1. The document discusses linear regression analysis and its use in quantifying the relationship between two continuous variables. 2. Linear regression finds the line of best fit to model the dependence of a dependent (response) variable on an independent (explanatory) variable. 3. The key aspects covered include estimating regression coefficients, testing the statistical significance of the regression model, and assessing the model's predictive power using the coefficient of determination.

2. Medical Statistics (full English class) Shaoqi Rao, PhD School of Public Health Sun Yat-Sen University Slides adapted from Dr. Ji-Qian Fang’s

3. Chapter 8 Linear Regression

7. 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)

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

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

15. 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