Chae Y Lim (Instructor) - Grade Details
(with breakdown by course)
Chae Y Lim - All Courses
Average Grade - 3.210
Median Grade - 3.5
Latest grades from Spring 2015
Chae Y Lim - Overview
| Course Number | Grade Info | Number of Students | Latest Grade Data | Breakdown |
|---|---|---|---|---|
| STT 351 | Average Grade - 3.260 Median Grade - 3.5 |
292 | Fall 2013 | |
| STT 441 | Average Grade - 2.847 Median Grade - 3.5 |
95 | Fall 2013 | |
| STT 802 | Average Grade - 3.823 Median Grade - 4.0 |
31 | Fall 2014 | |
| STT 442 | Average Grade - 3.136 Median Grade - 3.5 |
176 | Spring 2015 | |
| STT 868 | Average Grade - 3.731 Median Grade - 4.0 |
26 | Spring 2015 |
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STT 351 - Probability and Statistics for Engineering
Probability models and random variables. Estimation, confidence intervals, tests of hypotheses, simple linear regression. Applications to engineering.
Average Grade - 3.260
Median Grade - 3.5
Latest grades from Fall 2013
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STT 441 - Probability and Statistics I: Probability
Probability, conditional probability and independence. Random variables. Discrete, continuous, univariate, and multivariate distributions. Expectation and its properties, moment generating functions. Law of large numbers, central limit theorem.
Average Grade - 2.847
Median Grade - 3.5
Latest grades from Fall 2013
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STT 442 - Probability and Statistics II: Statistics
Parameter estimation, sampling distributions, confidence intervals, hypothesis testing, simple and multiple regression, analysis of variance. Time series models, data analysis and forecasting
Average Grade - 3.136
Median Grade - 3.5
Latest grades from Spring 2015
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STT 802 - Statistical Computation
Computational techniques commonly used in Statistics. Matrix decompositions. Least squares and Least Absolute Deviations. Solution of nonlinear equations. Optimization techniques including the EM algorithm and constrained optimization. Numerical integration. Generation of random numbers and stochastic simulation. Implementation in statistical software.
Average Grade - 3.823
Median Grade - 4.0
Latest grades from Fall 2014
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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 - 3.731
Median Grade - 4.0
Latest grades from Spring 2015
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