2.1: Inferences about β 1

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1 Chapter 2 1

2 2.1: Inferences about β 1 Test of interest throughout regression: Need sampling distribution of the estimator b 1. Idea: If b 1 can be written as a linear combination of the responses (which are independent and normally distributed), then from A.40, we will now have the probability (sampling) distribution of b 1! 2

3 Easy: where: So immediately from (A.40): b 1 ~ Normal (E{b 1 }, Var{b 1 }) But we still need to find E{b 1 } and Var{b 1 } 3

4 Fun facts about the k i Fun Fact 1. Fun Fact 2. Fun Fact 3. 4

5 Using the fun facts, find E{b 1 } and Var{b 1 } =? 5

6 Major Results b 1 ~ Normal (β 1, σ 2 ) So it follows from (A.59) that: 6

7 Picture of t density functions with various degrees of freedom (df) f(t) t 7

8 t distribution Ratio of a standard normal to the square root of a scaled chi-squared distribution with n degrees of freedom. n of about 30 is close to a standard normal but not exactly so n, tends to a normal distribution n = 1, we get a t with 1 degree of freedom, otherwise known as the distribution. 8

9 t distribution Ratio of a standard normal to the square root of a scaled chi-squared distribution with n degrees of freedom. n of about 30 is close to a standard normal but not exactly so n, tends to a normal distribution n = 1, we get a t with 1 degree of freedom, otherwise known as the distribution. 9

10 Confidence interval for β 1 From the sampling distribution of b 1 : Rearranging inside the brackets: Result: 10

11 Hypothesis Tests for β 1 Step 1: Null and alternative hypotheses: Step 2: Test statistic: Step 3: Critical Region (or see p-value): 11

12 S = sqrt (2384) = Var(b 1 ) = sqrt (2384/19800) = t = 3.57 / = = sqrt ( ) Note that = Var(X) times 24 12

13 2.3 Considerations for Inferences on β 0 and β 1 Non-normality of errors Small departure not too big of a deal For very large n, asymptotically okay Confidence coefficients interpretation The predictor variables are fixed (not random) X i spacing impacts variance of b 1 Power computable via non-centrality parameter of a t-distribution 13

14 Predictions and their uncertainties Mean function at given value of the predictor variable (with confidence limits) For many future lots of size 80, what should the average number of hours be? Future response at given value of the predictor variable (with prediction limits) A client dropped off an order of size 80, what do we expect for the number of hours this order will take? 14

15 2.4--Interval Estimation of E{Y h } Point estimate for the mean at X = X h : For the interval estimate for the mean at X = X h, we require the sampling distribution of Y ^ h : 1. Distribution? 2. Mean? 15

16 Variance: Show Y h is a linear combination of the responses: ^ Y h is b 0 + b 1 X h ; the b 0 term is y-bar b 1 x-bar ^ 16

17 17

18 18

19 i.e., a good idea to show b 1 and b 2 are linear combinations of the Y i s 19

20 Results: Mean: Variance: Estimated Variance: Result: 20

21 Major Result Estimated variance of prediction: So: 21

22 1 α confidence limits for E{Y h }: Question: At which value of X h is this confidence interval the smallest? (i.e., where is your estimation most precise?) 22

23 Predicting a Future Observation at X h ^ We will use Y h, and our prediction error will be: ^ pred = Y h(new) - Y h The variance of our prediction error is: Estimated by: 23

24 1 α confidence limits for E{Y h(new) }: where: 24

25 Example: GPA data for 2000 CH01PR19 25

26 JMP Pro 11 Analysis 26

27 Obtain Estimation Interval and Prediction Interval at X h = 25 And do all of the other X h -points while you re at it. 27

28 28

29 Easy with Fit Y by X 29

30 Y hours = *X lot size Summary of Fit RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) 25 Analysis of Variance Source DF Sum of Squares Mean Square F Ratio Model Error C. Total Prob > F <.0001* Parameter Estimates Term Estimate Std Error t Ratio Prob> t Intercept * X lot size <.0001* S = sqrt (2384) = St. Dev.(b 1 ) = sqrt (2384/19800) = t = 3.57 / = = sqrt ( ) Note that = Var(X) times 24 30

31 Lot size, variance is *24 = 19,800 31

32 Variance 825 times 24 = 19,800 32

33 Partitioning total sums of squares 33

34 Variation Explained by Regression Unexplained variation before reg. Unexplained variation after reg. So what variation was explained by regression? SSR = SSTO - SSE Amazing fact: 34

35 Aside: Show SSTO = SSR + SSE But: So: =? 35

36 Mean Squares 36

37 ANOVA Table 37

38 F test Test Statistic: Decision Rule: 38

39 Result: F = t 2 See text page 71. t is a Normal/sqrt(Chi-sq) so if you square t you get 39

40 Example: GPA 2000 Data Linear Fit GPA = *ACT Summary of Fit RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) Analysis of Variance Sum of Source DF Squares Mean Square F Ratio Model Error Prob > F C. Total * Parameter Estimates Term Estimate Std Error t Ratio Prob> t Intercept <.0001* ACT * F* = Critical value at.05 level: 40

41 2.8---General Linear Test Approach Compares any full and reduced models and answers the question: Do the additional terms in the full model explain significant additional variation? (Are they needed?) Examples: Full Model Reduced Model 41

42 General Linear Test Approach How much additional variation is explained by the full model? Result: Amazingly general test H 0 : Reduced model is true (R) H a : Full model is true (F) 42

43 Example: Test: β 1 = 0 versus β 1 0 Full model: Y i = β 0 + β 1 X i + ε i corresponding SSE(F) SSE Reduced model: Y i = β 0 + ε i (model under H 0 ) corresponding SSE(R) SSTO Test statistic is F = MSR/MSE (see pages 72-73) 43

44 2.9--Measures of Association R 2 : Coefficient of determination Variation explained by the regression is: Total variation is: SSTO What fraction of total variation was explained by the regression? R 2 = SSR/SSTO = 1 - SSE/SSTO (Rsquare) (Over-used, over-rated, possibly misleading statistic!) do not confuse with causation. As a screening tool in model selection, it is helpful. 44

45 Correlation illustrations 45

46 R 2? Y Y Y Y X

47 Answers: S = R-Sq = 97.7% R-Sq(adj) = 97.6% S = R-Sq = 63.4% R-Sq(adj) = 62.6% S = R-Sq = 0.1% R-Sq(adj) = 0.0% S = 0 R-Sq = 100.0% R-Sq(adj) = 100.0% 47

48 R 2? Y Y Y Y X

49 Misunderstandings about R 2 : 1. High R 2 implies precise predictions. (Not necessarily!) Y X1 2. High R 2 implies good fit. (Not necessarily!) Regression Plot Y5 = X S = R-Sq = 90.6 % R-Sq(adj) = 90.4 % Y X 49

50 Misunderstandings about R 2 : 3. Low R 2 implies X and Y not related. (Not necessarily!) Regression Plot GPA = ACT S = R-Sq = 14.1 % R-Sq(adj) = 13.5 % Example 1: GPA data! 4 3 GPA ACT Example 2: Wrong model: 50 Regression Plot Y7 = X S = R-Sq = 0.7 % R-Sq(adj) = 0.0 % 40 Y X 50

51 Misunderstandings about R 2 : 3. Low R 2 implies X and Y not related. (Not necessarily!) Example 3: Low R 2 may result from truncation Y X 51

52 Coefficient of Correlation r = ± R 2 where the sign is given by the sign of b 1 52

53 Anscombe s data if time permits now 53

54 Anscombe s data if time permits Resides in 8 columns in Help/Data Sets/ Regression Can stack x columns, y columns, add count and fit all 4 at once Fasten your seatbelt now You should do these fits yourself! 54

55 More Good Stuff Material in Chapter 2 but glossed over here Bivariate normal model (X also a random variable) Fisher s z transformation for correlation in bivariate normal model Spearman s rank correlation coefficient (replace data with marginal ranks; run usual analysis) 55

56 Already did mean function, future observation (JMP). 56

57 2.10 Cautionary Notes Inferences for the future X needs to be estimated, as well (not fixed) Levels of X outside range of observations β 1 0 does not imply cause-effect E(Y) and future Y go with single X h X subject to measurement errors 57

58 2.11 Normal Correlation Models Marginals are normal, conditional dists. are normal. Tests for can be applied (Fisher s z transformation). 58

59 Rank Regression Rank as in sorted not sordid Spearman s rank correlation coefficient is regular correlation coefficient with raw X s replaced by their ranks. Ranking procedures under-utilized compared to normal theory methods that are over-utilized. 59

60 Exercises 1.6 (draw plot by hand), 1.7, 1.13, 1.16, 1.18, 1.19 (needs software and data disc that comes with the book), 1.23 (continuation of 1.19), 1.29, 1.32, 1.33, 1.34, 1.35, 1.36,

61 Exercises 2.1, 2.4, 2.6, 2.10, 2.13, 2.23, 2.34, 2.50, 2.51, 2.57, Feel free to do more if this gets you more comfortable with the material. For example, you may wish to do the series of problems for copier maintenance all the way through as was done for the GPA data 61

62 Exercises. Chap intercept 200, slope is 5. small sigma. 10, 20, and 40 give 250, 300, 400 for y on 1.7. a. key word is exact. Plus/minus 1 standard deviation, cannot say much without the distribution; b for the normal error distribution a. observational no control over the amount of time each person supposed to devote; b. caused v. seem to be associated ; c.. (seminar leader measures productivity) d. Control participation level 62

63 Exercises 1.16 Least squares estimates do not depend on normality. MLEs with normal distribution error give same estimators but LS validity not dependent on normality e i are observed values, ε i are random variables whose means are each 0 but whose sum is a random variable GPA data sounds familiar Lets look at other ones as well (copier, airfreight, and plastic hardness) same issues 63

64 Exercises 1.29 true intercept happens to be zero. Collect data, fit intercept, could be > or < 0. If zero, do we know intercept is zero? Check on constrain through origin did this on an earlier handout; just in case see next slide 64

65 65

66 1.33 and 1.34 easier than with both intercept and slope 1.35 did that on the handout also since sum of residuals are zero 66

67 Appendix C.2 data set saved the file with col. headings 67

68 Exercises 2.1, 2.4, 2.6, 2.10, 2.13, 2.23, 2.34, 2.50, 2.51, 2.57, Feel free to do more if this gets you more comfortable with the material. For example, you may wish to do the series of problems for copier maintenance all the way through as was done for the GPA data 68

69 Exercises Chap a. CI for slope strictly positive; b. Present results that make sense 2.4 GPA data set. Look back at the CI 69

70 Exercises, chap 2.; 2.4 contin. p-value is low so null must go. 70

71 Exercise 2.6 airfreight baggage;

72 72

73 73

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