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BIST-512 Statistical Modeling I
Spring only
3 modules: regression and ANOVA, generalized linear models, longitudinal models.
Lab teaches SAS procedures to fit these models.
Regression and ANOVA : fitting a linear regression, least squares method, regression in observational studies, estimation and prediction of Y for a given X, predicted sample mean of Y, testing large deviation, no intercept model, correlation coefficient, bivariate normal distribution, testing single correlation, comparing correlations, rank correlation, 2×C contingency tables, linear trend, R×C contingency tables, fixed-effect ANOVA, model notation, F-test, planned comparisons, orthogonal comparisons, random effects model, homogeneity of variance, randomized blocks, latin squares.
Generalized Linear Models : exponential family, proportion, count and rate data, link functions, estimation, logistic and Poisson regressions, fixed and random effects, repeated measures, model selection.
Longitudinal Models: generalized estimating equations, estimation, clustered data, linear mixed models, model fit.
Credits: 3
Prerequisites: None
Sections:
BIST-512-01 Statistical Modeling I
Spring only
3 modules: regression and ANOVA, generalized linear models, longitudinal models.
Lab teaches SAS procedures to fit these models.
Regression and ANOVA : fitting a linear regression, least squares method, regression in observational studies, estimation and prediction of Y for a given X, predicted sample mean of Y, testing large deviation, no intercept model, correlation coefficient, bivariate normal distribution, testing single correlation, comparing correlations, rank correlation, 2×C contingency tables, linear trend, R×C contingency tables, fixed-effect ANOVA, model notation, F-test, planned comparisons, orthogonal comparisons, random effects model, homogeneity of variance, randomized blocks, latin squares.
Generalized Linear Models : exponential family, proportion, count and rate data, link functions, estimation, logistic and Poisson regressions, fixed and random effects, repeated measures, model selection.
Longitudinal Models: generalized estimating equations, estimation, clustered data, linear mixed models, model fit.
Credits: 3
Prerequisites: Introductory Statistics: confidence interval, hypothesis testing; BIST-511
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