Data description: Frequency table, histogram, measures of central tendency, scatter diagram and correlation. Probability theory; sampling; probability distributions; point and confidence interval estimation; application for managerial decision. A statistical package will be used.

Note: Not open for credit to Statistics or Mathematics Majors. Not to be taken for credit with ISE 205, STAT 201 and STAT 319.

Prerequisite: 

Hypothesis testing for means and variances; index numbers and time series; simple linear progression and correlation analysis; multiple regression analysis; the chi-squared and F distributions and their applications. A statistical package will be used.

Note: Not open for credit to Statistics or Mathematics Majors. Not to be taken for credit with ISE 205, STAT 201 and STAT 319.

Prerequisite:  STAT 211

Descriptive Statistics: Graphical and numerical measures. Elementary Probability theory; sampling techniques; probability distributions; estimation; hypothesis testing for means and variances; index number and introductory time series analyses; simple linear regression and correlation analysis; multiple regression analysis; the chi-squared and F distributions and their applications; application for financial decisions; application using statistical packages.

Note: Not to be taken for credit with  STAT 201, STAT 211, STAT 212, or STAT 319.

Prerequisite:  MATH 102 or MATH 106

Statistical computation with major statistics packages used in academics and industry: data structure, entry, and manipulation; numerical and graphical summaries; basic statistical methods; exploratory data analysis, simulation-based methods, selected advanced methods.

Prerequisite: STAT 201 or STAT 212 or STAT 214 or STAT 319

Basic classical models of probability. Set functions. Axiomatic definition of probability. Conditional probability and Bayes' theorem. Random variables and their types. Distributions, moments, and moment generating functions. Special discrete and continuous distributions. Random vectors and their distributions. Marginal and conditional distributions. Independent random variables. Functions of random variables. Sums of independent random variables. Weak law of large numbers and the central limit theorem.

Prerequisite:  MATH201, STAT201 or STAT212 or STAT214 or STAT319 or ISE315

Random sampling and the sampling distributions: t, chi-square, and F. Order Statistics. Methods of estimation: maximum likelihood and moments. Properties of a good estimator: unbiasedness, consistency, efficiency, sufficiency, and approximate normality. Testing of simple hypotheses, the Neyman-Pearson lemma. Testing composite hypotheses, uniformly most powerful and likelihood ratio tests. Bayesian Statistics.

Prerequisite:  STAT 301

Simple linear regression: The least squares method, parameter estimation, confidence intervals, tests of hypotheses and model adequacy checking. Multiple linear regression, including estimation of parameters, confidence intervals, tests of hypotheses and prediction. Model adequacy checking and multicollinearity. Polynomial regression. Variable selection and model building.

Prerequisite:  STAT201 or STAT212 or STAT214 or STAT319 or ISE315

Presentation and interpretation of data, elementary probability concepts, random variables and probability distributions, binomial, Poisson, exponential, Weibull, normal and lognormal random variables. Estimation, tests of hypotheses for the one sample problem. Simple and multiple linear regression, application to engineering problems. The lab session will be devoted to problem solving using statistics software.

Note: Not to be taken for credit with ISE205 or STAT214

Prerequisite:  MATH102

How control charts work. Control chart methods for attributes and variables. Process-control chart techniques. Process-capability analysis. Acceptance-sampling by attributes and variables.

Note: Not to be taken for credit with ISE 320

Prerequisite:  STAT201 or STAT212 or STAT214 or STAT319 or ISE315

One sample problem, the sign, and Wilcoxon signed rank tests. Two-Sample problem, Wilcoxon rank sum and Mann-Whitney tests. Kruskal-Wallis test for one-way layout. Friedman test for randomized block design. Run test for randomness. Goodness of fit tests.

Prerequisite:  STAT201 or STAT212 or STAT214 or STAT319 or ISE315

Review for descriptive statistics, estimation, and testing hypotheses. Simple linear regression. One way analysis of variance. Multiple regression. Randomized block designs. Factorial experiments. Random and mixed effect models.

Note: Not to be taken for credit with STAT 310 and/or STAT 430

Prerequisite:  STAT201 or STAT212 or STAT214 or STAT319 or ISE315

Scope of demography. Vital events. Demographic survey. History of world population and distribution. Demographic transition. Fertility and its measures. Mortality and its measures. Direct and indirect standardization. The life table. Construction of a life table. Stationary population. Stable population. Migration. Theories of migration. Consequences of migration. Population estimates and projections.

Prerequisite:  STAT201 or STAT212 or STAT214 or STAT319 or ISE315

Problem solving and decision making. Linear programming: formulation, the graphical method, the simplex method, sensitivity analysis, and duality. Transportation and assignment problem. Integer programming. Project scheduling PERT/CPM.

Note: Not to be taken for credit with ISE 303

Prerequisite:  STAT201 or STAT212 or STAT214 or STAT319 or ISE315

Concept of data collection. Sample surveys, finite and infinite populations, execution and analysis of samples. Basic sampling designs: simple, stratified, systematic, cluster, two-stage cluster. Methods of estimation of population means, proportions, totals, sizes, variances, standard errors, ratio, and regression.

Prerequisite:  STAT201 or STAT212 or STAT214 or STAT319 or ISE315

2x2 contingency tables, two-way contingency tables, three-way and higher dimensional contingency tables. Loglinear models for contingency tables. Logistic regression. Building and applying loglinear models.

Prerequisite:   STAT201 or STAT212 or STAT214 or STAT319 or ISE315

Students are required to spend one summer working in industry prior to the term in which they expect to graduate. Students are required to submit a report and make a presentation on their summer training experience and the knowledge gained.

Prerequisite:   ENGL 214, Junior Standing, Approval of the Department

Basic classes of stochastic processes. Poisson and renewal processes with applications in simple queuing systems. Discrete and continuous time Markov chains. Birth-death and Yule processes. Branching models of population growth and physical processes.

Prerequisite:  STAT 301

Basic classes of stochastic processes. Poisson (regular, compound, compound surplus, and non-homogenous) and renewal processes with applications in simple queuing systems and actuarial science. Discrete and continuous time Markov chains. Birth-death and Yule processes. Branching models of population growth processes. Actuarial risk models; simulation. Arithmetic and geometric Brownian motions, and applications of these processes such as in computation of resident fees for continuing care retirement communities, and pricing of financial instruments.

Note: Not to be taken for credit with STAT 415

Prerequisite:  STAT 301

Importance of statistical design of experiments. Single-factor and multifactor analysis of variance. Factorial designs. Randomized blocks. Nested designs. Latin squares. Confounding and 2-level fractional factorials. Analysis of covariance.

Prerequisite:  STAT201 or STAT212 or STAT214 or STAT319 or ISE315

Review of multiple regression. The general linear model. Quadratic forms. Gauss- Markov theorem. Multivariate normal distribution. Computational aspects. Full rank models. Models not of full rank. Computer applications.

Prerequisite:  STAT 310

Nonlinear, Poisson and Logistic regression. Linear models. Multivariate Normal and the distribution of Quadratic forms. Link function. The generalized linear model. Estimation (Estimation of Full and reduced rank models. OLS, GLS, ML and Quasi-likelihood. Fisher Scoring). Evaluation of Models (Including Deviance Residuals). Inference (Gauss-Markov theorem. Wald test). Computational aspects and Computer applications for categorical and continuous data.

Prerequisite:  STAT 310

Introduction to multivariate analysis. Multivariate normal distribution theory. Distribution of the sum of product matrix. Inference about the parameters of the multivariate normal distribution. Comparison of means. Linear models. Principal components. Factor analysis. Classification and discrimination techniques.

Prerequisite:  STAT 310

Examples of simple time series. Stationary time series and autocorrelation. Autoregressive moving average processes. Modeling and forecasting with ARMA processes. Maximum likelihood and least squares estimator. Non-stationary time series.

Prerequisite:  STAT 310

Inventory models. Waiting line models. Decision Analysis. Multicriteria decision problems. Markov process. Dynamic programming. Calculus-based Procedures.

Note: Not to be taken for credit with ISE 421

Prerequisite:   STAT 301, STAT 361

This course is designed to draw upon various components of the undergraduate curriculum. The project could be in the area of data analysis, sampling survey, experimental design, regression analysis, multivariate data analysis, time series and etc. A report is essential for course completion.

Prerequisite:  Senior Standing

Life tables, graph and related procedures. Single samples: complete or Type II censored data and Type I censored data for exponential, Weibull, gamma and other distributions. Parametric regression for exponential, Weibull and gamma distributions. Distributions- free methods for proportional hazard and related regression models.

Prerequisite:   STAT 302, STAT 310

Variable contents. Open for senior students interested in studying an advanced topic in statistics with a departmental faculty member.

Prerequisite:   Senior standing, permission of the Department Chairman upon recommendation of the instructor.