134 -0.0453868 97.05882353 700000 62 Residuals Sales Price Sales Price Sales Price
Transcribed Image Text:B D E G H K M 1 Square Feet Age of the Home Sales Price Square Feet Residual Plot Square Feet Line Fit Plot 2 1,610 70 227,900 3 2,146 284,900 149,900 59 100000 800,000 4 816 70 700,000 50000 600,000 5 2,183 48 309,900 500,000 6 1,046 64 134,900 400,000 5,183 21 440,000 1,000 2,000 3,000 4,000 5,000 6.000 300,000 -50000 8 1,150 62 150,000 200,000 100,000 9 1,068 70 154,900 -100000 10 5,570 50 700,000 -150000 1,000 2,000 3,000 4,000 5,000 6,000 11 2,449 53 257,000 Square Feet 12 1,950 59 239,900 Square Feet 2,630 2,732 13 73 349,900 14 20 339,900 Age of the Home Residual Plot 15 1,908 Age of the Home Line Fit Plot 289,000 399,900 46 100000 16 3,666 17 800,000 17 1,878 19 290,000 50000 18 2,172 62 278,000 600,000 19 20 MMARY OUTPUT 10 20 30 40 50 60. 70 80 400,000 -50000 21 200,000 -100000 ression Statistics 23 Multiple R 0.94020507 -150000 10 20 30 40 50 60 70 80 Age of the Home R Square 25 ljusted R Squa 0.867412084 26 tandard Erro 24 0.883985573 Age of the Home 49698.41817 27 Observations 17 28 Normal Probability Plot 29 ANOVA 800000 30 df S MS Significance F 31 Regression 2 2.63479E+11 1.3174E+11 53.33732344 2.82868E-07 600000 32 Residual 14 34579058765 2469932769 400000 33 Total 16 2.98058E+11 34 200000 Upper 95% 159992.9769 Standard Error t Stat Lower 95.0% -86656.451 35 Coefficients P-value Lower 95% Upper 95.0% 36 Intercept 36668.26295 57499.75727 0.63771161 0.533953161 -86656.451 159992.9769 20 40 60 80 100 120 37 Square Feet 38 ge of the Hon 99.16082372 10.96828321 9.04068776 3.20882E-07 75.6361959 122.6854515 75.6361959 122.6854515 Sample Percentile 452.6516011 747.433283 0.60560803 0.554466659 -1150.433354 2055.736556 -1150.433354 2055.736556 39 40 41 42 SIDUAL OUTPUT PROBABILITY OUTPUT 43 44 Observation redicted Sales Pric Residuals indard Residuals Percentile Sales Price 45 1 228002.8012 -102.801216 -0.0022113 2.941176471 134900 46 2 276173.8351 8726.164883 0.18770542 8.823529412 149900 47 3 149269.1072 630.8928168 0.01357091 14.70588235 150000 48 4 274863.618 35036.38202 0.75365513 20.58823529 154900 49 5 169360.187 -34460.187 -0.7412608 26.47058824 227900 50 6 560124.4959 -120124.496 -2,5839552 32.35294118 239900 51 7 178767.6095 -28767.6095 -0.6188098 38.23529412 257000 52 8 174257.6348 -19357.6348 -0.4163952 44.11764706 278000 53 9 611626.6311 88373.36888 1.90096805 50 284900 54 10 303503.6551 -46503.6551 -1.0003236 55.88235294 289000 55 11 256738.3137 -16838.3137 -0.362203 61.76470588 290000 56 12 330504.7962 19395.20379 0.41720332 67.64705882 309900 57 13 316628.6654 23271.33463 0.50058139 73.52941176 339900 58 14 246689.0883 42310.91174 0.91013495 79.41176471 349900 59 15 407886.9199 -7986.91992 -0.1718038 85.29411765 399900 60 16 231492.6703 58507.32968 1.25853032 91.17647059 440000 61 17 280109.9713 -2109.97134 -0.0453868 97.05882353 700000 62 Residuals Sales Price Sales Price Sales Price
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