For the following multiple regression output, answer the question (#7) below. Assessed value (1000s of $) = y Floor (sq. ft.) = x1 Number of offices = x2 Number of entrances = x3 Age of building = x4 Freeway (Access: 0 = no access; 1 = access) = x5 Regression Statistics Multiple R: 0.989 R Square: 0.977 Adjusted R Square: 0.969 Standard Error: 87.146 Observations: 20 ANOVA df: SS: MS: F: Significance F: Regression: 5 4601618.196 920323.639 121.185 5.1768E-11 Residual: 14 106321.004 7594.357 Total: 19 4707939.200 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept: -94.732 83.186 -1.139 0.274 -273.148 83.684 Floor (sq ft): 0.249 0.028 8.775 4.6E-07 0.188 0.309 Number of offices: 87.120 35.661 2.443 0.028 10.635 163.605 Number of entrances: 143.290 58.593 2.446 0.028 17.620 268.959 Age of building: -0.771 1.331 -0.579 0.572 -3.626 2.085 Freeway: 71.248 45.276 1.574 0.138 -25.858 168.355 7: What independent (explanatory) variables are not significant at the .05 significance level? a) Floor and age of building b) Age of building and number of offices c) Freeway and floor d) Freeway and age of building.
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For this question, use the following multiple regression output (which may differ from the output in other questions, even though the variables are the same) SUMMARY OUTPUT Regression Statistics Multiple R 0.534 R Square 0.285 Adjusted R Square 0.247 Standard Error 4791.473 Observations 100 ANOVA df SS MS F Significance F Regression 5 859571711.8 1.72E+08 7.488 5.940E-06 Residual 94 2158071843 22958211 Total 99 3017643555 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 3637.638 2700.353 1.347 0.1812 -1723.976 8999.252 Annual Income ($1000) 108.876 19.008 5.728 1.216E-07 71.135 146.617 Household Size 285.883 218.539 1.308 0.1940 -148.032 719.797 Education -200.021 320.557 -0.624 0.5342 -836.494 436.451 TV Hours -3.683 28.451 -0.129 0.8973 -60.175 52.808 Age -10.82 46.632 -0.232 0.817 -103.41 81.769 Find the predicted annual charges for a 44-year old customer with an annual income of $65 (thousand), a household size of 5, 2 years of post-high school education, 32 hours of watching television per week.
Lucas F.
Examine the Minitab output shown here for a multiple regression analysis. How many predictors were there in this model? Comment on the overall significance of the regression model. Discuss the t ratios of the variables and their significance. The regression equation is: y = 4.091 - 5.111x1 + 2.662x2 + 1.557x3 + 1.141x4 + 1.655x5 - 1.248x6 + 0.436x7 + 0.959x8 + 1.289x9 Predictor Coef Stdev T p Constant 4.096 1.2884 3.24 0.006 x1 -5.111 1.8700 2.73 0.011 x2 2.662 2.0796 1.28 0.212 x3 1.557 1.2811 1.22 0.235 x4 1.141 1.4712 0.78 0.445 x5 1.655 1.4994 1.10 0.281 x6 -1.248 1.2735 0.98 0.336 x7 0.436 0.3617 1.21 0.239 x8 0.959 1.1896 0.81 0.426 x9 1.289 1.9182 0.67 0.508 S = 3.437 R-sq = 40.8% R-sq(adj.) = 21.1% Analysis of Variance Source DF SS MS F p Regression 9 219.746 24.416 2.07 0.0697 Error 27 319.004 11.815 Total 36 538.750
Madhur L.
22. Assume you ran a multiple regression to gain a better understanding of the relationship between lumber sales, housing starts, and commercial construction. The regression uses lumber sales (in $100,000s) as the response variable with housing starts (in 1,000s) and commercial construction (in 1,000s) as the explanatory variables. The estimated model is Lumber Sales = β0 + β1 Housing Starts + β2 Commercial Constructions + ε. The following ANOVA table summarizes a portion of the regression results. df SS MS F Regression 2 180,770 90,385 103.3 Residual 45 39,375 875 Total 47 220,145 Coefficients Standard Error t-stat p-value Intercept 5.37 1.71 3.14 0.0030 Housing Starts 0.76 0.09 8.44 0.0000 Commercial Construction 1.25 0.33 3.78 0.0005 If Housing Starts were 17,000 and Commercial Construction was 3,200, the best estimate of Lumber Sales would be ________. Multiple Choice $16,920,000 $16,925,370 $22,014,000 $22,290,000
Banhishikha S.
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