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BIST-511 Statistical Inference
Fall only
This course will introduce the basics of statistical inference, parameter estimation, and hypothesis testing in preparation for more in depth coverage of specific models in later courses. Inference procedures: point and interval estimation, sufficient statistics, hypothesis testing, methods of constructing test and estimation procedures. Point estimation: criteria for estimators, maximum likelihood estimators, Bayes estimators, mean square error, unbiased estimators, asymptotic variance of estimators. Hypothesis testing: error probabilities, power function, one-sample inference about the mean with known and unknown variance, comparison of two samples, 2×2 contingency tables, shortcuts and non-parametric methods. Modeling and study design: missing data, extreme observations, transformations, factorial experiments, probability sampling, sample size, two-stage sampling, stratified sampling, nonsampling errors.
Credits: 3
Prerequisites: Calculus of several variables, matrix theory
Sections:
BIST-511-01 Statistical Inference
Fall only
This course will introduce the basics of statistical inference, parameter estimation, and hypothesis testing in preparation for more in depth coverage of specific models in later courses. Inference procedures: point and interval estimation, sufficient statistics, hypothesis testing, methods of constructing test and estimation procedures. Point estimation: criteria for estimators, maximum likelihood estimators, Bayes estimators, mean square error, unbiased estimators, asymptotic variance of estimators. Hypothesis testing: error probabilities, power function, one-sample inference about the mean with known and unknown variance, comparison of two samples, 2×2 contingency tables, shortcuts and non-parametric methods. Modeling and study design: missing data, extreme observations, transformations, factorial experiments, probability sampling, sample size, two-stage sampling, stratified sampling, nonsampling errors.
Credits: 3
Prerequisites: Calculus of several variables, matrix theory
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