Understanding and Applying Linear Regression
Pluralsight
Course Summary
Linear regression is a powerful tool, applicable in many common situations in business and data analysis. This course will cover both the theory and implementation of linear regression in Excel, Python, and R.
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Course Description
Linear regression is a key technique used in forecasting and in quantifying cause-effect relationships. In this course, Understanding and Applying Linear Regression, you will learn how to identify patterns in data and test those relationships for statistical soundness. You will also learn simple regression and multiple regression. Finally, you'll explore the use of categorical variables. When you're finished with this course, you will have a strong applied knowledge of regression in Excel, R, and Python that will help with factor analysis, logistic regression, and other powerful techniques.
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Course Syllabus
Course Overview- 2m 2s
—Course Overview 2m 2sModeling Relationships Between Variables Using Regression- 32m 15s
—Connecting the Dots Using Linear Regression 6m 26s
—Reasons for Using Regression 6m 18s
—Versatility of Regression 2m 46s
—Regression as Machine Learning 5m 47s
—Mean and Variance 3m 28s
—Probability Distributions and the Bell Curve 7m 28sUnderstanding Simple Regression Models- 36m 14sImplementing Simple Regression Models in Excel- 30m 3sImplementing Simple Regression Models in R- 24m 49sImplementing Simple Regression Models in Python- 16m 38sUnderstanding Multiple Regression Models- 40m 47sImplementing Multiple Regression Models in Excel- 39m 28sImplementing Multiple Regression Models in R- 15m 1sImplementing Multiple Regression Models in Python- 15m 16s
