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Interpret the coefficient of promotion deal and explain your findings

Interpret the coefficient of promotion deal and explain your findings

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 of promotion deal and explain your findings
**Final Model (Phase 1) Analysis** **Dependent Variable**: Likelihood_Revisit | Independent Variable | Coefficient (p-value) | Standardized Beta | |----------------------------------|-----------------------|--------------------| | Promotion deal | 1.846 (0.000) | 0.413 *** | | Complain Freq | -3.819 (0.000) | -0.218 *** | | Promotion deal X Complain Freq | 0.485 (0.000) | 0.141 *** | | Study (=1) | 1.670 (0.157) | 0.036 | | Promotion deal X Study (=1) | -0.271 (0.037) | -0.046 * | | Transaction Amount | 1.634 (0.000) | 0.682 *** | | Male | 0.217 (0.697) | 0.005 | | Heavy | 0.830 (0.183) | 0.017 | | Constant | 10.18 (0.000) | | - **Sample Size (N)**: 122 - **Prob > F**: 0.000 - **R²**: 0.985 - **Adjusted R²**: 0.984 **P-values Key**: - *p < 0.05 - **p < 0.01 - ***p < 0.001 **Explanation**: - This table presents a regression analysis model exploring factors affecting the likelihood of revisiting a service or product. - Key variables with strong positive impact include **Promotion deal** and **Transaction Amount**, indicated by significant positive coefficients and high standardized betas. - The **Complain Freq** shows a significant negative impact, reducing the likelihood of a revisit. - Interaction terms such as **Promotion deal X Complain Freq** and **Promotion deal X Study** show how combinations of variables can affect outcomes, with the latter showing a slight negative effect. - Gender (Male) and frequency of usage (Heavy) are not significant predictors in this model based on their p-values. - The constant term provides the baseline likelihood of revisit when all other variables are zero. This analysis is critical in understanding customer behavioral patterns
Transcribed Image Text:**Final Model (Phase 1) Analysis** **Dependent Variable**: Likelihood_Revisit | Independent Variable | Coefficient (p-value) | Standardized Beta | |----------------------------------|-----------------------|--------------------| | Promotion deal | 1.846 (0.000) | 0.413 *** | | Complain Freq | -3.819 (0.000) | -0.218 *** | | Promotion deal X Complain Freq | 0.485 (0.000) | 0.141 *** | | Study (=1) | 1.670 (0.157) | 0.036 | | Promotion deal X Study (=1) | -0.271 (0.037) | -0.046 * | | Transaction Amount | 1.634 (0.000) | 0.682 *** | | Male | 0.217 (0.697) | 0.005 | | Heavy | 0.830 (0.183) | 0.017 | | Constant | 10.18 (0.000) | | - **Sample Size (N)**: 122 - **Prob > F**: 0.000 - **R²**: 0.985 - **Adjusted R²**: 0.984 **P-values Key**: - *p < 0.05 - **p < 0.01 - ***p < 0.001 **Explanation**: - This table presents a regression analysis model exploring factors affecting the likelihood of revisiting a service or product. - Key variables with strong positive impact include **Promotion deal** and **Transaction Amount**, indicated by significant positive coefficients and high standardized betas. - The **Complain Freq** shows a significant negative impact, reducing the likelihood of a revisit. - Interaction terms such as **Promotion deal X Complain Freq** and **Promotion deal X Study** show how combinations of variables can affect outcomes, with the latter showing a slight negative effect. - Gender (Male) and frequency of usage (Heavy) are not significant predictors in this model based on their p-values. - The constant term provides the baseline likelihood of revisit when all other variables are zero. This analysis is critical in understanding customer behavioral patterns
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