Haolei Weng (Instructor) - Grade Details
(with breakdown by course)
Haolei Weng - All Courses
Average Grade - 3.732
Median Grade - 4.0
Latest grades from Spring 2024
Haolei Weng - Overview
| Course Number | Grade Info | Number of Students | Latest Grade Data | Breakdown |
|---|---|---|---|---|
| STT 867 | Average Grade - 3.777 Median Grade - 4.0 |
56 | Fall 2023 | |
| STT 465 | Average Grade - 3.483 Median Grade - 4.0 |
30 | Fall 2023 | |
| STT 864 | Average Grade - 3.577 Median Grade - 3.5 |
13 | Spring 2020 | |
| STT 868 | Average Grade - 3.727 Median Grade - 4.0 |
56 | Spring 2024 | |
| STT 953 | Average Grade - 4.000 Median Grade - 4.0 |
12 | Spring 2022 | |
| STT 951 | Average Grade - 4.000 Median Grade - 4.0 |
14 | Spring 2023 |
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STT 465 - Bayesian Statistical Methods
Probability, belief, and exchangeability. Objective, subjective, and empirical Bayes approaches. Applications to one-parameter models, linear regression models, and multivariate normal models. Hierarchical modeling. Computational methods.
Average Grade - 3.483
Median Grade - 4.0
Latest grades from Fall 2023
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STT 864 - Statistical Methods II
Generalized linear models(GLMs). Deviance and residual analysis in GLMs. Analysis of two-way and three-way contingency tables. Logistic regression. Log-linear models. Multicategorical response models. Poisson regression. Introduction to generalized estimating equations. Introduction to longitudinal data. Bayesian analysis using WinBUGS.
Average Grade - 3.577
Median Grade - 3.5
Latest grades from Spring 2020
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STT 867 - Linear Model Methodology
Properties of the multivariate normal distribution, Cochran's Theorem, simple and multiple linear regression models, Gauss-Markov Theorem, best linear unbiased prediction, one- and two-way ANOVA models, sums of squares, diagnostics and model selection, contingency tables and multinomial models, generalized linear models, logistic regression.
Average Grade - 3.777
Median Grade - 4.0
Latest grades from Fall 2023
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STT 868 - Mixed Models: Theory, Methods and Applications
Maximum likelihood estimation and other estimation methods for linear mixed models. Statistical properties of LME models. Prediction under LME models. Generalized linear mixed models. Quasi-likelihood estimation, generalized estimating equations for GLMM. Nonlinear mixed models. Diagnostics and influence analysis. Bayesian development in mixed linear models. Application of mixed models.
Average Grade - 3.727
Median Grade - 4.0
Latest grades from Spring 2024
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STT 951 - Statistical Inference II
Decision theoretic estimation: Minimaxity, admissibility, shrinkage estimators, James-Stein estimators. Advanced estimation theory, maximal invariant tests, multiple testing, FDR, and related methods. Permutation and rank tests, unbiasedness and invariance, Hunt Stein theorem.
Average Grade - 4.000
Median Grade - 4.0
Latest grades from Spring 2023
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STT 953 - Asymptotic Theory
Locally asymptotic normal models, empirical likelihood, U-statistics, Asymptotically efficient and adaptive procedures
Average Grade - 4.000
Median Grade - 4.0
Latest grades from Spring 2022
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