Download presentation

Presentation is loading. Please wait.

Published byHarry Wiler Modified about 1 year ago

1
1 3.3 Hypothesis Testing in Multiple Linear Regression Questions: –What is the overall adequacy of the model? –Which specific regressors seem important? Assume the errors are independent and follow a normal distribution with mean 0 and variance 2

2
2 3.3.1 Test for Significance of Regression Determine if there is a linear relationship between y and x j, j = 1,2,…,k. The hypotheses are H 0 : β 1 = β 2 =…= β k = 0 H 1 : β j 0 for at least one j ANOVA SS T = SS R + SS Res SS R / 2 ~ 2 k, SS Res / 2 ~ 2 n-k-1, and SS R and SS Res are independent

3
3 Under H 1, F 0 follows F distribution with k and n- k-1 and a noncentrality parameter of

4
4 ANOVA table

5
5

6
6 Example 3.3 The Delivery Time Data

7
7 R 2 and Adjusted R 2 –R 2 always increase when a regressor is added to the model, regardless of the value of the contribution of that variable. –An adjusted R 2 : –The adjusted R 2 will only increase on adding a variable to the model if the addition of the variable reduces the residual mean squares.

8
8 3.3.2 Tests on Individual Regression Coefficients For the individual regression coefficient: –H 0 : β j = 0 v.s. H 1 : β j 0 –Let C jj be the j-th diagonal element of (X’X) -1. The test statistic: –This is a partial or marginal test because any estimate of the regression coefficient depends on all of the other regression variables. –This test is a test of contribution of x j given the other regressors in the model

9
9 Example 3.4 The Delivery Time Data

10
10 The subset of regressors:

11
11 For the full model, the regression sum of square Under the null hypothesis, the regression sum of squares for the reduce model The degree of freedom is p-r for the reduce model. The regression sum of square due to β 2 given β 1 This is called the extra sum of squares due to β 2 and the degree of freedom is p - (p - r) = r The test statistic

12
12 If β 2 0, F 0 follows a noncentral F distribution with Multicollinearity: this test actually has no power! This test has maximal power when X 1 and X 2 are orthogonal to one another! Partial F test: Given the regressors in X 1, measure the contribution of the regressors in X 2.

13
13 Consider y = β 0 + β 1 x 1 + β 2 x 2 + β 3 x 3 + SS R ( β 1 | β 0, β 2, β 3 ), SS R ( β 2 | β 0, β 1, β 3 ) and SS R ( β 3 | β 0, β 2, β 1 ) are signal-degree-of –freedom sums of squares. SS R ( β j | β 0,…, β j-1, β j, … β k ) : the contribution of x j as if it were the last variable added to the model. This F test is equivalent to the t test. SS T = SS R ( β 1, β 2, β 3 | β 0 ) + SS Res SS R ( β 1, β 2, β 3 | β 0 ) = SS R ( β 1 | β 0 ) + SS R ( β 2 | β 1, β 0 ) + SS R ( β 3 | β 1, β 2, β 0 )

14
14 Example 3.5 Delivery Time Data

15
15 3.3.3 Special Case of Orthogonal Columns in X Model: y = X β + = X 1 β 1 + X 2 β 2 + Orthogonal: X 1 ’X 2 = 0 Since the normal equation (X’X) β = X’y,

16
16

17
17 3.3.4 Testing the General Linear Hypothesis Let T be an m p matrix, and rank(T) = r Full model: y = X β + Reduced model: y = Z + , Z is an n (p-r) matrix and is a (p-r) 1 vector. Then The difference: SS H = SS Res (RM) – SS Res (FM) with r degree of freedom. SS H is called the sum of squares due to the hypothesis H 0 : T β = 0

18
18 The test statistic:

19
19

20
20 Another form: H 0 : T β = c v.s. H 1 : T β c Then

21
21 3.4 Confidence Intervals in Multiple Regression 3.4.1 Confidence Intervals on the Regression Coefficients Under the normality assumption,

22
22

23
23 3.4.2 Confidence Interval Estimation of the Mean Response A confidence interval on the mean response at a particular point. x 0 = (1,x 01,…,x 0k )’ The unbiased estimator of E(y|x 0 ) :

24
24 Example 3.9 The Delivery Time Data

25
25 3.4.3 Simultaneous Confidence Intervals on Regression Coefficients An elliptically shaped region

26
26 Example 3.10 The Rocket Propellant Data

27
27

28
28 Another approach: is chosen so that a specified probability that all intervals are correct is obtained. Bonferroni method: Δ = t α /2p, n-p Scheffe S-method: Δ =(2F α,p, n-p ) 1/2 Maximum modulus t procedure: Δ = u α,p, n-2 is the upper tail point of the distribution of the maximum absolute value of two independent student t r.v.’s each based on n-2 degree of freedom

29
29 Example 3.11 The Rocket Propellant Data Find 90% joint C.I. for β 0 and β 1 by constructing a 95% C.I. for each parameter.

30
30 The confidence ellipse is always a more efficient procedure than the Bonferroni method because the volume of the ellipse is always less than the volume of the space covere3d by the Bonferroni intervals. Bonferroni intervals are easier to construct. The length of C.I.: Maximum modulus t < Bonferroni method < Scheffe S-method

31
31 3.5 Prediction of New Observations

32
32 3.6 Hidden Extrapolation in Multiple Regression Be careful about extrapolating beyond the region containing the original observations! Rectangle formed by ranges of regressors NOT data region. Regressor variable hull (RVH): the convex hull of the original n data points. –Interpolation: x 0 RVH –Extrapolation: x 0 RVH

33
33

34
34 h ii of the hat matrix H = X(XX) -1 X’are useful in detecting hidden extrapolation. h max : the maximum of h ii. The point x i that has the largest value of h ii will lie on the boundary of RVH {x | x(XX) -1 x ≦ h max } is an ellipsoid enclosing all points inside the RVH. Let h 00 = x ′(X′X)-1x 0 –h 00 h max : inside the RVH and the boundary of RVH –h 00 > h max : outside the RVH

35
35 MCE : minimum covering ellipsoid (Weisberg, 1985).

36
36

37
37 3.7 Standardized Regression Coefficients Difficult to compare regression coefficients directly. Unit Normal Scaling: Standardize a Normal r.v.

38
38 New model: –There is no intercept. –The least-square estimator of b is

39
39 Unit Length Scaling:

40
40 New Model: The least-square estimator:

41
41 It does not matter which scaling we use! They both produce the same set of dimensionless regression coefficient.

42
42

43
43

44
44 3.8 Multicollinearity A serious problem: Multicollinearity or near-linear dependence among the regression variables. The regressors are the columns of X. So an exact linear dependence would result a singular X’X

45
45 Unit length scaling

46
46 Soft drink data: Off-diagonal elements are of W’W usually called the simple correlations between regressors.

47
47 Variance inflation factors (VIFs): –The main diagonal elements of the inverse of X’X ((W’W) -1 above) –From above two cases:Soft drink: VIF 1 = VIF 2 = 3.12 and Figure 3.12: VIF 1 = VIF 2 = 1 –VIF j = 1/(1-R j ) –R j is the coefficient of multiple determination obtained from regressing x j on the other regressor variables. –If x j is nearly linearly dependent on some of the other regressors, then R j 1 and VIF j will be large. –Serious problems: VIFs > 10

48
48 Figure 3.13 (a): The plan is unstable and very sensitive to relatively small changes in the data points. Figure 3.13 (b): Orthogonal regressors.

49
49 3.9 Why Do Regression Coefficients Have the Wrong Sign? The reasons of the wrong sign: 1.The range of some of the regressors is too small. 2.Important regressors have not been included in the model. 3.Multicollinearity is present. 4.Computational errors have been made.

50
50 For reason 1:

51
51 Although it is possible to decrease the variance of the regression coefficients by increase the range of the x’s, it may not be desirable to spread the levels of the regressors out too far: –The true response function may be nonlinear. –Impractical or impossible. For reason 2:

52
52

53
53 Fore reason 3: Multicollinearity inflates the variances of the coefficients, and this increases the probability that one or more regression coefficients will have the wrong sign. Different computer programs handle round-off or truncation problems in different ways, and some programs are more effective than the others in this regard.

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google