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. Conduct t-tests on each of the beta parameters. What is your conclusion in each case? 1. What percentage of the variation in the price is explained by these independent variables'

. Conduct t-tests on each of the beta parameters. What is your conclusion in each case? 1. What percentage of the variation in the price is explained by these independent variables'

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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c and d

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 F G K M N 1 Square Feet Sum of Bedrooms and Bathrooms Sales Price Square Feet Residual Plot Square Feet Line Fit Plot 2 1,610 227,900 284,900 149,900 3 2,146 100000 800,000 4 816 4 700.000 5 2,183 6.5 309,900 50000 600,000 500.000 6 1,046 5.5 134,900 400,000 5,183 1,150 10.5 440,000 L,000 300,000 8 4 150,000 2,000 3,000 4,000 5,000 6.000 200,000 -50000 154,900 700,000 9 1,068 5 100,000 10 5,570 11 2,449 6 257,000 -100000 1,000 2,000 3,000 4,000 5,000 6,000 Square Feet 12 1,950 239,900 Square Feet 2,630 2,732 13 7.5 349,900 14 7.5 339,900 Sum of Bedrooms and Bathrooms Sum of Bedrooms and Bathrooms Line 15 1,908 289,000 16 3,666 6.5 399,900 Residual Plot Fit Plot 17 1,878 7 290,000 100000 18 2,172 7 278,000 800,000 19 50000 600,000 20 MMARY OUTPUT 400,000 21 8 10 12 200,000 ression Statistics 23 Multiple R -50000 0.949278954 -100000 2 4 6 10 12 R Square 25 ljusted R Squa 26 tandard Erro 27 Observations 24 0.901130532 Sum of Bedrooms and Bathrooms Sum of Bedrooms and Bathrooms 0.887006322 45879.39321 17 Normal Probability Plot 28 29 ANOVA 800000 30 df SS MS Significance F 31 Regression 2 2.68589E+11 1.34295E+11 63.80042133 9.23497E-08 600000 32 Residual 14 29468862100 2104918721 400000 33 Total 16 2.98058E+11 200000 34 Upper 95% 259075.9965 Lower 95% Coefficients 147150.7064 35 Standard Error t Stat P-value Lower 95.0% Upper 95.0% 36 Intercept 52184.81198 2.819799494 0.013638117 35225.41639 35225.41639 259075.9965 20 40 60 80 100 120 37 Square Feet 38 drooms and E 111.8732138 12.87582457 8.688625194 5.17078E-07 84.25731663 139.4891109 84.25731663 139.4891109 Sample Percentile -18673.40799 11045.48378 -1.690592133 0.113047191 -42363.61457 5016.798593 -42363.61457 5016.798593 39 40 41 42 SIDUAL OUTPUT PROBABILITY OUTPUT 43 44 Observation Predicted Sales Price Residuals andard Residuals Percentile Sales Price 45 1 233899.5407 -5999.540663 -0.139796504 2.941176471 134900 46 2 275190.1753 9709.824745 0.226250579 8.823529412 149900 47 3 163745.6169 -13845.61692 -0.322619504 14.70588235 150000 48 4 269992.7802 39907.21983 0.929886155 20.58823529 154900 49 5 161466.3441 -26566.3441 -0.619027726 26.47058824 227900 50 6 530918.7895 -90918.78953 -2.118516999 32.35294118 239900 51 7 201111.2703 -51111.27032 -1.190953988 38.23529412 257000 52 8 173264.2588 -18364.2588 -0.427909287 44.11764706 278000 53 9 639570.6512 60429.34878 1.408076408 50 284900 54 10 309087.759 -52087.75903 -1.213707348 55.88235294 289000 55 11 234589.6174 5310.382633 0.123738294 61.76470588 290000 56 12 301326.6987 48573.30126 1.131816261 67.64705882 309900 57 13 312737.7665 27162.23346 0.632912664 73.52941176 339900 58 14 267237.7584 21762.24163 0.507086369 79.41176471 349900 59 15 435900.7562 -36000.75619 -0.83886086 85.29411765 399900 60 16 226534.746 63465.25402 1.478816647 91.17647059 440000 61 17 259425.4708 18574.52918 0.43280884 97.05882353 700000 62 Residuals Sales Price Sales Price Sales Price
Transcribed Image Text:B F G K M N 1 Square Feet Sum of Bedrooms and Bathrooms Sales Price Square Feet Residual Plot Square Feet Line Fit Plot 2 1,610 227,900 284,900 149,900 3 2,146 100000 800,000 4 816 4 700.000 5 2,183 6.5 309,900 50000 600,000 500.000 6 1,046 5.5 134,900 400,000 5,183 1,150 10.5 440,000 L,000 300,000 8 4 150,000 2,000 3,000 4,000 5,000 6.000 200,000 -50000 154,900 700,000 9 1,068 5 100,000 10 5,570 11 2,449 6 257,000 -100000 1,000 2,000 3,000 4,000 5,000 6,000 Square Feet 12 1,950 239,900 Square Feet 2,630 2,732 13 7.5 349,900 14 7.5 339,900 Sum of Bedrooms and Bathrooms Sum of Bedrooms and Bathrooms Line 15 1,908 289,000 16 3,666 6.5 399,900 Residual Plot Fit Plot 17 1,878 7 290,000 100000 18 2,172 7 278,000 800,000 19 50000 600,000 20 MMARY OUTPUT 400,000 21 8 10 12 200,000 ression Statistics 23 Multiple R -50000 0.949278954 -100000 2 4 6 10 12 R Square 25 ljusted R Squa 26 tandard Erro 27 Observations 24 0.901130532 Sum of Bedrooms and Bathrooms Sum of Bedrooms and Bathrooms 0.887006322 45879.39321 17 Normal Probability Plot 28 29 ANOVA 800000 30 df SS MS Significance F 31 Regression 2 2.68589E+11 1.34295E+11 63.80042133 9.23497E-08 600000 32 Residual 14 29468862100 2104918721 400000 33 Total 16 2.98058E+11 200000 34 Upper 95% 259075.9965 Lower 95% Coefficients 147150.7064 35 Standard Error t Stat P-value Lower 95.0% Upper 95.0% 36 Intercept 52184.81198 2.819799494 0.013638117 35225.41639 35225.41639 259075.9965 20 40 60 80 100 120 37 Square Feet 38 drooms and E 111.8732138 12.87582457 8.688625194 5.17078E-07 84.25731663 139.4891109 84.25731663 139.4891109 Sample Percentile -18673.40799 11045.48378 -1.690592133 0.113047191 -42363.61457 5016.798593 -42363.61457 5016.798593 39 40 41 42 SIDUAL OUTPUT PROBABILITY OUTPUT 43 44 Observation Predicted Sales Price Residuals andard Residuals Percentile Sales Price 45 1 233899.5407 -5999.540663 -0.139796504 2.941176471 134900 46 2 275190.1753 9709.824745 0.226250579 8.823529412 149900 47 3 163745.6169 -13845.61692 -0.322619504 14.70588235 150000 48 4 269992.7802 39907.21983 0.929886155 20.58823529 154900 49 5 161466.3441 -26566.3441 -0.619027726 26.47058824 227900 50 6 530918.7895 -90918.78953 -2.118516999 32.35294118 239900 51 7 201111.2703 -51111.27032 -1.190953988 38.23529412 257000 52 8 173264.2588 -18364.2588 -0.427909287 44.11764706 278000 53 9 639570.6512 60429.34878 1.408076408 50 284900 54 10 309087.759 -52087.75903 -1.213707348 55.88235294 289000 55 11 234589.6174 5310.382633 0.123738294 61.76470588 290000 56 12 301326.6987 48573.30126 1.131816261 67.64705882 309900 57 13 312737.7665 27162.23346 0.632912664 73.52941176 339900 58 14 267237.7584 21762.24163 0.507086369 79.41176471 349900 59 15 435900.7562 -36000.75619 -0.83886086 85.29411765 399900 60 16 226534.746 63465.25402 1.478816647 91.17647059 440000 61 17 259425.4708 18574.52918 0.43280884 97.05882353 700000 62 Residuals Sales Price Sales Price Sales Price
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