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# University Catalog

### STAT 193. Statistical Thinking (GED CORE)

Credits: 3
 Description: Statistical background to critically read results reported in today's media regarding social, environmental and medical choices; how to collect good data; describe data graphically and numerically; uses and abuses of statistics; understanding variation and statistical significance; modeling chance; statistics in the courtroom, lotteries, opinion pools and other case studies; emphasis on understanding concepts rather than on computations; use of software packages and the internet. Prerequisites: MATH 070 or high school advanced algebra with satisfactory math placement score Semester Offered: FallSpringSummer Goal Area: GOAL AREA 4: MATHEMATICAL THINKING & QUANTITATIVE REASONING

### STAT 219. Statistical Methods I for Social Sciences

Credits: 3
 Description: Descriptive statistics, graphical displays, random sampling, and normal distribution; introduction to confidence intervals and hypothesis tests for means and proportions; paired t-test, chi-square test in contingency tables, brief introduction to correlation and simple linear regression; social science applications; use of statistical software. Prerequisites: STAT 193 or MATH 193 or satisfactory math placement score Semester Offered: FallSpringSummer Goal Area: GOAL AREA 4: MATHEMATICAL THINKING & QUANTITATIVE REASONING

### STAT 239. Statistical Methods I for Natural Sciences

Credits: 3
 Description: Descriptive statistics, correlation and regression, design and sampling methods, one and two sample inferences for means and proportions. Introduction to chi-square tests and one-way ANOVA. Use of statistical software. Prerequisites: STAT 193 or MATH 112 or satisfactory math placement score Semester Offered: FallSpringSummer Goal Area: GOAL AREA 4: MATHEMATICAL THINKING & QUANTITATIVE REASONING

### STAT 242. Statistical Methods I for Business

Credits: 4
 Description: Business problem solving: Data collection, summarizing and describing data, estimation and hypotheses testing, analysis of variance, regression analysis, time series, quality control, decision analysis. Use of statistical software. Prerequisites: MATH 196 or MATH 112 or equivalent Semester Offered: FallSpringSummer

### STAT 304. SAS Programming

Credits: 3
 Description: SAS statistical package; basic data manipulations and procedures; formatting, if-then-else, merge, arrays, do-loops, macros, functions, table look-up, custom reports. Corequisites: A statistics course or consent of the instructor Semester Offered: Fall

### STAT 321. Statistical Methods II

Credits: 3
 Description: Statistical methods for analyzing data beyond Statistical Methods I. Modeling data using simple and multiple regression, and one- and two-way analysis of variance. Transformations, model selection, multiple comparisons, randomized block design, and interactions. Prerequisites: One of: STAT 219, STAT 239, STAT 242, or STAT 353 Semester Offered: FallSpring

### STAT 325. Statistical Modeling with R

Credits: 3
 Description: An introduction to R, RStudio, and R Markdown. R programming basics; R packages; descriptive statistics and graphics; statistical inference; statistical modeling; simulation and resampling methods. Prerequisites: One of: STAT 219, STAT 239, STAT 242, or STAT 353 Semester Offered: Spring

### STAT 353. Statistical Methods I for Engineering

Credits: 3
 Description: Probability distributions; introduction to statistical methods, including hypothesis testing and confidence intervals, one-way anova, simple linear regression, quality control basics; applications, and the use of statistical software. Prerequisites: MATH 211 or MATH 221 Semester Offered: FallSpring

### STAT 360. Introduction to Data Visualization

Credits: 3
 Description: Graphically explore a wide variety of data sets. Visual techniques to improve the understanding and communication of complex data. Hands-on implementation of these techniques with real data sets. Methods for visualizing large data sets, including high dimensional data. Dynamic data visualizations. Good design practices for visualization and presentation of results. Prerequisites: One of: STAT 219, STAT 239, STAT 242, or STAT 353 Semester Offered: Fall

### STAT 380. Statistical Consulting and Data Analysis I

Credits: 3
 Description: Introduction to statistical consulting. Principles of good consulting practice. Effective communication skills for understanding the client's problem and available data, and choosing an appropriate procedure. Understanding client expectations, dealing with difficult clients, and working effectively with people individually and in teams. Prerequisites: STAT 321 and one of STAT 304 or STAT 325 Semester Offered: Fall

### STAT 381. Statistical Consulting and Data Analysis II

Credits: 3
 Description: Data analysis for statistical consulting projects. Working with the client to understand the problem and available data. Carrying out and documenting an appropriate analysis. Preparing written and oral summaries. Communication of results to the client. Prerequisites: STAT 380 Semester Offered: Spring

### STAT 415. Data Mining

Credits: 3
 Description: Data mining principles and applications. Predictive modeling techniques for large data sets include classification and regression trees, logistic regression, neural networks, random forests and boosted trees. Handle missing values and outliers. Compare models and deploy best model to predict new data. Hands-on use of data mining software. Prerequisites: STAT 321 Semester Offered: Spring

### STAT 417. Applied Probability and Simulation

Credits: 3
 Description: Probability distributions and random variables, simulation of random variates, probability modeling, applications to Markov chains, queueing models, reliability and survival; use of software. Prerequisites: MATH 211 or MATH 221, and one programming course Semester Offered: Spring

### STAT 421. Applied Regression Methods

Credits: 3
 Description: Advanced regression methods focused on complex real-world data. Model checking and diagnostics, model building, transformations, polynomial regression, logistic regression, general linear models, nonparametric regression methods. Prerequisites: STAT 321 Semester Offered: Spring

### STAT 424. Statistical Design for Process Improvement

Credits: 3
 Description: A study of statistically designed experiments which have proven useful in product development and process improvement; topics include randomization, blocking, factorial treatment structures, fractional factorial designs, screening designs, Taguchi methods, response surface methods; use of statistical software. Prerequisites: STAT 321 Semester Offered: DEMAND

### STAT 427. Applied Time Series

Credits: 3
 Description: A study of the most useful techniques of analysis and forecasting using time series data. Topics include an introduction to forecasting, time series regression, decomposition methods, smoothing, smoothing techniques, basic techniques of Box-Jenkins methodology; use of statistical software. Prerequisites: STAT 321 Semester Offered: DEMAND

### STAT 430. Multivariate Statistical Methods

Credits: 3
 Description: Principal component analysis, factor analysis, discriminant analysis, cluster analysis, manova, profile analysis, repeated measures; applications and use of statistical software. Prerequisites: STAT 321 Semester Offered: DEMAND

### STAT 433. Nonparametric Statistics

Credits: 3
 Description: Efficiency comparison of mean and median, one and two sample location problems, effect of alternative score functions, randomization and permutation tests, the independence problem, and selected problems in regression. Use of statistical software. Prerequisites: STAT 321 Semester Offered: DEMAND

### STAT 436. Applied Categorical Data Analysis

Credits: 3
 Description: Introduction to the analysis of discrete data; log-linear models for two-way and multi-way tables; linear logistics regression models; association models and models of symmetry; applications, use of statistical software. Prerequisites: STAT 321 Semester Offered: DEMAND

### STAT 440. Topics in Statistics

Credits: 3
 Description: Study of modern topics in theoretical or applied statistics. Semester Offered: DEMAND

### STAT 444. Internship

Credits: 3-12
 Description: Participation in a full or part-time position with a cooperating business, governmental, or civic organization whose program has been approved in advanced by the department of statistics. Credits are provided upon completion of all requirements of the internship. Can substitute for STAT 480 if approved by the department. Any remaining credits apply to university electives for graduation. Semester Offered: FallSpringSummer

### STAT 447. Basic Elements of Probability Theory

Credits: 3
 Description: A more mathematical treatment of probability distributions than STAT 417. Probability concepts and laws; sample spaces, combinations and permutations, Bayes' theorem, discrete and continuous random variables, expected value, distribution of functions of random variables, two-demensional variates, central limit theorem; T, F, and chi-square distributions. Prerequisites: MATH 320 or MATH 321 Semester Offered: Fall

### STAT 448. Basic Elements of Statistical Theory

Credits: 3
 Description: Theory of estimation and hypothesis testing; maximum likelihood, method of moments, likelihood ratio tests; elementary mathematical functions illustrate theory. Prerequisites: STAT 447 Semester Offered: Spring

### STAT 501. Credit By Arrangement

Credits: 1-4
 Description: Credit By Arrangement. Semester Offered: FallSpringSummer

### STAT 515. Data Mining

Credits: 3
 Description: Data mining principles and applications. Predictive modeling techniques for large data sets include classification and regression trees, logistic regression, neural networks, random forests and boosted trees. Handle missing values and outliers. Compare models and deploy best model to predict new data. Hands-on use of data mining software. Prerequisites: STAT 321 Semester Offered: Spring

The contents in this catalog and other university publications, policies, fees, bulletins or announcements are subject to change without notice and do not constitute an irrevocable contract between any student and St. Cloud State University.