STATISTCS 200C: Intermediate Probability and Statistical Theory

(Theory of linear models)

 

Lecture notes: Review of linear algebra, LSE, Estimable functions (1, 2), Computational Issues

Multivariate Normal (1, 2) , Chi-squared and quadratic forms, Cochranís theorem, F-test, examples, asymptotic results,

Simultaneous regions, random-effect models, model diagnostics

 

 

Homework assignments: homework 1, homework 2, homework 3, homework 4, homework 5 , homework 6 (due on)

 

Practice exam: stat200c final (2016), solution

 

Description and Objectives

Stat200C is the last of a three-quarter series on intermediate probability and statistical theory. The objective of this course is to develop the theoretical basis of statistical methods for linear models.

 

Meeting times and location: 12:30pm on every Monday and Wednesday @ MSTB122

  Stats   200C     INT PROB & STAT THY      (Prerequisites)

Code

Type

Sec

Units

Instructor

Time

Place

Final

Max

Enr

WL

Req

Rstr

Textbooks

Web

Status

37840

Lec

A

4

YU, Z.

MW   12:30- 1:50p

MSTB 122

Wed, Jun 13, 4:00-6:00pm

31

30

1

31

K and A

Bookstore

Web

Waitl

37841

Dis

1

0

YU, Z.

W   2:00- 2:50p

RH 108

 

31

30

1

30

K and A

Bookstore

 

Waitl

 

Office hours: 10 am on every Thursday

 

The following book is highly recommended:

Seber, G.A., and Lee, A.J. (2003). Linear regression analysis (2nd edition). Wiley Interscience.

Reference Texts:

Neter, J., Kutner, MH., Nachtsheim, CJ., and Wasserman, W. (2005). Applied linear statistical models, 5th edition. McGraw-Hill Irwin.

Casella, G. and Berger, R. (2001). Statistical inference (2nd edition). Duxbury Press.

 

Grading

The grade is based upon about seven homework assignments (20%), a midterm (40%) and a final exam (40%). No late homework will be accepted.

 

Important Dates

 

Both the midterm and the final are closed-book and in-class. The final exam will cover the material presented in 200C.

 

Prerequisites

Statistics 200B (or equivalent), or permission from instructor. Students are assumed to be familiar with linear algebra, basic properties of multivariate distribution, and basic convergence theorems of random variables. Prior exposure to applied linear regression is also desirable.

 

Topics