11 Logistic Regression Output – Example 2 A credit card company wants to predict if an existing card holder will upgrade (success) or not using independent variables Purchases (in thousands) - the card holder's purchases from last year and Platinum Profile (1 if card holder fits credit card's policy; 0 if they do not fit policy) Find the Point Estimate Probability p($42.572,1) (Note: $42.572 is what we used when purchases were $42,572 in thousands. 1 means card holder fits policy) Find the Point Estimate Probability p($51.835,0) (Note: $51.5835 is what we used when purchases were $51,835 in thousands. O means card holder does not fit policy) Parameter Estimates Term Estimate Std Error 4.0818938 Intercept -9.9524146 Purchases 0.20983066 0.0907079 PlatProfile 4.14786846 1.6101814
11 Logistic Regression Output – Example 2 A credit card company wants to predict if an existing card holder will upgrade (success) or not using independent variables Purchases (in thousands) - the card holder's purchases from last year and Platinum Profile (1 if card holder fits credit card's policy; 0 if they do not fit policy) Find the Point Estimate Probability p($42.572,1) (Note: $42.572 is what we used when purchases were $42,572 in thousands. 1 means card holder fits policy) Find the Point Estimate Probability p($51.835,0) (Note: $51.5835 is what we used when purchases were $51,835 in thousands. O means card holder does not fit policy) Parameter Estimates Term Estimate Std Error 4.0818938 Intercept -9.9524146 Purchases 0.20983066 0.0907079 PlatProfile 4.14786846 1.6101814
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.5: Correlation And Causation
Problem 15PPS
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Transcribed Image Text:11
Logistic Regression Output – Example 2
A credit card company wants to predict if an existing card holder will upgrade
(success) or not using independent variables Purchases (in thousands) - the card
holder's purchases from last year and Platinum Profile (1 if card holder fits credit
card's policy; 0 if they do not fit policy)
Find the Point Estimate Probability p($42.572,1)
(Note: $42.572 is what we used when purchases were $42,572 in thousands. 1 means card
holder fits policy)
Find the Point Estimate Probability p($51.835,0)
(Note: $51.5835 is what we used when purchases were $51,835 in thousands. O means card
holder does not fit policy)
Parameter Estimates
Term
Estimate
Std Error
4.0818938
Intercept -9.9524146
Purchases 0.20983066 0.0907079
PlatProfile 4.14786846 1.6101814
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