Instructor: Moo K. Chung mchung@stat.wisc.edu

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 yutao@stat.wisc.edu, 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. 

Discussions:

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 

http://www.stat.wisc.edu/~mchung/teaching/R/installingR.htm.

Course Evaluation: Exams may require understanding R statistical outputs.

• Assignments (20%) There will be  about 8 biweekly assignments. They will require using R (statistical package). Staple your papers together. Write your name clearly. Put your last name in the last position. Please hand in homework in class on time. No late homework will be accepted or graded. Homework grading is the responsibility of TAs. For all matters of homework, contact TAs directly.
• Mid Terms (45%)  There will be two midterms. Calculators are permitted; One page note is permitted (letter size).
• Final Exam (35%)  Covers everything. Calculators are permitted; One page note is permitted (letter size).

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.

• Lecture 00 Course outline
• Lecture 01 R basics
• Lecture 02 Point estimation
• Lecture 03 Minimum variance unbiased estimator
• Lecture 04 Moment matching
• Lecture 05 Maximum likelihood estimation
• Lecture 06 Confidence intervals
• Lecture 07 Quantile-Quantile plots
• Lecture 08 Large sample confidence intervals
• Lecture 09 Confidence intervals for normal population
• Sample midterm I
• Sample midterm II (Fall 2001) 
• Midterm I. Oct 7 covers Homework I and II.
• Lecture 10. Confidence intervals for variance
• Lecture 11. Hypothesis testing
• Lecture 12. Type I and II errors
• Lecture 13. Testing on population mean
• Lecture 14. One sample t test
• Lecture 15. P-values
• Lecture 16. Two sample tests
• Lecture 17. Two sample t tests
• Lecture 18. Paired data
• Sample midterm III
• Sample midterm IV (Fall 2001)
• Midterm II. Nov 18 covers homework III, IV and V.
• Lecture 19 Linear regression 1
• Lecture 20 Least squares estimation
• Lecture 21 Linear regression 2
• Sample final exam I
• Sample final exam II

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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