In this lesson of squares regression, we are going to talk about how to actually calculate a regression line and find it. First, we are going to talk about what it means to best fit the data, and what does it mean for a line to best fit the data. We are also going to talk about sum of squared errors and why that concept is important for regression. Then we are going to talk about some quantitative properties of the regression line. We will see how to actually find the slope and the intercept of the regression line and we will do some examples to sum up.
Lecture Slides are screen-captured images of important points in the lecture. Students can download and print out these lecture slide images to do practice problems as well as take notes while watching the lecture.
This book provides a clear and methodical approach to essential statistical procedures. It clearly explains the basic concepts and procedures of descriptive and inferential statistical analysis. This book features a new emphasis on expressions involving sums of squares and degrees of freedom as well as a stronger stress on the importance of variability.