# Regression Math: Getting Started with Linear Regression

Math    |    Beginner
• 14 videos | 1h 35m 54s
• Includes Assessment
Rating 4.7 of 3 users (3)
Linear Regression analysis is a simple yet powerful technique for quantifying cause and effect relationships. Use this course to get your head around linear regression as the process of fitting a straight line through a set of points. Learn how to define residuals and use the least square error. Define and measure the R-squared, implement regression analysis, visualize your data by computing a correlation matrix and plotting it in the form of a correlation heatmap, and use scatter plots as a prelude to performing the regression analysis. Finish by implementing the regression analysis first using functions that you write yourself and then using the scikit-learn python library. By the end of the course, you'll be able to identify the need for linear regression and implement it effectively.

## WHAT YOU WILL LEARN

• Discover the key concepts covered in this course
Define linear regression and outline how regression is used in prediction
Outline how residuals are used in regression
Describe what's meant by the least square error
Compute the best fit using partial derivatives
Calculate r-squared of a regression model
Summarize what comprises the normal equation
• Visualize correlations of features
Split train and test data and create computations
Manually define a regression line
Perform regression and view the predicted values
View the r-squared and residuals in regression
Implement regression models using libraries
Summarize the key concepts covered in this course

## IN THIS COURSE

• 3.  Residuals in Regression
• 4.  The Computation of "The Best Fit"
• 5.  Partial Derivatives with Regression Models
• 6.  Calculating R-squared
• 7.  The Normal Equation
• 8.  Setting up Data and Viewing Correlations
• 9.  Splitting Data for Regression
• 10.  Defining the Slope and Intercept for Regression
• 11.  Creating a Regression Line and Predictions
• 12.  Viewing the Performance of a Regression Model
• 13.  Performing Regression with Built-in Modules
• 14.  Course Summary

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