1. 1 Simple Regression ( 簡單迴歸分析 ) Social Research Methods 2109 & 6507 Spring, 2006 March 8, 9, 13, 2006

2. 2 From Correlation to Regression: Correlation ( 相關分析、相關係數 ): measures the strength of linear association between 2 quantitative variables ( 二變數線性關係的強度 ) Regression ( 迴歸分析 ): 1.Description ( 描述 ): summarize the relationship between the two variables with a straight line, what does the line look like? ( 如何用一直線描 述二變數的關係 ?) 2.Prediction ( 預測 ): how to make predictions about one variable based on another? ( 如何從 一變數預測另一變數 ?)

3. 3 Example: summarize the relationship with a straight line

4. 4 Draw a straight line, but how? ( 怎麼畫 那條直線 ?)

5. 5 Notice that some predictions are not complete accurate

6. 6 How to draw the line? Purpose: draw the regression line to give the most accurate predictions of y given x Criteria for “accurate”: Sum of (observed y – predicted y) 2 = sum of (prediction errors) 2 [ 觀察值與估計值之差的平方和 ] Called the sum of squared errors or sum of the squared residuals (SSE)

7. 7 Ordinary Least Squares (OLS) Regression ( 普通最小平方法 ) The regression line is drawn so as to minimize the sum of the squared vertical distances from the points to the line ( 讓 SSE 最小 ) This line minimize squared predictive error This line will pass through the middle of the point cloud ( 迴歸線從資料群中間穿 過 )(think as a nice choice to describe the relationship)

8. 8 To describe a regression line (equation): Algebraically, line described by its intercept ( 截 距 ) and slope ( 斜率 ) Notation: y = the dependent variable x = the independent variable y_hat ( )= predicted y based on the regression line β = slope of the regression line α= intercept of the regression line

9. 9 The meaning of slope and intercept: slope = change in (y_hat) for a 1 unit change in x (x 一單位的改變導 致 y 估計值的變化 ) intercept = value of (y_hat) when x is 0

10. 10 General equation of a regression line: (y_hat) = α +βx where α and β are chosen to minimize: sum of (observed y – predicted y) 2 A formula for α and β which minimize this sum is programmed into statistical programs and calculators

11. 11 An example of a regression line

12. 12 Residuals ( 殘差 ) Residual = difference between the predicted y and the observed y for an observation residual i = y i – (y_hat) i

13. 13 Interpreting regression coefficients Slope = change in y predicted with a one unit change in x –Slope = 0: no linear relationship between x and y (r = 0) Intercept = predicted value of y when x is 0 –Often, we are not interested in the intercept Note: interpretation of the slope and intercept requires thinking in the units of x and y ( 解釋截距 與斜率時要注意到 x and y 的單位 )

14. 14 Regression and Correlation Distinct but related measures Correlation: measures strength of relationship, a major aspect of which is how closely the points form a line shape Regression slope: how steep is the slope of the line?

15. 15 To get slope and intercept for a regression:

16. 16 How slope and correlation are mathematically related: β = r (s y )/ (s x ) α = (y_bar) – β(x_bar)

17. 17 Fit: how much can regression explain? ( 迴歸能解釋 y 多少的變異？ ) Look at the regression equation again: (y_hat) = (y_hat) = α +βx y = α +βx + ε Data = what we explain + what we don’t explain Data = predicted + residual ( 資料有我們不能解釋的與可解釋的部分，即 能預估的與誤差的部分）

18. 18 In regression, we can think “fit” in this way: Total variation = sum of squares of y explained variation = total variation explained by our predictions unexplained variation = sum of squares of residuals R 2 = (explained variation)/ (total variation) （判定係數） [y 全部的變易量中迴歸分析能解釋的部分 ]

19. 19 R 2 = r 2 NOTE: a special feature of simple regression (OLS), this is not true for multiple regression or other regression methods. [ 注意：這是簡單迴歸分析的特性，不 適用於多元迴歸分析或其他迴歸分析 ]

20. 20 Some cautions about regression and R 2 It’s dangerous to use R 2 to judge how “good” a regression is. ( 不要用 R 2 來判斷迴 歸的適用性 ) –The “appropriateness” of regression is not a function of R 2 When to use regression? –Not suitable for non-linear shapes [you can modify non-linear shapes] – regression is appropriate when r (correlation) is appropriate as a measure

21. 21 Residuals and residual plots residual i = y i – (y_hat) I We can use residual plots to help us assess the fit of a regression line A residual plot: a scatterplot of the regression residuals against the explanatory variable ( 殘差在 y 軸，自變數 在 x 軸 )

22. 22 Example of a residual plot

23. 23 Look at a residual plot 殘差 (residuals) 的分布是否平均散佈在 0 的 上面及下面？ 對整個自變數的分佈而言，殘差的垂直分 佈 (vertical spread) 是否都差不多？

24. 24 Types of residual plots

25. 25 Outliers and influences Outlier ( 極端值 ): a point that falls outside the overall patterns of the graph Influential observation ( 深具影響的觀察值 ) = a point which, if removed, would markedly change the position of the regression line NOTE: Outliers are not necessarily influential.

26. 26 The differences between outliers and influential outliers

27. 27 Outliers and influential observations Outliers which are at the extremes of x are more likely to be influential than those are at the extremes of y ( 自變數的極端值比依 變數的極端值較有可能是對迴歸影響力大 的觀察值 ) It is often a good idea to eliminate any influential outliers and recompute our regression without them.( 建議 : 將對迴歸影 響力大的觀察值刪除，再計算一次迴歸線 )

28. 28 Cautions about correlation and regression: Extrapolation is not appropriate Regression: pay attention to lurking or omitted variables –Lurking (omitted) variables: having influence on the relationship between two variables but is not included among the variables studied –A problem in establishing causation Association does not imply causation. –Association alone: weak evidence about causation –Experiments with random assignment are the best way to establish causation.