Courses
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SSC 303. Statistics in Experimental Research. An introduction to the fundamental concepts and methods of statistics emphasizing applications in experimental science. Exploratory data analysis, correlation and regression, descriptive statistics, sampling distributions, confidence intervals and hypothesis testing. Students may receive credit for only one of the following: Statistics and Computation 303, 304, 305, 306 or Mathematics 316.
SSC 304. Statistics in Health Care. An introduction to the fundamental concepts and methods of statistics emphasizing applications in the health sciences. Exploratory data analysis, correlation and regression, descriptive statistics, sampling distributions, confidence intervals and hypothesis testing. Students may receive credit for only one of the following: Statistics and Computation 303, 304, 305, 306 or Mathematics 316.
SSC 305. Statistics in Policy Design. An introduction to the fundamental concepts and methods of statistics emphasizing applications in policy evaluation and design. Exploratory data analysis, correlation and regression, descriptive statistics, sampling distributions, confidence intervals and hypothesis testing. Students may receive credit for only one of the following: Statistics and Computation 303, 304, 305, 306 or Mathematics 316.
SSC 306. Statistics in Market Analysis. An introduction to the fundamental concepts and methods of statistics emphasizing applications in the analysis of individual and group behaviors. Exploratory data analysis, correlation and regression, descriptive statistics, sampling distributions, confidence intervals and hypothesis testing. Students may receive credit for only one of the following: Statistics and Computation 303, 304, 305, 306 or Mathematics 316.
SSC 110T, 210T, 310T, 410T. Topics in Statistics and Computation. For each credit hour, one hour per week for one semester. May be repeated for credit when the topic varies.
SSC 321. Introduction to Probability and Statistics. Basic theory of probability and statistics with practical applications. Includes fundamentals of probability, distribution theory, sampling models, data analysis, basics of experimental design, statistical inference, interval estimation and hypothesis testing. Three lecture hours and one discussion hour a week for one semester. Prerequisite: Mathematics 408D or 408L with a grade of at least C. Students may receive credit for only one of the following: Statistics and Computation 321 or 323, or Mathematics 358K.
SSC 318M. Biostatistics. Basic theory of probability and statistics with practical applications with biological data. Includes fundamentals of probability, distribution theory, sampling models, data analysis, basics of experiemental design, statistical inference, interval estimation and hypothesis testing. Three lecture hours and one discussion hour a week for one semester. Prerequisite: Mathematics 408D or 408L with a grade of at least C. Students may receive credit for only one of the following: Statistics and Computation 321 or 323, or Biology 318M, or Mathematics 358K.
SSC 150K. Data Analysis Applications. Introduction to the use of statistical or mathematical applications for data analysis. Two hours per week for eight weeks. May be repeated for credit when the topics vary. Offered on the credit/no credit basis only. Prerequisites vary with the topic and are given in the Course Schedule.
Topic 1: SPSS
Topic 2: SAS
Topic 3: STATA
Topic 4: Selected Topics
SSC 352. Statistical Methods. Covers simple and multiple regression, fundamentals of experimental design, and analysis of variance methods. Other topics will be selected from the following: logistic regression, Poisson regression, resampling methods, introduction to Bayesian methods, and probability models. Includes substantial use of statistical software. Three lecture hours and one laboratory hour a week for one semester. Prerequisite: Statistics and Computation 303, 304, 305, 306, or Mathematics 316.
SSC 358. Special Topics in Statistics. May be repeated for credit when the topics vary. Prerequisite: Upper-division standing; additional prerequisites may vary with the topic and are given in the Course Schedule.
SSC 380C. Data Analysis I. Applied statistics for students in the social sciences who anticipate the need to apply statistics in their future work. Topics include descriptive statistics, exploratory data analysis, probability concepts, sampling distributions, bivariate correlation and regression, statistical inference, analysis of variance and non-parametric tests. Use of statistical software is emphasized. Prerequisite: Graduate standing.
SSC 380D. Data Analysis II. A second course in applied statistics for students in the social sciences, emphasizing design and analysis of experiments. Regression with several explanatory variables, including mixed models; analysis of variance for factorial designs; multiple comparisons; analysis of covariance; repeated measures designs. Extensive use of statistical software. Three lecture hours and one laboratory hour a week for one semester. Prerequisite: Graduate standing, and Statistics and Computation 380C or the equivalent.
SSC 381. Mathematical Methods for Statistical Analysis. Introduction to mathematical concepts and methods essential for multivariate statistical analysis. Topics include basic matrix algebra, eigenvalues and eigenvector, quadratic forms, vector and matrix differentiation, unconstrained optimization, constrained optimization, and applications in multivariate statistical analysis. Prerequisite: Graduate standing and one course in statistics.
SSC 382. Introduction to Probability and Statistics. Expectation and variance of random variables, conditional probability and independence, sampling distributions, point estimation, confidence intervals, hypothesis tests, and other topics. Prerequisite: Graduate standing, and M408D or M408L.
SSC 183K. Data Analysis Applications. Introduction to the use of statistical or mathematical applications for data analysis. Two hours per week for eight weeks. May be repeated for credit when the topics vary. Offered on the credit/no credit basis only. Prerequisite: Graduate Standing.
Topic 1: SPSS
Topic 2: SAS
Topic 3: STATA
Topic 4: Selected Topics
SSC 384: Topics in Statistics and Probablility. Concepts of probability and mathematical statistics with applications in data analysis and research. May be repeated for credit when the topics vary. Prerequisite: Graduate standing, and Statistics and Computation 382, Mathematics 362K and 378K, or consent of instructor.
Topic 1: Applied Probability. Basic probability theory, combinatorial analysis of random phenomena, conditional probability and independence, parametric families of distributions, expectation, distribution of functions of random variables, limit theorems.
Topic 2: Mathematical Statistics I. The first semester of a two-semester course covering the general theory of mathematical statistics. The two-semester course covers distributions of functions of random variables, properties of a random sample, principles of data reduction, overview of hierarchical models, decision theory, Bayesian statistics, and theoretical results relevant to point estimation, interval estimation, and hypothesis testing.
Topic 3: Mathematical Statistics II. A continuation of Statistics and Computation 384 (Topic 1). Additional prerequisite: Statistics and Computation 384 (Topic 1).
Topic 4: Regression Analysis. Simple and multiple linear regression, inference in regression, prediction of new observations, diagnostics and remedial measures, transformations, model building. Emphasis will be on both understanding the theory and applying theory to analyze real data.
Topic 5: Multivariate Statistical Analysis. Introduction to the general multivariate linear model: a selection of techniques including principle components, factor analysis, and discriminant analysis.
Topic 6: Design and Analysis of Experiments. Design and analysis of experiments, including one-way and two-way layouts; components of variance; factorial experiments; balanced incomplete block designs; crossed and nested classifications; fixed, random, and mixed models; split plot designs.
Topic 7: Bayesian Statistical Methods. Fundamentals of Bayesian inference in single and multi-parameter models for inference and decision making, including simulation of posterior distributions, Markov chain Monte Carlo methods, hierarchical models, and empirical Bayes models.
Topic 8: Time Series Analysis. Introduction to statistical time series analysis: ARIMA and more general models, forecasting, spectral analysis, and time domain regression. Model identification, estimation of parameters, and diagnostic checking are included. Additional Prerequisite: Statistics and Computation 384 (Topic 3) and consent of instructor.
Topic 9: Computational Statistics. A course in modern computationally-intensive statistical methods including simulation, optimization methods, Monte Carlo integration, maximum likelihood / EM parameter estimation, Markov chain Monte Carlo methods, resampling methods, non-parametric density estimation.
Topic 10: Stochastic Processes. Concepts and techniques of stochastic processes with an emphasis on the nature of change of variables with respect to time. Characterization, structural properties and inference are covered.
Topic 11: Selected Topics. Additional prerequisite: Consent of instructor.
SSC 385: Topics in Applied Statistics. Theories, models and methods for the analysis of quantitative data. With consent of the graduate advisor, may be repeated for credit when the topics vary. Prerequisite: Graduate standing, and Statistics and Computation 380 or 382 or consent of instructor.
Topic 1: Experimental Design. Principles, construction and analysis of experimental designs. Includes one-way classification, randomized blocks, Latin squares, factorial and nested designs. Fixed and random effects, multiple comparisons, and analysis of covariance. Additional prerequisite: Data Analysis I or its equivalent.
Topic 2: Applied Regression. Simple and multiple linear regression, residual analysis, transformations, model building with real data, testing models. Additional prerequisite: Experiemental Design or its equivalent.
Topic 3: Applied Multivariate Methods. A practical introduction to the analysis of multivariate data as applied to examples from the social sciences. Multivariate linear model, principal components and factor analysis, discriminant analysis, clustering and canonical correlation. Additional prerequisite: Applied Multivariate Methods or its equivalent.
Topic 4: Analysis of Categorical Data. Methods for analyzing categorical data. Topics include categorical explanatory variables within the General Linear Model; models of association among categorical variables; models in which the response variable is categorical or is a count. Logical similarities across methods will be emphasized.
Topic 5: Structural Equation Modeling. Introduction to the basic concepts, methods and computing tools of structural equation modeling. Emphasis will be placed on developing a working familiarity with some of the common statistical procedures, coupled with their application through the use of statistical software. Additional prerequisite: Applied Regression or its equivalent.
Topic 6: Hierarchical Linear Models. Introduction to multilevel data structures, model building and testing, effect size, fixed and random effects, missing data and model assumptions, logistic HLM, statistical power, and design planning. Additional prerequisite: Applied Regression or its equivalent.
Topic 7: Survey Sampling and Methodology. Survey planning, execution and analysis. Principles of survey research, including sampling, measurement; questionnaire construction and distribution; response effects; validity and reliability; scaling data sources; data reduction and analysis.
Topic 8: Introduction to Bayesian Methods. A practical introduction to Bayesian statistical interference, with an emphasis on applications in behavioral and measurement research. Examination of how Bayesian statistical inference differs from classical inference in the context of simple statistical procedures and models, such as hypothesis testing, ANOVA and regression. Additional prerequisite: Applied Regression or its equivalent.
Topic 9: Longitudinal Data Analysis. Applications of models to data collected at successive points in time. Multilevel models for change, random coefficient models; latent growth curve models; models for nonlinear growth. applications of models to event-occurrence data. Discrete-time and continuous-time event history models.
Topic 10: Modern Statistical Methods. An introduction to conducting statistical analysis using modern resampling methods of bootstrapping and Monte Carlo simulation. Equal emphasis will be placed on theoretical understanding and application.
Topic 11: Mathematical Statistics for Applications. Introduction to the basic concepts of probability and mathematical statistics for doctoral degree students who plan to use statistical methods in their research but do not need a highly mathematical development of the subject. Topics include probability distributions and estimation theory and hypothesis testing techniques. Additional prerequisite: A calculus course covering integration and differentiation.
Topic 12: Meta-Analysis. An introduction to statistics used to synthesize statistical results from a set of studies. Course content can include calculation of different effect sizes, calculating pooled estimates using fixed and random effects models, testing moderating variables using fixed and mixed effects models, test of heterogeneity of effect sizes, assessing and correcting publication bias. Additional prerequisite: Applied Regression (Topic 2) or the equivalent.
Topic 13: Factor Analysis. An introduction to exploratory and confirmatory factor analysis. Exploratory factor analysis section's content can include review of matrix algebra and vector geometry, principal components and principle axis factoring, factor rotation methods. Confirmatory factor analysis section's content includes single- and multiple-factor, multi-sample models, multi-trait-multi-method method and latent means modeling. For both methods, experience will be provided in writing up and critiquing others' studies. Additional prerequisite: Applied Regression (Topic 2) or the equivalent.
Topic 14: Maximum-Likelihood Statistics. Introduction to the likelihood theory of statistical inference. Topics include probability distributions, estimation theory, and applications of the MLE to models with categorical or limited dependent variables, even count models, event history models, models for time-series cross-section data, and models for hierarchical data.
Topic 15: Selected Topics.
SSC 388. Consulting Seminar. Supervised experience in applying statistical or mathematical methods to real problems. Participation in weekly consulting sessions; directed readings in the statistical literature; the ethics of research and consulting; report writing and presentations. May be repeated for credit. Prerequisite: Graduate standing, and consent of instructor.