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:
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
http://www.stat.wisc.edu/~mchung/teaching/R/installingR.htm.
Course Evaluation: Exams may require understanding R statistical outputs.
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.
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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