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STAT:5200 (22S:164) APPLIED STATISTICS

computing environments, statistical packages, descriptive statsitics, basic inferential methods, confidence intervals, chi-square tests, linear models, regression, ANOVA models, specification, assumption, fitting, diagnostics, selection, testing, interpretation, regression analysis, involves modeling data, diagnostic methods, statistical inference, applied statistics course, data analysis, computing, communicating, statistics course, 

Finite Mixture Models – Latent Class Analysis Macready

 

• Univariate Distributions – Structured and Unstructured Mixtures, Multivariate Distributions – Structured and Unstructured Mixtures, SEM – Theoretical Foundations, Mixtures in SEM ‐ Estimation, Interpretation, Application, LGCM – Theoretical Foundations
• Growth Mixture Modeling– Estimation, Interpretation,, Application, Applications and/or Methodological Extensions, IRT – Theoretical Foundations, Nov Mixtures in IRT – Model Specification, Estimation, Interpretation, Latent DIF & Model‐Based Standard Setting –, Applications in Psychometrics, Nov Introduction to DCM –Theoretical Foundations, Applications – Estimation
• Interpretation, Computer Software, Applications and/or Method, Data, samples and populations, Graphical and numerical descriptions of data, Association between variables, Probability, probability distributions and the, normal distribution, Sampling, experiments, and observational, studies, Introduction to inferential statistics, Confidence interval for proportions
• Hypothesis tests for proportions, Using Technology, Inferences for multiple proportions, Confidence intervals and hypothesis, Inference for multiple means; ANOVA, Inference for linear regression, sampling distributions, probability, confidence intervals, t tests, ANOVA, correlation, regression, nonparametric statistics, data transformation, null hypothesis significance testing