Approved by: Undergraduate Course and Curriculum Committee
Implementation Date: Summer 2013
Note: Deletions are strikethroughs. Insertions are underlined.
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STAT 1220. Elements of Statistics I (BUSN). (3) Prerequisite: MATH 1100 or appropriate score on the Mathematics Placement Test or placement by the department. Non-calculus based introduction to data summarization, discrete and continuous random variables (e.g., binomial, normal), sampling, central limit theorem, estimation, testing hypotheses, and linear regression. Applications of theory will be drawn from areas related to business. May not be taken for credit if credit has been received for STAT 1221 or 1222. (Fall, Spring, Summer) (Evenings)
STAT 1221. Elements of Statistics I. (3) Prerequisite: MATH 1100 or appropriate score on the Mathematics Placement Test or placement by the department. Same topics as STAT 1220 with special emphasis on applications to the life sciences. May not be taken for credit if credit has been received for STAT 1220 or 1222. (Fall, Spring)
STAT 1222. Introduction to Statistics. (3) Prerequisite: MATH 1100 or appropriate score on the Mathematics Placement Test or placement by the department. Same topics as STAT 1220 with special emphasis on applications to the social and behavioral sciences. May not be taken for credit if credit has been received for STAT 1220 or 1221. (Fall, Spring, Summer) (Evenings)
STAT 2122. Introduction to Probability and Statistics. (3) Prerequisite: MATH 1242 or 2120 or permission of the department. A study of probability models, discrete and continuous random variables, inference about Bernoulli probability, inference about population mean, inference about population variance, the maximum likelihood principle, the minimax principle, Bayes procedures, and linear models. (Fall, Spring, Summer) (Evenings)
STAT 2223. Elements of Statistics II. (3) Prerequisite: Either STAT 1220, STAT 1221, STAT 1222, STAT 2122 or permission of the department. Topics include contingency analysis, design of experiments, more on simple linear regression, and multiple regression. Computers will be used to solve some of the problems. (Fall)
STAT 3110. Applied Regression. (3) (W) Prerequisite: STAT 1220, 1221, 1222, or 2122 and MATH 1242 or 2120 or permission of the department. Ordinary regression models, logistic regression models, Poisson regression models. (Spring)
STAT 3123. Probability and Statistics II. (3) Prerequisite: MATH/STAT 3122. Cross-listed as MATH 3123. (Spring) (Evenings)
STAT 3128. Probability and Statistics for Engineers. (3) Prerequisite: MATH 2241. An introduction to: probability theory; discrete and continuous random variables and their probability distributions; joint probability distributions; functions of random variables and their probability distributions; descriptive statistics; point and interval estimation; one and two sample hypothesis testing; quality control; one and two factor ANOVA; and regression. Credit will not be given for both STAT 3128 and any of these courses: STAT 2122, MATH/STAT 3122/3123.
STAT 3140. Design of Experiments. (3) Prerequisite: STAT 2122 2223 or 3110 or permission of the department. Randomization and blocking with paired comparisons, Significance tests and confidence intervals, experiments to compare k treatment means, randomized blocks and two-way factorial designs, designs with more than one blocking variable, empirical modeling, factorial designs at two levels. (Fall) (Alternate years)
STAT 3150. Time Series Analysis. (3) Prerequisites: STAT 2223 or 3110 or permission of the department. Stationary time series models, ARMA processes, modeling and forecasting with ARMA processes, ARIMA models for nonstationary time series models, spectral densities. (Spring) (Alternate years)
STAT 3160. Applied Multivariate Analysis. (3) Prerequisite: STAT 2223 or 3110 or permission of the department. Introduction to the fundamental ideas in multivariate analysis using case studies. Descriptive, exploratory, and graphical techniques; introduction to cluster analysis, principal components, factor analysis, discriminant analysis, Hotelling T2 and other methods. (Fall)