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
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
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.
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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