Kean University, 1000 Morris Ave., Union, NJ 07083
MATH 3544_02
Probability and Mathematical Statistics
Spring 2015
Classroom: CAS 238
Class Time: Thursday 4:30-7:15 pm
Textbook: Modern Mathematical Statistics with Applications. By Devore and Berk, Second Edition. Springer,
2012.
Credit Hours: Three.
Prerequisite: Math 2415, or permission of instructor.
Instructor: Dr. Jiantian Wang
Office: C228 (Science Building)
Email: jwang@kean.edu
Phone: (908)737-3713.
Office Hours: M: 1:20-2:20; W:11:00-12:00; 1:20-2:20; Th: 1:00-4:00; F: 11:00-12:00, 1:20-2:20, or by
appointment.
Course Objectives:
A prime objective of this course is to present techniques and basin results of probability and mathematical statistics at
a rigorous, but not advanced level. In Math 3544, we develop the probabilistic tools and language of mathematical
statistics. The course describes probabilistic models for and properties of random variables, common probability
distributions, and large sample results. In this course, the structure of statistical inference procedures is studied. In
particular, the theory of estimation, confidence sets, hypothesis testing, and linear and logistic regression models are
investigated.
Grading will be based on three exams (including final) and homework assignments. The two term exams each will
count for 25% and the final exam 30%. Homework assignments, computer labs, and etc, will count for the remaining
20% of your course grade.
Grade Scale: 100-93 A, 92-89 A-, 88-85 B+, 84-82 B, 81-79 B-, 78-76 C+, 75-70 C, 69-50 D, < 50 F.
Homework : Homework sheets will be provided to you periodically. Once a section has been covered in class, it is
then your responsibility to do the homework for this section. Homework must be your own, though you may discuss
with others. No late homework will be accepted.
Exams: Most exam problems will be at a similar difficulty level as that of homework problems and those problems
discussed in class. Exams will be closed book and closed notes. However, students will be permitted to bring one 8.5
by 11 inch sheet of notes ( both sides, any content) to each exam. It would be very helpful if you can prepare a well
organized review note for each exam. Partial credit will be given to problems in which you show by your written
work that you partially solved the problem. Unexcused absences will result in a grade of 0. Make-up exam may be
arranged in the case of an excused absence within one week of exam time. The final exam will be cumulative. Bring a
calculator to all exams.
Academic honesty and Integrity: The instructor supports and enforces the University Academic Integrity Policy. The
policy covers, cheating, fabrication, facilitating, academic dishonesty, plagiarism and related areas. If I become aware
of violations of this policy, appropriate steps have to be taken. Penalties range from the assignment of a grade of zero
for the activity to dismissal from the University. Each case is handled on an individual basis.
Deadlines: Last day to withdraw with 100% refund
Last day to withdraw with 75% refund
Last day to withdraw with 50% refund
Last day to withdraw with a “W” grade
Jan. 27
Feb. 03
Feb. 10
Mar. 2
Math 3544 Temporary Class Schedule
Date
01/22
Day
Thu
01/29
02/05
Thu
Thu
02/12
02/19
Thu
Thu
02/26
03/05
Thu
Thu
03/12
Thu
03/19
03/26
Thu
Thu
04/02
Thu
04/09
Thu
04/16
04/23
Thu
Thu
04/30
Thu
05/07
Thu
05/14
Thu
Topic
Class Introduction,
1.2.Describe data pictorially and numerically,
2.2. Introduction to probability
2.3 Counting techniques for finding probability
2.4 Conditional probability,
2.5 Independence.
3.2, 3.3 Distribution and expected values
3.4 Moments and moment generating function
3.5 Binomial probability distribution
3.6 Other distributions
Exam 1
4.1, 4.2. pdf and cdf, Moment
4.3. The normal distribution
4.4. The gamma distribution
4.7. Transformation of Random variable
Spring Recess
5.1. Joint distribution
5.2. Expected value, covariance, and correlation
7.2. Methods of point estimation
7.3. Sufficiency
9.1. Hypotheses and test procedures.
9.4. p-value
Exam 2
10.1. Difference between two population means
11.1 Single-factor ANOVA
11.2 Multiple comparison in ANOVA
12.1 Linear and Logistic regression models
12.2 Estimating model parameters
12.3 Inferences about the regression coefficient
Final
NOTE: The above schedule and procedures are subject to change in the event of extenuating circumstances.
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