**STAT 2301**. Statistics for Modern Business Decisions. A foundation in data analysis and probability models is followed by elementary applications of condense intervals, hypothesis testing, correlation, and regression. Prerequisite: CEE Math Fundamentals or equivalent. (Not offered starting FALL 2017).

**STAT 2331**. Introduction to Statistical Methods. A non-calculus based introduction to statistical methods, and how to use statistical concepts in decision making. Topics include; descriptive statistics, simple linear regression, elementary probability theory, confidence intervals, and hypothesis tests. Introduces the use of Excel for statistical analysis.

** NOTE: STAT 2301 and STAT 2331 both can satisfy the quantitative fundamentals (QF) foundation of the University Curriculum (UC). Once a student has matriculated to SMU, they must satisfy this QF requirement through coursework taken at SMU. This means no transfer credit will be given for STAT 2301 or STAT 2331 for coursework taken outside of SMU after matriculation.**

**STAT 3300**. Applied Statistics. Emphasizes the analysis of data using state-of-the-art statistical methods and specialized statistical software.

**STAT 3304**. Introduction to Statistical Computing. Introduces students to statistical computing, including SAS programming.

**STAT 3312**. Categorical Data Analysis. Examines techniques for analyzing data that are described by categories or classes. Discusses classical chi-square tests and modern log-linear models. Emphasizes practical applications using computer calculations and graphics. Prerequisite: STAT 2301 or 2331, or equivalent.

**STAT 4340.** (EMIS 3340, CSE 4340). Statistical Methods for Engineers and Applied Scientists. Basic concepts of probability and statistics useful in the solution of engineering and applied science problems. Topics: probability, probability distributions, data analysis, sampling distributions, estimation, and simple tests of hypothesis. Prerequisites: MATH 1337 and 1338.

**STAT 4341** (old number 5340) (EMIS 5370). Probability and Statistics for Scientists and Engineers. Introduction to fundamentals of probability and distribution theory, statistical techniques used by engineers and physical scientists. Examples of tests of significance, operating characteristic curve, tests of hypothesis about one and two parameters, estimation, analysis of variance, and the choice of a particular experimental procedure and sample size. Prerequisites: MATH 1337, 1338, and 2339, or equivalent. Cannot be used for credit with STAT 4340.

**STAT 4344** (old number 5344) (EMIS 5364). Statistical Quality Control. Statistics and simple probability are introduced in terms of problems that arise in manufacturing; their application to control of manufacturing processes. Acceptance sampling in terms of standard sampling plans: MIL-STD 105, MIL-STD 414, Dodge-Romig plans, continuous sampling plans, etc. Prerequisite: STAT (EMIS) 4340 or STAT 4341 (EMIS 5370).

**STAT 4350** (old number 5350) (ECO 5350) Introductory Econometrics. The basic concepts of econometrics and, in particular, regression analysis, with topics geared to first-time regression users. Preprquisites: MATH 1309 or 1337; ECO 3301; and ITOM 2305 or STAT 2301, 2331, or 4340.

**STAT 4370** Survey Sampling. Principles of planning and conducting surveys. Simple random sampling; stratified, systematic, subsampling; means, variances, confidence limits; finite population correction; and margin of error and sample-size determination. Prerequisite: STAT 2301 or 2331, or equivalent.

**STAT 4375** (old number 5375) (ECO 5375) Economic and Business Forecasting. This course presents methods used by economists to forecast economic and business trends. Statistical procedures for evaluating the usefulness of these methods are also discussed. Illustrative examples include forecasting GNP, interest rates, and unemployment. Prerequisites: C- or better in ECO 3301 and one of the following: STAT 2301, 2331, 4340; or ITOM 2305. UG only, no graduate STAT credit.

**STAT 4377** (old number 5377). Statistical Design and Analysis of Experiments. Introduction to statistical principles in the design and analysis of industrial experiments. Completely randomized, randomized complete and incomplete block, Latin square, and Plackett-Burman screening designs. Complete and fractional factorial experiments. Descriptive and inferential statistics. Analysis of variance models. Mean comparisons. Prerequisite: STAT 4340, UG only, no graduate STAT credit.

**STAT 4385**. Introduction to Nonparametric Statistics. Statistical methods that do not require explicit distributional assumptions such as normality. Analyses based on ranks. One- and multi-sample procedures. Tests of randomness and independence. Prerequisite: STAT 2301 or 2331, or equivalent.

**6000 level courses (MASDA)**

The following classes may be used as graduate credit in the MASDA program. They may also (with permission) be used as UG credit in the B.S. program. Note that due to SACS regulations, students who declare a STAT major (B.S.) on or after 8/22/2016 can only use 12 hours of 6xxx classes taken as an undergraduate towards a MASDA degree, and these 12 hours must be in their final year of undergraduate study.

**STAT 6301** (old number 5371). Experimental Statistics. A non-calculus development of the fundamental procedures of applied experimental statistics, including tests of hypotheses and interval estimation for the normal, binomial, chi-square and other distributions, and nonparametric tests. Prerequisite: Junior standing or permission of instructor.

**STAT 6301** (old number 5372). Experimental Statistics. Analysis of variance, completely randomized design, randomized complete block designs-nested classifications, factorials; analysis of covariance, simple and multiple linear regressions, and correlation. Prerequisite: STAT 6301.

**STAT 6307** (old number 5304). Introduction to Statistical Computing. This course introduces students to statistical computing with an emphasis on SAS programming. Students will learn how to read, write, and import data; prepare data for analysis; use SAS procedures; and create graphs.
STAT 6308. SAS II and Databases. Covers topics in data management and statistical analysis techniques, including data cleaning and verification; reading, writing, and manipulating data using DDE and SQL techniques; programming macros; combing, interleaving, stacking, and transposing data sets; customizing output using ODS; and advanced data analysis techniques. Prerequisite: STAT 5304 or permission of instructor.

**STAT 6311** (was 5373). Introduction to Mathematical Statistics. Topics include: probability; probability distributions; mathematical expectation; discrete and continuous random variables and their distributions; sampling distributions; moment generating function; functions of random variables; confidence intervals. Prerequisite: MATH 2339

**STAT 6312** (was 5374): Mathematical Statistics II: Second course in Mathematical Statistics. Topics include: order statistics; limiting distributions; central limit theorem; point estimation; testing statistical hypotheses; Bayesian procedures; nonparametric methods. Prerequisite: STAT 6311.

**STAT 6315**. Survey Sampling.

**STAT 6316** (was 5380) (ECO 6380) Predictive Analytics. A study of data mining techniques used by economics in the fields of applied economics, marketing, finance, and statistics. These techniques include classification methods, affinity analysis, and data reduction and exploration methods. Prerequisites: STAT 2301, 2331, 4340 or ITEM 2305 or graduate standing.