a) Draw these fitted regression lines on the same diagram. b) Suppose 'Good' credit is scored between 0-400 and 'Excellent' credit 400-800. Add to your fitted equations a cloud of points you might reasonably expect to see. c) Explain how you could combine the information from both of these univariate regressions by running a single (multivariate) linear regression. Deduce from the information provided what the parameter estimates would be in your multivariate regression. What is the estimated size of the 'jump'? (The 'jump' does not occur at 0). What is the estimated 'kink'?
a) Draw these fitted regression lines on the same diagram. b) Suppose 'Good' credit is scored between 0-400 and 'Excellent' credit 400-800. Add to your fitted equations a cloud of points you might reasonably expect to see. c) Explain how you could combine the information from both of these univariate regressions by running a single (multivariate) linear regression. Deduce from the information provided what the parameter estimates would be in your multivariate regression. What is the estimated size of the 'jump'? (The 'jump' does not occur at 0). What is the estimated 'kink'?
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
![7. Two analysts at a bank want to determine an appropriate credit limit for new customers with a
given credit score using existing data on credit limit decisions. The first analyst studies customers
with 'good' credit, and the second studies customers with 'excellent' credit. For the purpose of this
exercise, suppose these are the only two categories. The analysts separately report the following
fitted equations:
'Good' i
'Excellent' ŷi
=
=
1000 +0.5scorei.
1500 +0.7scoreį.
a) Draw these fitted regression lines on the same diagram.
b) Suppose 'Good' credit is scored between 0-400 and 'Excellent' credit 400-800. Add to your fitted
equations a cloud of points you might reasonably expect to see.
c) Explain how you could combine the information from both of these univariate regressions by
running a single (multivariate) linear regression. Deduce from the information provided what the
parameter estimates would be in your multivariate regression. What is the estimated size of the
‘jump'? (The ‘jump' does not occur at 0). What is the estimated 'kink'?](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F42f9d7c6-4c7a-44ab-8c36-1d379ff2d1eb%2Fb5dbe506-1395-4a8e-951d-f90986289607%2F1jpjmtq_processed.png&w=3840&q=75)
Transcribed Image Text:7. Two analysts at a bank want to determine an appropriate credit limit for new customers with a
given credit score using existing data on credit limit decisions. The first analyst studies customers
with 'good' credit, and the second studies customers with 'excellent' credit. For the purpose of this
exercise, suppose these are the only two categories. The analysts separately report the following
fitted equations:
'Good' i
'Excellent' ŷi
=
=
1000 +0.5scorei.
1500 +0.7scoreį.
a) Draw these fitted regression lines on the same diagram.
b) Suppose 'Good' credit is scored between 0-400 and 'Excellent' credit 400-800. Add to your fitted
equations a cloud of points you might reasonably expect to see.
c) Explain how you could combine the information from both of these univariate regressions by
running a single (multivariate) linear regression. Deduce from the information provided what the
parameter estimates would be in your multivariate regression. What is the estimated size of the
‘jump'? (The ‘jump' does not occur at 0). What is the estimated 'kink'?
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