Faculty                        : Mathematics and Natural Sciences
Study Program           : Mathematics Education
Course & Code           : Mathematical Statistics, SMA 328
Credit Hours               : Theory 2 credit, Practice 1 credit
Semester                     : V
Prerequisites & Code : Probability Theory, MAA 318
Lecturer                      : Rosita Kusumawati, M.Sc.
I.    COURSE DESCRIPTION
The course is more focused on probability concepts than statistical mathematics. The materials of  probability  theory  are  combinatorial  methods,  probability,  random  variables  and  their distributions,  joint  distributions,  properties  of  random  variables,  and  functions  of  random variables.

II.   BASED COMPETENCY
The students able to use probability and probability distribution for solving a real problem and mathematics problem which is need the calculation of probability, prove the theorems which related to probability, and find a relationship between distributions, and determine expected value of random variables.

III. ACTIVITIES PLAN
 Meeting Based Competency Subject Matter Activities References 1-3 To understand CDF technique, Transformation methods and To gain the ability to compute the CDF of a new variable CDF technique, Transformation methods Discussion & Exercises A: 31-39 B: 1-16 C: 1-113 4-6 To understand sums of random variables, order Statistics Sums of random variables, Order Statistics Discussion & Exercises A: 1-30 B: 22-102 C: 1-113 7-10 To understand the concept of law large number, central limit theorem and its assumptions Sequences of random variables, The central limit theorem, Approximations for the binomial distribution Discussion & Exercises A: 53-83 B: 117-134 11-15 To recognize and learn properties of stochastic convergence Asymptotic normal distributions, Properties of stochastic convergence Discussion & Exercises A: 91-124 B: 134-224 16 Mid Test

 17-21 To solve sampling distributions Sampling distributions, Large- sample properties, Discussion & Exercises A: 137-160 B: 232-286 22-26 To explain methods of estimation Methods of estimation, Criteria for evaluating estimators Discussion & Exercises A: 171-188 B: 297-373 27-32 To gain the ability to use to Bayes and minimax estimation methods Bayes and minimax estimators Discussion & Exercises A: 193-214

IV. REFERENCES Compulsory textbooks  :
A.  Bain, Lee J. & Engelhardt, Max. 1992.  Introduction to Probability and Mathematical
Statistics. Belmont: Duxbury Press.
B.  Ross, Sheldon M. 2010. A First Course in Probability. New Jersey: Prentice-Hall.

Suggested reference books         :
C.  Rice, John A., 1995. Mathematical Statistics and Data Analysis. Belmont: Duxbury Press.

V.   EVALUATION
 No. Components Weight (%) 1. Participations 5 2. Assigment 10 3. Quiz 15 4. Mid Test 30 6. Final Test 40 Total 100

Yogyakarta, September 2012
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