Linear Regression for Business Statistics
Master linear regression techniques for business applications. Learn to build models, test hypotheses, and make predictions using Microsoft Excel.
Master linear regression techniques for business applications. Learn to build models, test hypotheses, and make predictions using Microsoft Excel.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Business Statistics and Analysis Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Build and estimate linear regression models using Microsoft Excel
Interpret regression coefficients and use models to make accurate predictions
Perform hypothesis testing and evaluate statistical significance using p-values
Develop models using categorical variables through dummy variable regression
Assess model quality using R-square and adjusted R-square measures
Implement advanced techniques including interaction effects and variable transformations
Skills you'll gain
This course includes:
4.4 Hours PreRecorded video
28 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course introduces linear regression as a powerful tool for business statistics and data analytics. Designed with a focus on practical application rather than mathematical derivation, the curriculum teaches students how to build, estimate, and interpret regression models using Microsoft Excel. The course is structured into four modules that progressively build expertise. Students begin by learning regression fundamentals, including model building, estimation, interpretation, and prediction. They then advance to hypothesis testing, understanding p-values, confidence intervals, and goodness-of-fit measures like R-square and adjusted R-square. The third module covers dummy variable regression for working with categorical variables and addresses multicollinearity issues. The final module explores advanced techniques including mean-centering variables, building confidence bounds for predictions, interaction effects, and variable transformations such as log-log and semi-log models. Throughout the course, concepts are reinforced through practical examples using real-world datasets, with a strong emphasis on Excel-based analysis that can be immediately applied in business contexts.
Regression Analysis: An Introduction
Module 1 · 7 Hours to complete
Regression Analysis: Hypothesis Testing and Goodness of Fit
Module 2 · 7 Hours to complete
Regression Analysis: Dummy Variables, Multicollinearity
Module 3 · 6 Hours to complete
Regression Analysis: Various Extensions
Module 4 · 5 Hours to complete
Fee Structure
Instructor
Associate Professor of Management
Sharad is a faculty of Management at the Jones Graduate School of Business at Rice University, Houston, Texas. He holds a Ph.D. from Carnegie Mellon University, and his expertise lies in Business statistics and data analytics.
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