STAT-4534: Applied Statistical Time Series Analysis
Description: Applied course in time series analysis methods. Topics include regression analysis, detecting and address autocorrelation, modeling seasonal or cyclical trends, creating stationary time series, smoothing techniques, forecasting and forecast errors, and fitting autoregressive integrated moving average models.
Pathways: N/A
Course Hours: 3 credits
Corequisites: N/A
Crosslist: N/A
Repeatability: N/A
Sections Taught: 18
Average GPA: 3.45 (A-)
Strict A Rate (No A-) : 52.26%
Average Withdrawal Rate: 1.48%
| Marco A Ferreira | 2022 | 96.7% | 2.5% | 0.5% | 0.0% | 0.0% | 0.5% | 3.96 | 3 |
| Clark K Gaylord | 2015 | 40.9% | 36.4% | 4.6% | 0.0% | 0.0% | 18.2% | 3.41 | 1 |
| William H Woodall | 2007 | 35.6% | 31.9% | 19.9% | 12.6% | 0.0% | 0.0% | 2.88 | 2 |
| Sara K Harrell | 2010 | 70.7% | 20.0% | 6.2% | 1.5% | 1.6% | 0.0% | 3.59 | 2 |
| Leigh M Williams | 2012 | 76.9% | 20.3% | 0.0% | 0.0% | 2.9% | 0.0% | 3.71 | 2 |
| Justin B Loda | 2020 | 89.1% | 10.8% | 0.0% | 0.0% | 0.0% | 0.0% | 3.86 | 1 |
| Gordon G Vining | 2018 | 46.5% | 30.8% | 14.7% | 3.2% | 2.4% | 2.4% | 3.14 | 3 |
| Julie Bessac | 2023 | 50.0% | 50.1% | 0.0% | 0.0% | 0.0% | 0.0% | 3.44 | 1 |
| Khouly Mohamed I El | 2019 | 60.4% | 28.9% | 5.5% | 1.6% | 3.5% | 0.0% | 3.36 | 2 |
| Stephen C Loftus | 2016 | 18.9% | 64.8% | 13.5% | 2.7% | 0.0% | 0.0% | 2.95 | 1 |