A Lecture by Lecture Summary of Where We Are

Statistics 511 - Fall 2006

 

Date

 

Topic

NWNK 4th Ed

NWNK 5th Ed

9/6

1

Intro to 511

Some theory of estimation

Sampling distributions

Appendix A.5

Appendix A.5

9/8

2

The least squares criterion

Appendix A.5

Appendix A.5

9/11

3

Checking for Normality

Transforming to Normal

Chap 3

Chap 3

9/13

4

testing the mean

transformation

t-test (with transformed data)

Appendix A.6

Appendix A.6

9/15

5

confidence  intervals for mean

A.6

A.6

9/18

 6 prediction    
9/20

7

What is Regression?

1.1, 1.2, 1.4

1.1, 1.2, 1.4
9/22

8

Nonparametric Regression

3.10

3.10
9/25

9

ANOVA table and other computations

F

1.6

2.7,2.8

1.3

2.7,2.8
9/27

10

R2

Regression assumptions

Residual plots

correlation of errors

2.9

3.3-3.4,3.8-3.9

 

2.9

3.3,3.8,3.9

9/29

11

Transforming Data to achieve linearity

Tranformation to achieve constant variance

Fitting a Regression - example

3.9

1.5,3.11

3.9

1.5,3.11
10/4

13

Inference in the Normal Linear Regression Model

2.1-2.8

2.1-2.8
10/9

14

Prediction

Regression Diagnostics

2.5-2.6

9.2, 9.3, 9.5

2.5-2.6

10.2-10.4
10/11

15

Random Vectors

5.8

5.8
10/13

16

Linear Regression in Matrix Notation

5.9-5.11

5.9-5.11
10/16

17

ANOVA Table in Matrix Notation

5.12, 5.13

5.12, 5.13
10/18

18

Regression Through the Origin

4.4

4.4
10/20 19 Regression Through the Origin 4.4 4.4
10/23

20

Multiple Regression

6.1, 6.2

6.1,6.2
10/25

21

Example of Multiple Regression

Transforming the Data

The ANOVA Table and F-test

6.9

6.5

6.7

6.9

6.5

6.7
10/27

22

Confidence and Prediction Intervals involving Y-hat

Intervals versus Bands

6.7

7.1, 7.4

 

6.7

7.1,7.4

 
10/30 23

Partial and Sequential SS

Partial t-test and CI for one regression coefficient

 6.6 6.6
11/1

24

Partial F-tests

Sequential F-tests

7.2, 7.3

7.2,7.3
11/3

25

Why do the regression coefficients depend on which independent variables are in the model?

7.6

7.6
11/6

26

Variance Inflation and Tolerance

Partial Leverage

Linear Transformation to reduce tolerance

9.5

9.1

10.5

10.1
11/8

27

Polynomial Regression

7.7

8.1
11/10

28

Polynomial Regression –examples

7.7

8.1
11/13 29

Polynomial Regression in 2 or more Predictors

7.8

8.2

11/15

 30

Variable Selection

 

8.1-8.2

9.1-9.2
11/17 31

All Subsets RegressionStepwise Variable Selection

8.3- 8.5

9.3-9.5
11/20 32

Variable Selection - cautions

 8.5,8.6

9.5,9.6
11/21 33

BLUEs and MLEs

A.5, 1.8

A.5
11/27 34

Binary (Logistic) Regression

14.1-14.3, 14.6

14.1-14.5
11/29 35

Multiple Logistic Regression

Inference for Logistic Regression

14.4, 14.5

14.7-14.9

14.4-14.5
12/1 36

Weighted Least Squares

10.1

11.1
12/4 37

Weighted Least Squares

10.1

11.1
12/6 38

Weighted Least Squares

10.1

11.1
12/8 39

Categorical Predictors

11.1,11.2

8.3, 8.4
12/11 40

Categorical Predictors

11.1,11.2

8.3, 8.4
12/13 41

Comparing 2 slopes

11.3-11.7

8.5-8.7
12/15 42

Comparing more than 2 slopes

11.3-11.7

8.5-8.7