This document discusses linear regression analysis. It begins by introducing linear regression and its goal of exploring quantitative dependence between two continuous variables. It then defines independent and dependent variables, and explains that linear regression finds the line that best fits the data points by minimizing the sum of squared residuals. The document provides formulas for calculating the regression coefficients and discusses hypothesis testing and determining if a linear regression model is statistically significant. It concludes by noting some assumptions and limitations of linear regression analysis.

1. Chapter 9 Linear Regression

5. What is regression in statistics? To find out the track of the means 100 120 140 160 180 200 220 100 120 140 160 180 200 220 Father ’ s height （ cm ） Son’s height (cm)

9. Example 8.1 Calculate the regression equation of the height of son Y on the height of father X .

12. Statistic Standard deviation of regression coefficient Standard deviation of residual

13. For Example 8.1 p <0.001 . Reject ---- the regression of the son’s height on the father’s height is statistically significant. :  =0, :  ≠0