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Course Criteria
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3.00 Credits
Goals, objectives, techniques, materials, and evaluation. Directed observation in public schools. Preparation of teaching plans and materials. Simulated teaching experiences.
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3.00 Credits
Data collection and descriptive statistics. Concepts of probability and probability distributions. Binomial and normal distributions. Estimation of means, confidence intervals, and hypothesis tests for single mean and proportion. Simple regression and correlation. Contingency tables. Process improvement and statistical process control. Use of statistical computing software. Applied course appropriate for a general audience.
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3.00 Credits
Intended as an alternative to 201 for higher GPA students.
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3.00 Credits
Data collection and descriptive statistics. Concepts of probability and probability distributions. Discrete and continuous distributions. Estimation of means, confidence intervals, and hypothesis tests for single mean and proportion. Simple regression and correlation. Process improvement, statistical process control, and 2-level experiments. Use of statistical computing software.
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3.00 Credits
Simple linear regression and correlation analysis, time series analysis, multiple regression, variable selection, regression diagnostics, partial correlation, and categorical data analysis techniques. Use of statistical computing software. Applied course appropriate for a general audience.
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3.00 Credits
Strategies of experimentation: randomization, blocking, sequential experimentation, replication. Design and analysis of experiments to collect nominal data (paired comparison, triangle tests), ordinal data (rating and ranking experiments) and numerical data (single and multiple factor experiments, fractional factorials). Use of statistical computing software. Applied course for a general audience.
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3.00 Credits
Concept of special versus common causes of variation. Construction and interpretation of control charts for attributes and variables data, Pareto charts, cause/effect diagrams, and process flow diagrams. Rational subgrouping issues. Process capability analysis and capability indices. Statistical tolerancing. Accuracy, precision and resolution of measurement processes. Quantifying components of variation. Introduction to design of experiments. Discussion of enumerative versus analytical statistical techniques.
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3.00 Credits
Numeric and graphic description of data, probability and probability distributions, simulation, and sampling distributions. Estimation and hypothesis testing for one and two samples, parametric and nonparametric approaches, and bootstrapping. Tests for count data, simple and multiple linear regression, diagnostics and validation, and analysis of variance. Uses SAS and other statistical software.
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3.00 Credits
Understanding and application of data mining methods. Data preparation, exploratory data analysis and visualization, cluster analysis, logistic regression, decision trees, neural networks, association rules, model assessment, and other topics. Applications to real world data. Use of standard computer packages.
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3.00 Credits
Model building techniques for linear time series models, practical methods for univariate time series forecasting, Box-Jenkins forecasting methods, forecasting based on exponential smoothing, autoregression and stepwise autoregression, and forecasting from regression models. Use of standard computing packages. Major writing requirement.
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