|
Instructor |
Dr. Anda Gadidov |
|
Office |
Science
Building Room 529 |
|
Phone |
(770)423-6098,
e-mail: agadidov@kennesaw.edu |
|
Office
hours |
TTH 11:00am-
12:30pm; TH:5:00pm-6:30pm other times by appointment |
|
Class
meets |
TH
6:30pm-9:15pm in CL1005 |
|
Text |
An Introduction
to Mathematical Statistics and its Applications, fourth ed., by Richard J.
Larsen and Morris L. Marx, Pearson Education, Prentice Hall, Upper
Saddle River, ISBN: 0 – 13 – 186793-8. Additional
Resource: Understanding Probability and
Statistics- A book of problems by Ruma Falk |
|
Prerequisites |
MATH
1190 or STAT 7000 |
|
Description |
Fundamental
concepts of probability, random variables and their distributions; review of sampling
distributions; theory and methods of point estimation and hypothesis testing,
interval estimation, nonparametric tests, introduction to linear models.
Software such as Maple 10, Minitab or SAS will be used. |
|
Learning |
1. Students will understand and be
able to use the basic probability theory. 2. Students will be able to use the
concepts of probability distributions and distribution functions in problems
and real life models. 3. Students will understand how the
most commonly used discrete as well as continuous distributions arise in the
real world. 4. Students will be able to solve
problems involving random variables and their distributions. 5. Students will understand the
concept of joint distributions and be able to compute marginal probabilities
and probabilities involving jointly distributed random variables. 6. Students will be able to compute
mathematical expectations, moments, joint moments and moment generating
functions of random variables. 7. Students will be able to apply the
various techniques they learned in calculus to the field of Statistics. 8. Students will be able to derive
estimators for various parameters using the method of moments or the method
of maximum likelihood. 9. Students will be able to
construct confidence intervals for the parameters of various distributions. 10. Students will understand the
concept of statistical hypothesis testing and will be able to use it
accordingly in applications. 11. Students will be able to use the
normal distribution and distributions derived from it in applications. |
|
Topic
outline |
1.
Probability: Sample Spaces, The Probability Function, Conditional Probability and 2.
Random Variables: Binomial and Hypergeometric Distributions, Discrete and Continuous
Random Variables, Expected Values and Variances, Joint Distributions, Combining Random Variables,
Conditional Distributions, Order Statistics, The Moment-Generating Function. 3. Special
Distributions: Poisson, Normal, Geometric, Negative Binomial, Gamma. 4. Estimation:
Estimating Parameters: The Method of Maximum Likelihood and the Method of
Moments, Interval Estimations, Properties of Estimators, Minimum-Variance
Estimators: The Cramér-Rao Lower Bound, Sufficient
Estimators, Consistency, Bayesian Estimation. 5. Hypothesis
Testing: The Decision Rule, Testing Binomial Data, Type I and Type II Errors,
The Generalized Likelihood Ratio. 6. The Normal
Distribution: Deriving The Student Distribution, Inferences and means and
variances. 7. Types of Data:
Classifying Data. 8. Two Sample Problems:
Tests for Two Means, two variances and two proportions; Confidence Intervals
for Two-Sample Problem. |
|
Grading |
Working
the homework will be a very important component of success in the course. Your grade
will be based on your performance on homework assignments, class discussions
and presentations, projects and tests. Occasionally there may be a quiz,
there will be two semester exams and a final comprehensive exam. The final is
scheduled on Thursday, Dec. 4, 8:00 pm- 10:00 pm. Make-up
tests will not be given unless there are exceptional circumstances. If
you must miss a test, you should notify me in writing before the
scheduled test time. Check
my homepage http://math.kennesaw.edu/~agadidov
for updates on the course. A: 90%
or above, B: between 80% and 90%; C: between 70% and 80%, D: below 70%. |
|
Academic |
Every KSU student is responsible for upholding the
provisions of the Student Code of Conduct, as published in the Undergraduate
and Graduate Catalogs. Section II of the Student Code of Conduct addresses
the University’s policy on academic honesty, including provisions
regarding plagiarism and cheating, unauthorized access to University
materials, misrepresentation/falsification of University records or academic
work, malicious removal, retention, or destruction of library materials,
malicious/intentional misuse of computer facilities and/or services, and
misuse of student identification cards. Incidents of alleged academic
misconduct will be handled through the established procedures of the
University Judiciary Program, which includes either an “informal”
resolution by a faculty member, resulting in a grade adjustment, or a formal
hearing procedure, which may subject a student to the Code of Conduct’s
minimal one semester suspension requirement. |
|
Withdrawal |
Students who find that they cannot continue in college for
the entire semester after being enrolled, because of illness or any other
reason, need to complete an online form. To completely or partially withdraw
from classes at KSU, a student must withdraw online at www.kennesaw.edu, under Owl Express, Student Services.
The date the withdrawal is submitted online will be
considered the official KSU withdrawal date which will be used in the
calculation of any tuition refund or refund to Federal student aid and/or HOPE
scholarship programs. It is advisable to print the final page of the
withdrawal for your records. Withdrawals submitted online prior to Students may, by means of the same online withdrawal and
with the approval of the university Dean, withdraw from individual courses
while retaining other courses on their schedules. This option may be
exercised up until October 10, 2008. This is the date to withdraw without academic penalty
for Fall Term, 2008 classes. Failure to withdraw by the date above will mean
that the student has elected to receive the final grade(s) earned in the
course(s). The only exception to those withdrawal regulations will be for
those instances that involve unusual and fully documented circumstances. |
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