北大暑期课程《回归分析》(Linear-Regression-Analysis)讲义PKU5汇编

Class 5: ANOVA (Analysis of Variance) and F-testsI. What is ANOVAWhat is ANOVA? ANOVA is the short name for the Analysis of Variance. The essenceof ANOVA is to decompose the total variance of the dependent variable into two additive components, one for the structural part, and the other for the stochastic part, of a regression. Today we are going to examine the easiest case.II. ANOVA: An IntroductionLet the model beεβ+= X y .Assuming x i is a column vector (of length p) of independent variable values for the i th'observation,i i i εβ+='x y .Then b 'x i is the predicted value.sum of squares total:[]∑-=2Y y SST i []∑-+-=2'x b 'x y Y b i i i[][][][]∑∑∑-+-+-=Y -b 'x b 'x y 2Y b 'x b 'x y 22i i i i i i[][]∑∑-+=22Y b 'x e i ibecause [][][]∑∑=-=--0Y b 'x e Y b 'x b 'x y ii i i i .This is always true by OLS. = SSE + SSRImportant: the total variance of the dependent variable is decomposed into two additive parts: SSE, which is due to errors, and SSR, which is due to regression. Geometric interpretation: [blackboard ]Decomposition of VarianceIf we treat X as a random variable, we can decompose total variance to the between-group portion and the within-group portion in any population:()()()i i i x y εβV 'V V +=Prove:()()i i i x y εβ+='V V()()()i i i i x x εβεβ,'Cov 2V 'V ++=()()iix εβV 'V +=(by the assumption that ()0 ,'Cov =εβk x , for all possible k.)The ANOVA table is to estimate the three quantities of equation (1) from the sample.As the sample size gets larger and larger, the ANOVA table will approach the equation closer and closer.In a sample, decomposition of estimated variance is not strictly true. We thus need toseparately decompose sums of squares and degrees of freedom. Is ANOVA a misnomer?III. ANOVA in MatrixI will try to give a simplied representation of ANOVA as follows:[]∑-=2Y y SST i ()∑-+=i i y Y 2Y y 22∑∑∑-+=i i y Y 2Y y 22∑-+=222Y n 2Y n y i (because ∑=Y n y i )∑-=22Y n y i2Y n y 'y -=y J 'y n /1y 'y -= (in your textbook, monster look)SSE = e'e[]∑-=2Y b 'x SSR i()()[]∑-+=Y b 'x 2Y b 'x 22i i()[]()∑∑-+=b 'x Y 2Y n b 'x 22i i()[]()∑∑--+=i i i e y Y 2Y n b 'x 22()[]∑-+=222Y n 2Y n b 'x i(because ∑∑==0e ,Y n y i i , as always)()[]∑-=22Yn b 'x i2Y n Xb X'b'-=y J 'y n /1y X'b'-= (in your textbook, monster look)IV. ANOVA TableLet us use a real example. Assume that we have a regression estimated to be y = - 1.70 + 0.840 xANOVA TableSOURCE SS DF MS F with Regression 6.44 1 6.44 6.44/0.19=33.89 1, 18Error 3.40 18 0.19 Total 9.8419We know∑=100xi, ∑=50y i , 12.509x 2=∑i , 84.134y 2=∑i , ∑=66.257y x i i . If weknow that DF for SST=19, what is n?n= 205.220/50Y ==84.95.25.22084.134Y n y SST 22=⨯⨯-=-=∑i()[]∑-+=0.1250.84x 1.7-SSR 2i[]∑-⨯⨯⨯-⨯+⨯=0.125x 84.07.12x 84.084.07.17.12i i= 20⨯1.7⨯1.7+0.84⨯0.84⨯509.12-2⨯1.7⨯0.84⨯100- 125.0= 6.44SSE = SST-SSR=9.84-6.44=3.40DF (Degrees of freedom): demonstration. Note: discounting the intercept when calculating SST. MS = SS/DFp = 0.000 [ask students]. What does the p-value say?V. F-TestsF-tests are more general than t-tests, t-tests can be seen as a special case of F-tests.If you have difficulty with F-tests, please ask your GSIs to review F-tests in the lab. F-tests takes the form of a fraction of two MS's.MSR/MSE F , df2df1An F statistic has two degrees of freedom associated with it: the degree of freedom inthe numerator, and the degree of freedom in the denominator.An F statistic is usually larger than 1. The interpretation of an F statistics is thatwhether the explained variance by the alternative hypothesis is due to chance. In other words, the null hypothesis is that the explained variance is due to chance, or all the coefficients are zero.The larger an F-statistic, the more likely that the null hypothesis is not true. There is atable in the back of your book from which you can find exact probability values.In our example, the F is 34, which is highly significant.VI. R 2R 2 = SSR / SSTThe proportion of variance explained by the model. In our example, R-sq = 65.4%VII. What happens if we increase more independent variables.1. SST stays the same.2. SSR always increases.3. SSE always decreases.4. R 2 always increases.5. MSR usually increases.6. MSE usually decreases.7. F-test usually increases.Exceptions to 5 and 7: irrelevant variables may not explain the variance but take up degrees of freedom. We really need to look at the results.VIII. Important: General Ways of Hypothesis Testing with F-Statistics.All tests in linear regression can be performed with F-test statistics. The trick is to run"nested models."Two models are nested if the independent variables in one model are a subset or linearcombinations of a subset （子集）of the independent variables in the other model.That is to say. If model A has independent variables (1, 1x , 2x ), and model B hasindependent variables (1, 1x , 2x ,3x ), A and B are nested. A is called the restricted model; B is called less restricted or unrestricted model. We call A restricted because A implies that0=3β. This is a restriction.Another example: C has independent variable (1, 1x , 2x +3x ), D has (1, 2x +3x ). C and A are not nested.C and B are nested. One restriction in C: 32ββ=.C andD are nested. One restriction in D: 0=1β. D and A are not nested.D and B are nested: two restriction in D: 32ββ=; 0=1β.We can always test hypotheses implied in the restricted models. Steps: run tworegression for each hypothesis, one for the restricted model and one for the unrestrictedmodel. The SST should be the same across the two models. What is different is SSE and SSR. That is, what is different is R 2. Let()()df df SSE ,df df SSE u u r r ==;df df ()()0u r u r r u n p n p p p -=---=-<Use the following formulas:()()()()(),SSE SSE /df SSE df SSE F SSE /df r u r u dfr dfu dfu u u---=or()()()()(),SSR SSR /df SSR df SSR F SSE /df u r u r dfr dfu dfu u u---=(proof: use SST = SSE+SSR)Note, df(SSE r )-df(SSE u ) = df(SSR u )-df(SSR r ) =df ∆,is the number of constraints (not number of parameters) implied by the restricted modelor()()()22,2R R /df F 1R /dfur dfr dfu dfuuu--∆=- Note thatdf 1df ,2F t =That is, for 1df tests, you can either do an F-test or a t-test. They yield the same result. Another way to look at it is that the t-test is a special case of the F test, with the numerator DF being 1.IX. Assumptions of F-testsWhat assumptions do we need to make an ANOVA table work?Not much an assumption. All we need is the assumption that (X'X) is not singular, so that the least square estimate b exists.The assumption of ε'X =0 is needed if you want the ANOVA table to be an unbiased estimate of the true ANOVA (equation 1) in the population. Reason: we want b to be an unbiased estimator of β, and the covariance between b and εto disappear.For reasons I discussed earlier, the assumptions of homoscedasticity and non-serial correlation are necessary for the estimation of ()i V ε.The normality assumption that εi is distributed in a normal distribution is needed for small samples.X. The Concept of IncrementEvery time you put one more independent variable into your model, you get an increase in 2R . We sometime called the increase "incremental 2R ." What is means is that more variance is explained, or SSR is increased, SSE is reduced. What you should understand is that the incremental 2R attributed to a variable is always smaller than the 2R when other variables are absent.XI. Consequences of Omitting Relevant Independent VariablesSay the true model is the following:0112233i i i i i y x x x ββββε=++++.But for some reason we only collect or consider data on 21,,x and x y . Therefore, we omit3x in the regression. That is, we omit in 3x our model. We briefly discussed this problembefore. The short story is that we are likely to have a bias due to the omission of a relevant variable in the model. This is so even though our primary interest is to estimate the effect of1x or 2x on y.Why? We will have a formal presentation of this problem.XII. Measures of Goodness-of-FitThere are different ways to assess the goodness-of-fit of a model. A. R 2R 2 is a heuristic measure for the overall goodness-of-fit. It does not have an associated test statistic.R 2 measures the proportion of the variance in the dependent variable that is “explained” by the model: R 2 =SSESSR SSRSST SSR +=B. Model F-testThe model F-test tests the joint hypotheses that all the model coefficients except for the constant term are zero.Degrees of freedoms associated with the model F-test: Numerator: p-1 Denominator: n-p.C. t-tests for individual parametersA t-test for an individual parameter tests the hypothesis that a particular coefficient is equal to a particular number (commonly zero).t k = (b k - βk0)/SE k , where SE k is the (k, k) element of MSE(X’X)-1, with degree of freedom=n-p.D. Incremental R 2Relative to a restricted model, the gain in R 2 for the unrestricted model:∆R2= R u2- R r2E. F-tests for Nested ModelIt is the most general form of F-tests and t-tests. ()()()()(),SSE SSE /df SSE df SSE F SSE /df r u r dfu dfr u dfu u u---=It is equal to a t-test if the unrestricted and restricted models differ only by one single parameter.It is equal to the model F-test if we set the restricted model to the constant-only model.[Ask students] What are SST, SSE, and SSR, and their associated degrees of freedom, for the constant-only model?Numerical ExampleA sociological study is interested in understanding the social determinants of mathematicalachievement among high school students. You are now asked to answer a series of questions. The data are real but have been tailored for educational purposes. The total number of observations is 400. The variables are defined as: y: math scorex1: father's educationx2: mother's educationx3: family's socioeconomic status x4: number of siblings x5: class rankx6: parents' total education (note: x6 = x1 + x2) For the following regression models, we know: Table 1 SST SSR SSE DF R 2 (1) y on (1 x1 x2 x3 x4) 34863 4201 (2) y on (1 x6 x3 x4) 34863 396 .1065 (3) y on (1 x6 x3 x4 x5) 34863 10426 24437 395 .2991 (4) x5 on (1 x6 x3 x4) 269753 396 .02101. Please fill the missing cells in Table 1.2. Test the hypothesis that the effects of father's education (x1) and mother's education (x2) on math score are the same after controlling for x3 and x4.3. Test the hypothesis that x6, x3 and x4 in Model (2) all have a zero effect on y.4. Can we add x6 to Model (1)? Briefly explain your answer.5. Test the hypothesis that the effect of class rank (x5) on math score is zero after controlling for x6, x3, and x4.学习-----好资料更多精品文档 Answer:1.SST SSR SSE DF R 2(1) y on (1 x1 x2 x3 x4) 34863 4201 30662 395 .1205(2) y on (1 x6 x3 x4) 34863 3713 31150 396 .1065(3) y on (1 x6 x3 x4 x5) 34863 10426 24437 395 .2991(4) x5 on (1 x6 x3 x4) 275539 5786 269753 396 .0210Note that the SST for Model (4) is different from those for Models (1) through (3).2.Restricted model is 01123344()y b b x x b x b x e =+++++Unrestricted model is ''''''011223344y b b x b x b x b x e =+++++(31150 - 30662)/1F 1,395 = -------------------- = 488/77.63 = 6.2930662 / 3953.3713 / 3F 3,396 = --------------- = 1237.67 / 78.66 = 15.7331150 / 3964. No. x6 is a linear combination of x1 and x2. X'X is singular.5.(31150 - 24437)/1F 1,395 = -------------------- = 6713 / 61.87 = 108.5024437/395t = 10.42t ===。