All homeworks must be turned in by Wednesday, August 3 to be counted in your final grade. This includes homework 8, and any late homeworks you wish to submit. (Sorry, I'm off to a conference on Saturday, August 6, so need to have everything graded by August 5.)

Reminder: The final is Friday, August 5. It will primarily cover homeworks 7-8, Section 4.2 to Chapter 6, but there will be some material from the earlier sections as well. You may bring in one page with whatever you wish on it; this will be turned in with your exam.
A sample exam may be found here.
Solutions may be found here.

I'll put course announcements here. Be sure to check here regularly, especially if you can't make it to class for any reason.

1. Due June 15: Ch. 1: 1, 4, 5, 6, 7, 9
   A) Return to the experiment of problem 1. Assuming the coin is fair, and thus all elements of the sample space are equally likely, find the probability of each of the events in 1b and 1c.

2. Due June 22: Ch. 1: 11, 13, 14, 16, 22, 29, 35, 41, 47, 48, 50, 61, 63, 68, 70

3. Due June 29: Ch. 2: 1, 2, 3, 8, 12, 15, 19, 20, 21, 25, 28, 29, 31, 32

4. Due Friday July 8: Ch. 2: 33, 34, 37, 38, 39, 40, 45, 49, 53, 55, 59, 60, 61, 67, 68

5. Due July 13: Ch. 3: 1, 3, 7, 8, 9, 12, 14, 19, 20, 27, 40, 46, 54

6. Due July 20: Ch. 3: 33, 37, 38, 42, 57, 59
   Ch. 4: 2, 5, 7, 13, 16, 17, 34

7. Due Friday July 29: Ch. 4: 23, 30, 39, 40, 43, 45, 46, 53, 57, 64, 67, 69

8. Due August 3: Ch. 4: 75, 76, 79, 80, 87, 88
   Ch. 5: 5, 10, 16
   Ch. 6: 1, 2, 3, 7, 10

I'll put copies of my powerpoint slides here. These are the same handouts provided in class. They are in pdf format, and will require an appropriate reader.

• Handout 1 (slides 2-66)
• Handout 2 (slides 67-116)
• Handout 3 (slides 117-165)
• Handout 4 (slides 166-191)
• Handout 5 (slides 192-205)
• Handout 6 (slides 206-225)
• Handout 7 (slides 226-244)
• Handout 8 (slides 245-264)
• Handout 9 (slides 265-293)
• Handout 10 (slides 294-309)
• Handout 11 (slides 310-330)

Instructor: Michael Minnotte
Office: Lund 201-C
Phone: 797-2844
E-mail: minnotte@math.usu.edu
Office Hours: MWF 9:00 - 9:40 or by appointment

Text: Rice, John A., (1995), Mathematical Statistics and Data Analysis, Second Edition, Belmont, CA: Duxbury Press.

Prerequisites: You should have a good background in calculus, ideally including multivariable calculus (Math 2210 or equivalent), for this course. It would be helpful to have had Math 4200 or another course with an emphasis on mathematical proofs, but this is not required.

Homework:Homeworks will be due in class every Wednesday. The homeworks will primarily consist of problem sets taken from Rice. All problems will be worth 10 points and contribute the same amount to your final grade, so the value of a perfect score may vary from week to week.

Please make things easy for me; make your homeworks easy to read and grade. Use one side of the paper, write neatly, and leave plenty of space. I will not grade a paper which I can't read. Also, show your work. Full credit will not, in general, be given for just the answer. If your answer is wrong, you will probably receive partial credit if you show your work, but not otherwise.

Finally, you may help each other with your homeworks, but I expect what you turn in to be your own work. Helping does not mean simply copying what someone else has put down.

Late Homeworks: Homeworks will be due in class on the due date unless otherwise specified. The grade for the homework will be reduced by 10% if it is turned in late on Wednesday, and another 10% for every working day it is late after that, to a minimum of 30% of the original grade.

Once during the semester, I will, on request, waive the late penalty for a homework turned in up to one week after the due date (or start the clock then for a still later turn-in). Simply note the request at the top of your homework when turning it in. Additional requests for extension without penalty will not be granted, so save this for a time you really need it. No late homeworks will be accepted after August 5, the last day of class.

Tests: There will be two midterms, on July 1 and July 22, and a final exam on August 5. All tests will be in class at the usual class time, and will consist of problems similar to those on the homeworks.

Grades: For each student I will compute an overall score according to the formula

and will assign grades accordingly. There is no fixed grade profile for this class: if everyone does well, everyone can get an A.

Topics: This course will cover Chapters 1-6 in Rice, and should provide a strong foundation in the mathematics of probability. Probability, the study of random or chance processes, is widely used in many fields of study. It was first studied by and for gamblers for their games of chance. Today it is fundamental to modern physics, genetics, computer science, electrical engineering, and a wide spectrum of other areas. It is particularly central to statistics, the study of data collection and analysis, as almost all statistical methods are founded upon probabilistic models.

1. Basic Probability: sample spaces; axioms of probability; counting methods for computing probabilities; permutations and combinations; conditional probabilities; law of total probability; Bayes' rule; independence.

2. Random Variables: discrete random variables; cumulative distribution functions; probability mass functions; bernoulli, binomial, negative binomial, hypergeometric, and Poisson distributions; continuous random variables; probability density functions; uniform, exponential, gamma, normal, and beta distributions; distributions of functions of random variables.

3. Random Vectors: joint, conditional, and marginal distributions, densities, and mass functions; multinomial distributions; bivariate normal distributions; independence of random variables; functions of jointly distributed random variables; extreme values and order statistics.

4. Expected Values: expectation; mean; Markov's inequality; variance; Chebyshev's inequality; covariance and correlation; mean and variance of linear functions of random variables; conditional expectation; moment-generating functions.

5. Limit Theorems: convergence in probability; the weak law of large numbers; convergence in distribution; the central limit theorem.

6. Distributions Related to the Normal Distribution: the chi-square, t, and F distributions; normal distribution sample mean and variance.

Disability Policy: If a student has a disability that will likely require some accomodation by the instructor, the student must contact the instructor and document the disability through the Disability Resource Center, preferably during the first week of the course. Any requests for special considerations relating to attendance, pedagogy, taking of examinations, etc. must be discussed with and approved by the instructor. In cooperation with the Disability Resource Center, course materials can be provided in alternative formats - large print, audio, diskette or Braille.

Late Adds: The last day to add this class is June 22. Attending this class beyond that date without being officially registered will not be approved by the Dean's Office.

The above schedule and procedures in this course are subject to change in the event of extenuating circumstances.