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Course Descriptions - Stat

210 Statistical Methods I
Data collection and experimental design, graphical and numerical methods for describing data, correlation and linear regression, probability models, random variables, the central limit theorem, one and two sample hypothesis tests and confidence intervals, Chi-Square test, and one-way ANOVA. (1 credit)
Lecture: 3 hrs/week

(May not enroll in HNRS 240 or INQ 240 if STAT 210 has been completed; may not receive credit for STAT 210 if HNRS 240 or INQ 240 has been completed.)

220 Statistical Methods II
Simple and multiple regression techniques, one and two-way ANOVA, nonparametric methods, logistic regression, big data, and bootstrap methods. (1 credit)
Lecture: 3 hrs/week
Prerequisite: HNRS 240, INQ 240, or STAT 210.

301 Mathematical Statistics
Probability, discrete and continuous distributions, moments and moment-generating functions, sampling theory and estimation. (1 credit)
Lecture: 3 hrs/week
Prerequisite: Mathematics 112.

303 Experimental Design
Analysis of variance, analysis of covariance, multiple-range tests, completely randomized and randomized block designs, Latin squares, factorial designs and split-plot designs. (1 credit)
Lecture: 3 hrs/week
Prerequisite: Statistics 210 or 220.

304 Applied Regression Analysis
Applied statistical methods with emphasis on interpretation of regression models, data analysis, statistical computation, and model building. Specific topics covered include: simple and multiple linear regression, non linear regression, correlation, use of dummy variables, the diagnoses of residuals, selection of variables and time series techniques. There will be a significant use of statistical software. (1 credit)
Lecture: 3 hrs/week
Prerequisite: Statistics 210 or 220.

405, 406, 407 Independent Study and Research
Selected topics in statistics carried out under the direction of a member of the department staff. Enrollment with the approval of the department. (1/2 credit, 1 credit, 1/2 credit)

416 Internship
Field placement providing practical experience and training in areas in which statistics is applied. These areas may include industry, government agencies, educational institutions, insurance companies and a variety of private enterprises. Permission of the department is required. (1 credit)