Stat 312  Introduction to Mathematical Statistics II, Fall 2004

Instructor: Moo K. Chung

Office Hour: TR 11:00-12:30 or by appointment
Office:  1245C Medical Science Center (MSC)
Tel: (608) 262-1287
Lectures: TR 9:30-10:45 120 Ingraham

TA: Tao Yu, 1231 MSC

TA Office Hour: M, T and R: 2:30--3:30 pm. On M in #1276 and on T and R in #1231. 


DIS 311 1:20p R 5295 MED SC CTR
DIS 312 4:00p R 1207 COMP S&ST

Final Exam 12:25 P.M. FRI. DEC 17

Requirements: Stat 311 + Access to Windows computers. 

Required textbook:
Jay L. Devore, Probability and Statistics for Engineering and the Sciences, 6th edition.
Publisher: Duxbury,
ISBN: 0534399339
5th edition can be used.

Recommended book for computing:
Peter Dalgaard, Introductory Statistics with R.
Publisher: Springer Verlag; 1st edition.
ISBN: 0387954759

Topics: R statistics package. sampling distributions, point estimation, properties of estimators, hypothesis testing, correlations, regression analysis, analysis of variance, categorical data.

Computing: Access to a Windows-based computer and a printer are required. Half of assignments will require using R (statistical computing package) to generate outputs and statistical graphics.  Installation guide can be found at

Course Evaluation: Exams may require understanding R statistical outputs.

Final Grade (100%) = Assignments (20%) + Midterms (45%) + Final Exam (35%).

Important dates

Sept 2 Class starts

Sept 6 Labor day

Sept 15 Last day to drop course without notations on transcript.

Oct 7 First Midterm

Nov 5 Last day to drop courses (undergrduates)
Nov 18 Second Midterm

Nov 26 Last day to withdraw courses/drop courses (graduates)
Nov 25-28 Thanksgiving
Dec 15 Last class day

Final exam 12:25 P.M. FRI. DEC 17

Current lectures - current lecture notes will be based on old lecture notes with  some changes.


Old lectures given in 2002.

Lecture 4 Maximum likelihood estimation
Lecture 5 Confidence intervals
Lecture 6 Large sample confidence intervals
Lecture 7 Large sample confidence intervals II.
Lecture 8 t-distributions
Sample Midterm I.
Lecture 9 Chi-squared distributions
Lecture 10 Hypothesis testing
Lecture 11 Testing on population mean
Lecture 12 Testing on population proportion
Midterm I Solutions
Lecture 13 P-values

Lecture 14 Two sample tests
Lecture 15 Two sample t-test  
Lecture 16 Other two-sample tests
Sample Midterm II.    
Lecture 17 Simple linear models.  
Lecture 18 Least-squares estimation

Lecture 19 Inference on slope 
Lecture 20 Inference on intercept
Lecture 21 Correlation
Midterm II Solutions
Lecture 22 Inference on correlation

Lecture 23 Categorical data
Lecture 24 Chi-square goodness-of-fit tes

Lecture 25 Testing on distributions
Lecture 26 Contigency Table
Lecture 27 Testing on idependence
Sample Final Exam
Final Exam Solutions