Simple Linear Regression, Least Squares Method, Residuals, and R-Squared are crucial in predicting values based off historical data. This video dives into how to set up a Simple Linear Regression from a Scatter Plot, how to plot the Line of Best Fit, minimize errors and maximize predicting ability through Ordinary Least Squares. It will also go throguh why we validate it with R Squared, Residual Plots and QQ Plots, and how we can predict expected values all in an accessibile story format. This video is perfect for those who are studying for exams and want to undertand why we need these steps, or for someone looking to dive into the material and truly understand it. Timestamps: 0:00 - Linear Regression Motivation 0:45 - Building the Scatterplot and Defining Variables 1:15 - Building the Simple Linear Regression Model 2:00- Interpreting our Regression Equation 3:15- Error Minimizing Through Ordinary Least Squares 4:25 - Looking at Goodness-of-Fit With R-Squared (R^2) 5:00 - Residual Plots, QQ Plots, And Model Assumptions 6:08 - Predicting Values with Linear Regression 6:35 - Correlation Vs Causation, and the 3rd Variable Problem 7:33 - Applications and Real World Examples This video does build on a concept I have talked about in the past. If you are unsure about it, definitely check it out! Normal Distribution - Here are some more videos not related but that you may find interesting: Standard Deviation - Confidence Intervals - Central Limit Theorem - Like, comment, and subscribe for more! #statistics #mathematics #education #dataanalysis #datascience #linearregression #scatterplot