## MATH 308 Topics in Statistical Inference Fall 2010 - Class number=8890

 INSTRUCTOR: Prof. KARIN REINHOLD Office: ES 123A Phone: 442-4641 e-mail: reinhold@albany.edu Class Time: MWF 1:40-2:35pm. Room: SLG12 Office Hours: MWH:11-12am TEXT: Intro Stats by De Veaux, Velleman \& Bock Pearson, Prentice Hall

## Files

• cellphones
• Class 1
• Homework Sept 1, Problems due - Sept 3 (actually they are due Sept 8).
• week2, There is homework on course compass that is optional -Descriptive statistics and normal model(I'm not going to collect the grade) however there is Quiz 2 on course compass that is due Monday. You can use the homework to practice as much as you need and then go to do the quiz. There is only one try for the quiz, so make sure you know how to do the problems by going over the homework.
• Fri Sept 17: Read Chapter 16. Problems: 1, 3, 11, 18, 27, 29, 37 39
• Mon Sept 20: Read chapters 14 and 15. Problems: Ch 14: 11, 19, 21, 25, 31, 33, 35 Ch 15: 1, 5, 9,11, 13, 19.
• Central Limit Theorem project, Due Sept 29. File: CLTdistrib.xls, CLTfile in R.
• Confidence Intervals file in R
• Exam 2 file: take home due Wed Oct 15. Grade for exam 2= max(exam2 in class, 85% exam 2 take home).
• Test of Proportion worksheet file.<.li>
• Exam 3: Covers chapters: 20 to 25. Answers file
• Exam 3 files: aroma, and scores
• Chi-square test file

### Course description:

This course is part 2 of a basic course in statistics. Its pre-requisites are: Math 108 or having taken statistics in highschool. You should be familiar with descriptive statistics, histograms, boxplots, correlation, the basics of probability, the Binomial random variable and the normal distribution. We will continue hypothesis testing for proportions, estimating means with confidence, testing hypothesis about means, Chi--square tests, simple and multiple regression, analysis of variance, and lastly, non--parametric methods. The class objectives are to learn (a) the mathematics behind the tests, (b) being able to identify when to use a particular case, (c) interpret the results. To aid the computational aspects of the techniques and to do graphical representations of data, we will be using Excel and R. You don't need to be familiar with them, but we will learn what we need in both progrmas as we go. Familiarity with excel will be a great tool to have for any future job. The skills you'll learn with R can be translated to any other statistical software like S-plus or SAS, widely used for applications of statistics - but R is free.

The grade in the course will be based on four exams of 100 pts each, plus in class or on line quizzes and/or projects (total of 100 pts). Grade scale (in percentage of total grade): 100-94 = A, 93-87=A-, 86-81=B+, 80-75=B, 74-69=B-, 68-63=C+,62-57=C, 56-50=C-. Familiarize yourself with Blackboard. The on-line quizzes and projects will be posted there. To pass the course you need to obtain a total average of 50\% or more, must not obtain a grade of 30 or less in more than one exam or in the combination of all quizzes.

It is your responsibility to be aware of the dates of the exams and the content and due date of assignments. If you miss a class, it is your responsibility to be aware of the topics discussed during that class, the assigned homework and the possibly given assignment. There are no make ups for in-class quizzes missed.

There is no reason to miss an exam other than getting sick (bring note from doctor), being on a team that has a game at the same time an exam is given (bring a note from your coach), or a death or serious illness in your family (bring a note from your family). In the event you can not attend an exam, you must notify me in advance, otherwise your grade for that exam will be 0. You can contact me by phone (leave a message if I'm not in), stop by my office (leave a note if I'm not in) or send me an e-mail.

EXAM SCHEDULE:
Exam 1: Sept 17
Exam 2: Oct 8
Exam 3: Nov 5
Exam 4: Dec 3