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Digression: The F distribution. 18. The Fq,n k 1 distribution: 19. Another digression: A little history of statistics... 20. A little history of statistics, ctd... – PowerPoint PPT presentation

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Title: Outline


1
Outline
2
Hypothesis Tests and Confidence Intervals for a
Single Coefficient in Multiple Regression (SW
Section 7.1)
3
Standard errors in multiple regression in STATA
4
Tests of Joint Hypotheses(SW Section 7.2)
  • A joint hypothesis specifies a value for 2 or
    more coefficients, also called imposing
    restrictions on q coefficients
  • Consider the population regression model
  • Expn expenditures per pupil
  • Test the null hypothesis that school resources
    dont matter vs. the alternative that they do

5
Why cant we just test the coefficients one at a
time?
  • The one at time test is
  • Reject H0 ?1 ?2 0 if t1 gt 1.96
    and/or t2 gt 1.96
  • What is the probability that this one at a time
    test rejects H0, when H0 is actually true? (It
    should be 5.)

6
Size of a Test
7
The F-statistic testing ?1 and ?2
8
Large-sample distribution of the F-statistic
9
(No Transcript)
10
Computing the p-value using the F-statistic
11
F-test example, California class size data
12
(No Transcript)
13
The restricted and unrestricted regressions
14
Simple formula for the homoskedasticity-only
F-statistic
15
Example
16
The homoskedasticity-only F-statistic summary
17
Digression The F distribution
18
The Fq,nk1 distribution
19
Another digression A little history of
statistics
20
A little history of statistics, ctd
21
Summary the homoskedasticity-only F-statistic
and the F distribution
22
Summary testing joint hypotheses
23
Testing Single Restrictions on Multiple
Coefficients (SW Section 7.3)
24
Testing single restrictions on multiple
coefficients, ctd.
25
Method 1 Rearrange (transform) the regression
26
Rearrange the regression, ctd.
27
Method 2 Perform the test directly
28
Confidence Sets for Multiple Coefficients (SW
Section 7.4)
29
Joint confidence sets ctd.
30
(No Transcript)
31
Confidence set based on inverting the F-statistic
32
An example of a multiple regression analysis
and how to decide which variables to include in a
regression
33
A general approach to variable selection and
model specification
34
Digression about measures of fit
35
Back to the test score application
36
More California data
37
Digression on presentation of regression results
38
(No Transcript)
39
Summary Multiple Regression
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