Regression Analysis Methods and Code Examples for Various Business Scenarios

This article presents regression analysis as a statistical method for exploring relationships between variables and making predictions. It covers multiple business scenarios with specific regression techniques and corresponding Python code examples using the statsmodels library.

The e-commerce scenario demonstrates linear regression to analyze the relationship between advertising expenses and sales volume, using OLS (Ordinary Least Squares) modeling with pandas for data handling.

Market research applications show polynomial regression for examining the relationship between product price and sales volume, incorporating polynomial features using numpy's column_stack function.

Healthcare scenarios utilize logistic regression to study the relationship between patient age and disease risk, employing the Logit model from statsmodels for binary classification problems.

Financial applications demonstrate time series regression analysis to examine the relationship between stock returns and market indices, using standard OLS regression for financial modeling.

Social media analysis employs Poisson regression to study the relationship between user follower counts and content likes, using the GLM (Generalized Linear Model) with Poisson family distribution.

Human resources scenarios apply ridge regression to analyze the relationship between employee training hours and performance, using regularized regression with L2 penalty to handle multicollinearity.

Educational applications demonstrate weighted least squares regression to study the relationship between study time, sleep time, and exam scores, incorporating weights based on study time variability.

Hotel industry analysis uses multivariate regression to examine the relationship between room prices, location scores, and customer ratings, using multiple independent variables in OLS modeling.

Marketing scenarios apply generalized linear model regression to analyze the relationship between advertising spend, promotions, and sales revenue, using Gaussian family distribution for continuous outcomes.

Logistics applications demonstrate additive model regression to study the relationship between shipment distance, weight, and delivery time, using standard OLS regression for transportation modeling.

Each scenario includes complete Python code examples with data loading using pandas, model construction, fitting procedures, and result printing using the summary() method for comprehensive statistical output.