Linear Regression Fundamentals and Applications

Linear regression analysis is critical for understanding and defining the strength of the relationship between variables. This analysis can be used to make predictions for a variable given the value of another known variable. This course provides an overview of linear regression. You will learn how linear regression works, how to build effective linear regression models and how to use and interpret the information these models give us. In addition to the theory, we will perform linear regression on real data using both Excel and Python. The practical cases you will work through will be similar to those you might encounter in a business setting. Upon completing this course, you will be able to: • Define linear regression and its applications • Perform simple “pen and paper” regression calculations in Excel • Apply Excel’s RegressIt plugin to solve advanced regression calculations • Construct linear regression models in Python using both statsmodels and sklearn modules • Explain the implicit assumptions behind linear regression • Interpret regression outputs such as coefficients and p-values • Recommend various regression techniques when appropriate Regression is the critical tool used for making inferences or predictions based on the relationships between variables. Whether you’re working as a business leader or data analyst, the theory and practical toolsets taught in this course will serve you throughout your career. No background in coding with Python is required for this course. Common career paths for students who take the BIDA™ program are Business Intelligence, Asset Management, Data Analyst, Quantitative Analyst, and other finance careers

• Define linear regression and its applications
• Perform simple pen and paper regression calculations in Excel
• Apply Excel’s RegressIt plugin to solve advanced regression calculations
• Construct linear regression models in Python using statsmodels and sklearn
• Explain implicit assumptions behind linear regression
• Interpret regression outputs such as coefficients and p-values

• Prior hands-on experience with the core concepts covered in this course
• Comfort applying the main tools or methods independently