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Course Criteria
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3.00 Credits
Prerequisite(s): STA 2023 or Consent of the Instructor. Descriptive statistics; correlation; bivariate, multiple and logistic regression; reliability and validity; effect size, power, confidence intervals; one and two sample tests; ANOVA; categorical data analysis. Fall,Spring.
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3.00 Credits
Prerequisite(s): STA 4163 and knowledge of a programming language, graduate status or senior standing, or Consent of the Instructor. Use of SAS and other statistical software packages; data manipulation; graphical data presentation; data analysis; creating analytical reports. Fall.
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3.00 Credits
Prerequisite(s): STA 4322, graduate status or senior standing, or Consent of the Instructor. Full and partial credibility. The credibility premium. Exact credibility. Parametric and nonparametric estimation of credibility. Loss models for claim severities and frequencies. Aggregate claims models. Fall.
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3.00 Credits
Prerequisite(s): STA 2023, graduate status or senior standing, or Consent of the Instructor. Design and analysis of experiments with emphasis on biological/ecological application; one-way and multi-way ANOVA; regression; ordination; classification. Spring.
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3.00 Credits
Prerequisite(s): STA 4163 or STA 4173, graduate status or senior standing, or Consent of the Instructor. Fixedeffects model, random-effects model,repeated measures design, logistic regression, survival analysis, Kaplan-Meier estimates, proportional hazards model. Occasional.
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3.00 Credits
Prerequisite(s): STA 4164, STA 5206 or ESI 5219, and graduate status or senior standing, or Consent of the Instructor. Construction and analysis of designs for experimental investigations. Blocking, randomization, replication; Incomplete block designs; factorial and fractional designs; design resolution. Spring.
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3.00 Credits
Prerequisite(s): STA 2023; not open to students who have completed STA 4164. Graduate status or senior standing or Consent of the Instructor. Data analysis; statistical models; estimation; tests or hypotheses; analysis of variance, covariance, and multiple comparisons; regression and nonparametric methods. Fall.
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3.00 Credits
Prerequisite(s): STA 4163 or STA 5206, and graduate status or senior standing or Consent of the Instructor. Considers discrete probability distributions, contingency tables, measures of association, and advanced methods, including loglinear modeling, logistic regression, McNemar's Test, Mantel-Haenszel test. Occasional.
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3.00 Credits
Prerequisite(s): STA 4322 and STA 4641, graduate status or senior standing, or Consent of the Instructor. Individual risk rating and classification of risk for property/casualty insurance. Re insurance and expense issues. Reserves for insurance and loss adjustment expenses. Investment income. Occasional.
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3.00 Credits
Prerequisite(s): STA 5104 and STA 5206, graduate status or senior standing, or Consent of the Instructor. Data mining to uncover valuable information through SEMMA (Sample, Explore, Model, Modify, and Access). Process with neural network and decision tree. Fall.
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