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Sum of Bedrooms and Bathrooms Age of the Home Sales Price 227,900 284,900 149,900 309,900 134,900 440,000 150,000 154,900 700,000 Sum of Bedrooms and Bathrooms Sum of Bedrooms and Bathrooms Line 70 6. Residual Plot 59 Fit Plot 4 70 400000 800.000 6.5 48 5.5 64 600,000 200000 10.5 21 4 62 400,000 70 2 10 • 12 200,000 50 -200000 53 257,000 Sum of Bedrooms and Bathrooms 59 239,900 8 10 12 Sum of Bedrooms and Bathrooms 349,900 339,900 289,000 399,900 290,000 278,000 7.5 73 7.5 20 Age of the Home Residual Plot 5 46 400000 6.5 17 Age of the Home Line Fit Plot 300000 7 19 800,000 700,000 7 62 200000

Sum of Bedrooms and Bathrooms Age of the Home Sales Price 227,900 284,900 149,900 309,900 134,900 440,000 150,000 154,900 700,000 Sum of Bedrooms and Bathrooms Sum of Bedrooms and Bathrooms Line 70 6. Residual Plot 59 Fit Plot 4 70 400000 800.000 6.5 48 5.5 64 600,000 200000 10.5 21 4 62 400,000 70 2 10 • 12 200,000 50 -200000 53 257,000 Sum of Bedrooms and Bathrooms 59 239,900 8 10 12 Sum of Bedrooms and Bathrooms 349,900 339,900 289,000 399,900 290,000 278,000 7.5 73 7.5 20 Age of the Home Residual Plot 5 46 400000 6.5 17 Age of the Home Line Fit Plot 300000 7 19 800,000 700,000 7 62 200000

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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B D E F G H K M 1 Sum of Bedrooms and Bathrooms Age of the Home Sales Price Sum of Bedrooms and Bathrooms Sum of Bedrooms and Bathrooms Line 2 5 70 227,900 284,900 149,900 Fit Plot 3 59 Residual Plot 4 4 70 400000 800,000 5 6.5 48 309,900 6 5.5 64 134,900 600,000 200000 440,000 150,000 7 10.5 21 8 4 62 400,000 9 70 154,900 10 • 12 200,000 10 50 700,000 -200000 11 6 53 257,000 239,900 349,900 Sum of Bedrooms and Bathrooms 12 7 59 2 4. 6 8 10 12 13 7.5 73 Sum of Bedrooms and Bathrooms 14 20 339,900 Age of the Home Residual Plot 7.5 15 46 289,000 400000 16 Age of the Home Line Fit Plot 6.5 17 399,900 300000 17 7 19 290,000 800,000 18 62 278,000 200000 700,000 19 100000 600,000 20 SUMMARY OUTPUT 500,000 21 20 70 400,000 10 30 40 50 80 -100000 Regression Statistics Multiple R R Square Adjusted R Square 22 300,000 23 0.621120988 -200000 200,000 Age of the Home 100,000 24 0.385791282 25 0.298047179 10 20 30 40 50 60 70 80 26 Standard Error 114352.208 Age of the Home 27 Observations 17 28 29 ANOVA Normal Probability Plot 30 df MS Significance F F 31 Regression 2 1.14988E+11 57494139965 4.396777334 0.032977053 800000 32 Residual 14 1.8307E+11 13076427484 700000 600000 33 Total 16 2.98058E+11 500000 34 400000 Standard Error P-value 0.746793667 Upper 95% 35 Coefficients 68063.34627 t Stat Lower 95% Lower 95.0% Upper 95.0% 300000 36 206682.4812 0.329313572 -375226.4881 -375226.4881 Intercept 37 Sum of Bedrooms and Bathrooms Age of the Home 511353.1807 511353.1807 200000 44956.45037 22249.49977 2.020560051 0.062882492 -2763.980539 92676.88129 -2763.980539 92676.88129 100000 38 -1125.62813 1767.438606 -0.63686972 0.534485638 -4916.406924 2665.150663 -4916.406924 2665.150663 20 40 60 80 100 120 39 40 Sample Percentile 41 42 RESIDUAL OUTPUT PROBABILITY OUTPUT 43 44 Observation redicted Sales Pric Residuals andard Residuals Percentile Sales Price 45 1 214051.629 13848.37099 0.129464324 2.941176471 134900 46 2 271389.9888 13510.01118 0.126301098 8.823529412 149900 47 3 169095.1786 -19195.1786 -0.17945005 14.70588235 150000 48 4 306250.1234 3649.876563 0.034121616 20.58823529 154900 49 243283.623 -108383.623 -1.01324643 26.47058824 227900 50 6 516467.8845 -76467.8845 -0.71487563 32.35294118 239900 51 178100.2037 -28100.2037 -0.26270049 38.23529412 257000 52 8 214051.629 -59151.629 -0.55299108 44.11764706 278000 53 326477.0924 373522.9076 3.491955176 50 284900 54 10 278143.7576 -21143.7576 -0.19766674 55.88235294 289000 55 11 316346.4392 -76446.4392-0.71467515 61.76470588 290000 56 12 323065.8705 26834.12945 0.250864339 67.64705882 309900 57 13 382724.1615 -42824.1615 -0.40035042 73.52941176 339900 58 14 241066.7041 47933.29586 0.448114204 79.41176471 349900 59 15 341144.5955 58755.40452 0.549286897 85.29411765 399900 60 16 361371.5644 -71371.5644 -0.66723164 91.17647059 440000 61 17 312969.5548 -34969.5548 -0.32692002 97.05882353 700000 62 Residuals Residuals Sales Price Sales Price Sales Price
Transcribed Image Text:B D E F G H K M 1 Sum of Bedrooms and Bathrooms Age of the Home Sales Price Sum of Bedrooms and Bathrooms Sum of Bedrooms and Bathrooms Line 2 5 70 227,900 284,900 149,900 Fit Plot 3 59 Residual Plot 4 4 70 400000 800,000 5 6.5 48 309,900 6 5.5 64 134,900 600,000 200000 440,000 150,000 7 10.5 21 8 4 62 400,000 9 70 154,900 10 • 12 200,000 10 50 700,000 -200000 11 6 53 257,000 239,900 349,900 Sum of Bedrooms and Bathrooms 12 7 59 2 4. 6 8 10 12 13 7.5 73 Sum of Bedrooms and Bathrooms 14 20 339,900 Age of the Home Residual Plot 7.5 15 46 289,000 400000 16 Age of the Home Line Fit Plot 6.5 17 399,900 300000 17 7 19 290,000 800,000 18 62 278,000 200000 700,000 19 100000 600,000 20 SUMMARY OUTPUT 500,000 21 20 70 400,000 10 30 40 50 80 -100000 Regression Statistics Multiple R R Square Adjusted R Square 22 300,000 23 0.621120988 -200000 200,000 Age of the Home 100,000 24 0.385791282 25 0.298047179 10 20 30 40 50 60 70 80 26 Standard Error 114352.208 Age of the Home 27 Observations 17 28 29 ANOVA Normal Probability Plot 30 df MS Significance F F 31 Regression 2 1.14988E+11 57494139965 4.396777334 0.032977053 800000 32 Residual 14 1.8307E+11 13076427484 700000 600000 33 Total 16 2.98058E+11 500000 34 400000 Standard Error P-value 0.746793667 Upper 95% 35 Coefficients 68063.34627 t Stat Lower 95% Lower 95.0% Upper 95.0% 300000 36 206682.4812 0.329313572 -375226.4881 -375226.4881 Intercept 37 Sum of Bedrooms and Bathrooms Age of the Home 511353.1807 511353.1807 200000 44956.45037 22249.49977 2.020560051 0.062882492 -2763.980539 92676.88129 -2763.980539 92676.88129 100000 38 -1125.62813 1767.438606 -0.63686972 0.534485638 -4916.406924 2665.150663 -4916.406924 2665.150663 20 40 60 80 100 120 39 40 Sample Percentile 41 42 RESIDUAL OUTPUT PROBABILITY OUTPUT 43 44 Observation redicted Sales Pric Residuals andard Residuals Percentile Sales Price 45 1 214051.629 13848.37099 0.129464324 2.941176471 134900 46 2 271389.9888 13510.01118 0.126301098 8.823529412 149900 47 3 169095.1786 -19195.1786 -0.17945005 14.70588235 150000 48 4 306250.1234 3649.876563 0.034121616 20.58823529 154900 49 243283.623 -108383.623 -1.01324643 26.47058824 227900 50 6 516467.8845 -76467.8845 -0.71487563 32.35294118 239900 51 178100.2037 -28100.2037 -0.26270049 38.23529412 257000 52 8 214051.629 -59151.629 -0.55299108 44.11764706 278000 53 326477.0924 373522.9076 3.491955176 50 284900 54 10 278143.7576 -21143.7576 -0.19766674 55.88235294 289000 55 11 316346.4392 -76446.4392-0.71467515 61.76470588 290000 56 12 323065.8705 26834.12945 0.250864339 67.64705882 309900 57 13 382724.1615 -42824.1615 -0.40035042 73.52941176 339900 58 14 241066.7041 47933.29586 0.448114204 79.41176471 349900 59 15 341144.5955 58755.40452 0.549286897 85.29411765 399900 60 16 361371.5644 -71371.5644 -0.66723164 91.17647059 440000 61 17 312969.5548 -34969.5548 -0.32692002 97.05882353 700000 62 Residuals Residuals Sales Price Sales Price Sales Price
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.
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