33 courses with the subject STAT, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
Integrated co-requisite support of mathematics, statistics and probability topics necessary for success in STAT 103, with an emphasis on representing, analyzing and interpreting data. Course credits apply towards financial aid requirements but do not count towards graduation.
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. Pre- Requisite(s): MATH070 or MATH072
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. Pre-Requisite(s): STAT103 or STAT193 or MATH103 or MATH193 or MATH196 or MATH106 or MATH112 or MATH115 or MATH221 or MATH211 e STAT239 Statistical Methods I for Natural Sciences [Credits: 3] 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. Pre-Requisite(s): STAT103 or MATH112
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. Pre-Requisite(s): MATH106 or MATH112
SAS statistical package; basic data manipulations and procedures; formatting, if-then-else, merge, arrays, do-loops, macros, functions, table look-up, custom reports.
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. Pre- Requisite(s): STAT219 or STAT353 or IS242 or STAT239 or STAT242
An introduction to R, R Studio, and R Markdown. R programming basics; R packages; descriptive statistics and graphics; statistical inference; statistical modeling; simulation and resampling methods. Pre- Requisite(s): STAT219 or STAT239 or STAT242 or STAT353
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. Pre-Requisite(s): MATH211 or MATH221
Graphically explore a wide variety of data sets. Visual techniques to 231
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. Pre- Requisite(s): STAT321, STAT304 or STAT325
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. Pre-Requisite(s): STAT380
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. Pre- Requisite(s): STAT321
Probability distributions and random variables, simulation of random variates, probability modeling, applications to Markov chains, queueing models, reliability and survival; use of software. Pre-Requisite(s): MATH211 or MATH221
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. Pre-Requisite(s): STAT321
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. Pre-Requisite(s): STAT321
Principal component analysis, factor analysis, discriminant analysis, cluster analysis, manova, profile analysis, repeated measures; applications and use of statistical software. Pre-Requisite(s): STAT321
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. Pre-Requisite(s): STAT321
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. Pre-Requisite(s): STAT321
Credit for off-campus professional experience with industry partners, non-profit organizations or government agencies.
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-dimensional variates, central limit theorem; T, F, and chi-square distributions. Pre-Requisite(s): MATH321 or MATH320
Theory of estimation and hypothesis testing; maximum likelihood, method of moments, likelihood ratio tests; elementary mathematical functions illustrate theory. Pre-Requisite(s): STAT447
Credit by Arrangement.
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. Pre- Requisite(s): STAT321
Probability distributions and random variables, simulation of random variates, probability modeling, applications to Markov chains, queueing models, reliability and survival; use of software. Pre-Requisite(s): MATH211 or MATH222
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. Pre-Requisite(s): STAT321
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. Pre-Requisite(s): STAT321, STAT304
Principal component analysis, factor analysis, discriminant analysis, cluster analysis, manova, profile analysis, repeated measures; applications and use of statistical software. Pre-Requisite(s): STAT321
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. Pre-Requisite(s): STAT321
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. Pre-Requisite(s): STAT321, STAT304
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-dimensional variates, central limit theorem; T, F, and chi-square distributions. Pre-Requisite(s): MATH321 or MATH320
Theory of estimation and hypothesis testing; maximum likelihood, method of moments, likelihood ratio tests; elementary mathematical functions illustrate theory. Pre-Requisite(s): STAT447 or STAT547
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. Extensive hands-on use of data mining software. Pre- Requisite(s): STAT242 or IS242
Explore visual representations of data for exploratory analysis. 281
Source: Saint Cloud State University's catalog, linked per course · table learning_unit · CourseShelf publish 61
