Questions 18-27 are related to the following How much does the size of the living area of a house {x} affect the price of the house {y}? The following data shows the price of the house {$1,000} and the size {living area in square feet]. Use the data below to develop an estimated regression equation that could be used to predict the price of the house for a given size. 1: = Square feet y = Price in thousands of dollars {$1,000] PRICE SQP'I' 135.4.- 1161 386.5 3616 91.2 114-7 231.9 2389 324-.6 2950 339.8 354-7 200.5 1918 159 1229 481.1 2855 189.6 1832 160.2 1334- 112.2 1651 218.9 2298 136.8 1319 159.7 1716 215.3 1929 128.4- 2805 91.1 1316 490.8 2956 \f22 :anrm 23 Qnu'm 24 Dunn's: 25 Duncrm The measure of closeness of t, or measure of dispersion of observed price around the regression line is, 65.463 62.945 60.524 58.196 The fraction of variations in house price explained by house size is, 0.6426 0.5950 0.5610 0.5101 The house price and size gures above are data from a sample of houses selected from the population of all houses in a geographic area to compute b1 as an estimate of the population slope parameter 31- The sample statistic '01 is the estimator of the population parameter 81. The standard error of the sample statistic b1 is, 0.0053 0.0088 0.0147 0.0245 The margin of error for a 95% condence interval for the population slope parameter is, 0.01758 0.02197 0.02747 0.03433 $65,463 $62,945 $60,524 $58,196 $17.53 $21.97 $27.47 $34.33 26 The 95% interval estimate for [31 is, a 0.0655 0.0827 $65.52 $82.75 b 0.0618 0.0864 $61.83 $86.44 C 0.0566 0.0917 $56.56 $91.71 (1 0.0490 0.0992 $49.02 $99.25 27 The value of the test statistic to test the hypothesis that house size has no impact on house price is, a 11.2008 b 10.1825 (2 9.2568 [1 8.4153 Questions 28-35 are related to the following The following table is the regression summary output for a mode] depicting the impact household weekly income [INCOME in $100] on household weekly expenditure on food DEPENDENT VARIABLE = FOOD Regression Statistics Multiple R 0.5043 R Square a = 0.05 Adjusted R Square 0.2433 t_d_z"2,df = 1.995 Standard Error Observations 7'0 ANOVA df SS MS F Stat Pvalue Regression 1 4526812 23.189 0.00001 Error 19521.79 Total 69 1?80169 bj sefbj] t Stat Pvalue Lower 95%Upper 95% Intercept 60.8303 1.9423 0.05624 -3.2324 2395377 INCOME 0.00001 x = INCOME Hundreds of dollars [$100] 3* = FOOD Dollars [$] Use the following calculations first: Exy = 8668682 n = 70 7: = 29.29114 iv = 399.325 2 Ex 64953.30? 28 Gun Dill-1 29 -nU'-I 30 -nu'm 31 9-055\" 32 QnU'-I The model predicts that weekly expenditure on food rises by _ for each additional $100 weekly income. $1058 $9.62 $834 $195 The predicted weekly expenditure on food for a household with $1,500 weekly income is, $262.40 $252.30 $242.60 $233.2? SSE = _ 1,381,112 1,354,031 1,32?,481 1,301,452 The measure of closeness of fit, or measure of dispersion of observed expenditure on food around the regression line is, $152.68 $148.23 $143.91 $139.?2 The fraction of variations in food expenditure explained by weekly income is, 030?? 0.279? 0.2543 0.2312