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Interpret the coefficient for tenure in terms of percentage change in odds of churning (tenure is calculated/recorded in years).

Interpret the coefficient for tenure in terms of percentage change in odds of churning (tenure is calculated/recorded in years).

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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 Interpret the coefficient for tenure in terms of percentage change in odds of churning (tenure is calculated/recorded in years).

The image displays a table labeled "Coefficients and Statistics" from a Logistic Regression Learner node. The table outlines various variables along with their respective coefficients. Here's a breakdown of the data presented: - **Variables**: These include demographic and service-related features such as gender, senior citizenship, tenure, various internet and streaming services, contract types, payment methods, monthly charges, and total charges. - **Logit**: This represents the log odds, though specific values are not detailed in the table. - **Coefficients**: Each variable is associated with a coefficient value which indicates the weight or impact of the variable in the model. Key coefficients highlighted include: - Gender (Male): -0.004 - Senior Citizen: 0.31 - Dependents (Yes): -0.079 - Tenure: 0.057 - Various internet and streaming service subscriptions showing a range of positive and negative impacts. - Contract and payment methods each have varied impacts, with "Contract=Two year" showing a significant positive value (1.259). - Monthly Charges and Total Charges have larger coefficients of 0.033 and 0.005, respectively. - The constant for the model is 1.383. These coefficients help in understanding the influence of each factor on the logistic regression model outcomes, such as customer retention or churn probability. Differences in coefficients indicate how much each variable contributes to the log odds of the dependent variable.
Transcribed Image Text:The image displays a table labeled "Coefficients and Statistics" from a Logistic Regression Learner node. The table outlines various variables along with their respective coefficients. Here's a breakdown of the data presented: - **Variables**: These include demographic and service-related features such as gender, senior citizenship, tenure, various internet and streaming services, contract types, payment methods, monthly charges, and total charges. - **Logit**: This represents the log odds, though specific values are not detailed in the table. - **Coefficients**: Each variable is associated with a coefficient value which indicates the weight or impact of the variable in the model. Key coefficients highlighted include: - Gender (Male): -0.004 - Senior Citizen: 0.31 - Dependents (Yes): -0.079 - Tenure: 0.057 - Various internet and streaming service subscriptions showing a range of positive and negative impacts. - Contract and payment methods each have varied impacts, with "Contract=Two year" showing a significant positive value (1.259). - Monthly Charges and Total Charges have larger coefficients of 0.033 and 0.005, respectively. - The constant for the model is 1.383. These coefficients help in understanding the influence of each factor on the logistic regression model outcomes, such as customer retention or churn probability. Differences in coefficients indicate how much each variable contributes to the log odds of the dependent variable.
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