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Answer the following questions based on the attached MINITAB output Y= Purchased car during the year (yes/no) X1= Family Yearly Income (in thousands) X2= Age of oldest family automobile   a) suppose that a family owns a sigle car that is 3 years old and has income of $45000 per year. Provide a statement estimating the chance they'll buy a new car this year. b) Another model using 5 additional variables produced these numbers. WHich models would you choose and why? Deviance Devicance   R-sq R-sq (adj) AIC 19.44% 13.98% 40.69   e) Draw whatever concludions seem appropriate about the impact of the age of the families oldest car. Include in your comments any reccomendation for further study that might be appropriate.

Answer the following questions based on the attached MINITAB output Y= Purchased car during the year (yes/no) X1= Family Yearly Income (in thousands) X2= Age of oldest family automobile   a) suppose that a family owns a sigle car that is 3 years old and has income of $45000 per year. Provide a statement estimating the chance they'll buy a new car this year. b) Another model using 5 additional variables produced these numbers. WHich models would you choose and why? Deviance Devicance   R-sq R-sq (adj) AIC 19.44% 13.98% 40.69   e) Draw whatever concludions seem appropriate about the impact of the age of the families oldest car. Include in your comments any reccomendation for further study that might be appropriate.

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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Answer the following questions based on the attached MINITAB output

Y= Purchased car during the year (yes/no)

X1= Family Yearly Income (in thousands)

X2= Age of oldest family automobile

 

a) suppose that a family owns a sigle car that is 3 years old and has income of $45000 per year. Provide a statement estimating the chance they'll buy a new car this year.

b) Another model using 5 additional variables produced these numbers. WHich models would you choose and why?

Deviance Devicance  
R-sq R-sq (adj) AIC
19.44% 13.98% 40.69

 

e) Draw whatever concludions seem appropriate about the impact of the age of the families oldest car. Include in your comments any reccomendation for further study that might be appropriate. 

Binary Logistic Regression: Purchase versus Income, Age (Output Problem 3) Age 0 Method Link function Logit Rows used 33 Response Information Variable Value Count Purchase Yes No Goodness-of-Fit Tests Income 45 29 Test Deviance Pearson DF Chi-Square P-Value 30 36.69 0.186 30 33.62 0.296 Hosmer-Lemeshow 8 6.80 0.558 Analysis of Variance Wald Test Source DF Chi-Square P-Value Regression 2 Income 1 Age 1 10 20 14 (Event) 19 Scatterplot of Age vs Income …... 30 Age 3 15 5.83 0.054 5.83 0.016 2.36 0.125 40 Prediction for Purchase Regression Equation 50 Income 60 70 P (Yes) = exp (Y¹) / (1 + exp(Y')) Y' = - 4.74 +0.0677 Income + 0.599 Age 80 ¹8 Regression Equation P(Yes) exp(Y)/(1+ exp(Y')) Y' = -4.74 +0.0677 Income + 0.599 Age Coefficients = Term Coef SE Coef Z-Value P-Value VIF Constant -4.74 2.10 -2.25 2.41 1.53 Income 0.0677 0.0281 Age 0.599 0.390 Model Summary Deviance Deviance Area Under R-Sq R-Sq(adj) AIC AICC BIC ROC Curve 18.44% 14.00% 42.69 43.52 47.18 0.7575 Odds Ratios for Continuous Predictors Odds Ratio 95% CI 1.0701 (1.0128, 1.1306) 1.8196 (0.8471, 3.9085) Income Age Fits and Diagnostics for Unusual Observations 0.024 0.016 1.67 0.125 1.67 Observed Obs Probability Fit Resid Std Resid 29 1.000 0.114 2.085 2.17 R R Large residual 95% CI Fitted Probability SE Fit 0.526114 0.108932 (0.320429, 0.723302) 0.997984 0.009049 (0.068589, 1.000000)
Transcribed Image Text:Binary Logistic Regression: Purchase versus Income, Age (Output Problem 3) Age 0 Method Link function Logit Rows used 33 Response Information Variable Value Count Purchase Yes No Goodness-of-Fit Tests Income 45 29 Test Deviance Pearson DF Chi-Square P-Value 30 36.69 0.186 30 33.62 0.296 Hosmer-Lemeshow 8 6.80 0.558 Analysis of Variance Wald Test Source DF Chi-Square P-Value Regression 2 Income 1 Age 1 10 20 14 (Event) 19 Scatterplot of Age vs Income …... 30 Age 3 15 5.83 0.054 5.83 0.016 2.36 0.125 40 Prediction for Purchase Regression Equation 50 Income 60 70 P (Yes) = exp (Y¹) / (1 + exp(Y')) Y' = - 4.74 +0.0677 Income + 0.599 Age 80 ¹8 Regression Equation P(Yes) exp(Y)/(1+ exp(Y')) Y' = -4.74 +0.0677 Income + 0.599 Age Coefficients = Term Coef SE Coef Z-Value P-Value VIF Constant -4.74 2.10 -2.25 2.41 1.53 Income 0.0677 0.0281 Age 0.599 0.390 Model Summary Deviance Deviance Area Under R-Sq R-Sq(adj) AIC AICC BIC ROC Curve 18.44% 14.00% 42.69 43.52 47.18 0.7575 Odds Ratios for Continuous Predictors Odds Ratio 95% CI 1.0701 (1.0128, 1.1306) 1.8196 (0.8471, 3.9085) Income Age Fits and Diagnostics for Unusual Observations 0.024 0.016 1.67 0.125 1.67 Observed Obs Probability Fit Resid Std Resid 29 1.000 0.114 2.085 2.17 R R Large residual 95% CI Fitted Probability SE Fit 0.526114 0.108932 (0.320429, 0.723302) 0.997984 0.009049 (0.068589, 1.000000)
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