Statistics Graduate

Statistics Courses

STA 5106 Intermediate Statistics I

Course Description: Basic Concepts; Describing and Exploring Data; Normal Probability Distribution; Logic of Hypothesis Testing; Probability, Sampling Distributions and the Central Limit Theorem; Categorical Data Analysis; Hypothesis Tests and Confidence Intervals
Prerequisites: STA 3111 or STA 3123 or STA 3033; and graduate standing.

STA 5107 Intermediate Statistics II

Course Description: Power; Analysis of Variance; Regression Analysis; Correlation
Prerequisites: Intermediate Statistics I

STA 5126 Fundamentals of Design of Experiments

Course Description: Introduction; Review of Parametric Structural Inference; The Completely Randomized Design; Randomized Complete Block Design; Latin Square and Related Design; Factorial Experiments; 2f Factorial Experiments; Fixed, Random, and Mixed Models; Nested and Nested-Factorial Experiments; Factorial Experiments and Restrictions on Randomization; Fractional Factorials
Prerequisites: STA 3112 or STA 3123 or STA 3163 or STA 4322 or equivalent.

STA 5206 Design of Experiments

Course Description: Introduction; The Completely Randomized Design (CRD); Treatment Comparison; Validity of Model Assumptions; Random Effects Model; Factorial Experiments; Factorial, Nested and Nested-Factorial Experiments; Randomized Complete Block Design (RCB); Latin Square Design; Factorial Experiments Fitted in Complete Block Designs
Prerequisites: STA 4322 or STA 3164 or STA 3033 or (STA 3163 and STA 4321).

STA 5207 Topics in Design of Experiments

Course Description: Review of Basic Experimental Designs; More on 3(n) Factorial Experiments; Split Plot Designs; Factorial Experiments and Confounding; Fractional Replication; Incomplete Block Design; Miscellaneous Topics
Prerequisites: STA 5206 'Design of Experiments' or equivalent.

STA 5236 Regression Analysis

Course Description: Simple Regression; Inferences in Regression Analysis; Residual Analysis; Simultaneous Inferences; Matrix Approach; Multiple Regression; Special Topics in Regression Analysis; Selection of Independent Variables; Normal Correlation Model
Prerequisites: STA 3164 or STA 3123 or STA 3112 or STA 6167

STA 5507 Non-Parametric Methods

Course Description: Tests Based on the Binomial Distribution; Contingency Tables; Methods Based On Ranks; Tests of the Kolmogrov-Smirnov Type; Run Test for Randomness; Special Topics; Projects
Prerequisites: A course in statistics.

STA 5666 Advanced Quality Control

Course Description: This course has been designed for both upper level of undergraduate and graduate students for various discipline. The focus of this course is on both applications and theory. Modern statistical methods will be used for quality control and improvement.
Prerequisites: First course in Math. Stat. (STA 4321), or Engineering Stat. (STA 3033), or Stat Method. (STA 3163), or STA 5166, or STA 6244, or STA 6247 or an equivalence course

STA 6166 Statistics Methods Research I

Course Description: Looking At Data: Distribution; Producing: Data; The Study of Randomness; The Probability to Inference; Introduction to Inference; Inference of Distributions; Inferences for Count Data.
Prerequisites: Graduate Standing

STA 6167 Statistics Methods Research II

Course Description: Review Inference for Distributions; Review Inference for Count Data; Inference for Two-Way Tables; Inference for Regressions; Analysis of Variance
Prerequisites: Graduate Standing

STA 6176 Biostatistics

Course Description: Introduction; Counting Data; Discrimination and Classification; Survival Analysis
Prerequisites: STA 3163 or equivalent

STA 6244 Data Analysis I

Course Description: Introduction; Review of Probability; Collecting Data; Exploring and Summarizing Data; Sampling Distributions of Statistics; Basic Concept of Inference; Inference for Single Population; Inference for Two Samples; Inference for Proportions and Count Data; Simple Linear Regression
Prerequisites: STA 3033, STA 4322 or STA 6327

STA 6246 Linear Models

Course Description: Based on the materials learnt from STA 6246, students will learn how to establish a linear model for prediction. Students will learn the estimation of the regression coefficients including both point estimation and interval estimation, learn test of hypotheses on a subset of the regression coefficients and the general linear function of the regression coefficients, prediction of the value of response variable, and analysis of variance models.

STA 6247 Data Analysis II

Course Description: Correlation Analysis; Multiple Linear Regression; Analysis of Single Factor Experiments; Two-Factor Experiments with Fixed Crossed Factors; Nonparametric Statistical Method; Time Series Analysis
Prerequisites: STA 6246 and MAS 3105

STA 6326 Mathematical Statistics I

Course Description: Probability; Random Variables and their Probability Distributions; Moment and Moment Generating Functions; Multiple Random Variables; Some Special Distributions; Limit Theorems
Prerequisites: MAC 2313

STA 6327 Mathematical Statistics II

Course Description: Some Special Distributions; Limit Theorems; Sample Moments and Their Distributions; Parametric Point Estimation; Neyman-Pearson theory of testing Hypotheses; Confidence Estimation
Prerequisites: STA 6327

STA 6505 Categorical Data Analysis

Prerequisites: STA 5107 or STA 5236, STA 6167 or Data Analysis I.

STA 6990 Multivariate Statistical Analysis I

Course Description: Introduction; Introduction to Matrix Algebra; Multivariate Normal Distributions; Multivariate Sampling Distributions; Statistical Inference: One-sample Case; Statistical Inference: Two-sample Case; Multivariate Analysis of Variance (MANOVA); Principal Components Analysis; Factor Analysis; Cluster Analysis
Prerequisites: STA 3123 or STA 3112 or STA 6167