What will you in the Linear Regression for Business Statistics Course
Understand the basics of linear regression in a business context.
Learn to build and estimate regression models using Microsoft Excel.
Interpret regression coefficients, p-values, and R-squared values.
Use dummy variables to handle categorical inputs in regression.
Identify and address issues like multicollinearity and interaction terms.
Apply regression to solve real-world business problems.
Program Overview
1. Introduction to Regression
Duration: 2 hours
Overview of regression models and their role in business analytics.
Use Excel to estimate and visualize regression equations.
Understand residuals and basic prediction.
2. Hypothesis Testing and Model Evaluation
Duration: 2 hours
Learn hypothesis testing in the context of regression.
Interpret key output statistics like t-tests and R-squared.
Begin working with categorical data using dummy variables.
3. Dummy Variables and Multicollinearity
Duration: 2.5 hours
Explore more complex models involving multiple categorical variables.
Handle multicollinearity issues and understand their implications.
Practice interpreting models with multiple predictors.
4. Advanced Topics in Regression
Duration: 2 hours
Work with interaction effects and centered variables.
Build confidence intervals for predicted values.
Analyze practical examples using regression in various business functions.
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Job Outlook
Business Analysts: Improve forecasting and strategic analysis using regression tools.
Financial Analysts: Apply regression to model risk, pricing, and financial behavior.
Marketing Professionals: Use regression to assess customer behavior and campaign results.
HR & Operations Managers: Leverage regression to evaluate performance and resource use.
Consultants: Support decision-making with data-driven regression analysis.
Specification: Linear Regression for Business Statistics
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