| Week |
Day |
Date |
Topic |
Lecture Slides |
R Software Session |
Homework |
Projects |
| 1 |
Tue. |
05.12 |
Course Overview and Information |
Slides 0 |
R Session 0 |
|
|
| Wed. |
05.13 |
Review of Simple Linear Regression |
Slides 1 |
R Session 1 |
|
|
| Thu. |
05.14 |
Multiple Linear Regression (MLR): Model Setup, Estimation |
Slides 2 |
R Session 2 |
R Lab 1 Due |
|
| Fri. |
05.15 |
MLR Inference: Individual t-Tests and General Linear F-Tests |
Slides 3 |
|
R Lab 2 Due |
|
| 2 |
Mon. |
05.18 |
MLR: Prediction, Prediction Uncertainty, and Multicollinearity |
|
R Session 3 |
|
|
| Tue. |
05.19 |
MLR: Model Selection |
Slides 4 |
|
R Lab 3 Due |
|
| Wed. |
05.20 |
MLR: Model Checking and Diagnostics |
|
R Session 4 |
|
|
| Thu. |
05.21 |
Linear Models with Continuous and Categorical Predictors |
Slides 5 |
|
R Lab 4 Due |
|
| Fri. |
05.22 |
Polynomial Regression and Nonlinear Regression |
|
R Session 5 |
|
|
| 3 |
Tue. |
05.26 |
Nonparametric Regression |
Slides 6 |
|
R Lab 5 Due |
|
| Wed. |
05.27 |
Shrinkage Methods in MLR: Ridge Regression and LASSO |
|
R Session 6 |
|
|
| Thu. |
05.28 |
Logistic Regression |
Slides 7 |
|
R Lab 6 Due |
|
| Fri. |
05.29 |
Poisson Regression and GLM Overview |
|
R Session 7 |
|
|
| 4 |
Mon. |
06.01 |
Introduction to Design of Experiments (DOE) |
Slides 8 |
R Session 8 |
|
|
| Tue. |
06.02 |
Completely Randomized Designs (CRDs) |
Slides 9 |
R Session 9 |
|
|
| Wed. |
06.03 |
Randomized Complete Block Design (RCBD) |
Slides 10 |
|
|
Project I Due |
| Thu. |
06.04 |
Factorial and Split-Plot Designs |
|
R Session 10 |
R Lab 7 Due |
|
| Fri. |
06.05 |
Random and Mixed Effects Models; Computer Experiments |
Slides 11 |
R Session 11 |
R Lab 8 Due |
|
| 5 |
Mon. |
06.08 |
Introduction to Time Series: Dependence, Stationarity, and Autocorrelation |
Slides 12 |
|
|
|
| Tue. |
06.09 |
Time Series Models: AR, MA, and ARMA Processes |
|
R Session 12 |
|
|
| Wed. |
06.10 |
Seasonal Seasonal Models: Model Building, Forecasting, and Diagnostic Checking |
Slides 13 |
|
R Lab 9 Due |
|
| Thu. |
06.11 |
Seasonal ARIMA (SARIMA): Case Study and Forecast Evaluation |
|
R Session 13 |
|
|
| Fri. |
06.12 |
Spatial Statistics: Spatial Dependence and Covariance Functions |
Slides 14 |
|
|
|
| 6 |
Mon. |
06.15 |
Spatial Statistics: Variograms, Empirical Estimation, and Model Fitting |
Slides 15 |
R Session 14 |
|
|
| Tue. |
06.16 |
Spatial Statistics: Kriging, Spatial Prediction, and Uncertainty Quantification |
|
R Session 15 |
|
|
| Wed. |
06.17 |
|
|
|
R Lab 10 Due |
|
| Thu. |
06.18 |
|
|
|
|
Project II Due |