SAS/ETS
| 名称 | 水平 | 培训形式 |
|---|---|---|
Establishing Causal Inferences: Propensity Score Matching, Heckman's Two-Stage Model, Interrupted Time Series, and Regression Discontinuity Models 
This course introduces some methods commonly used in program evaluation and real-world effectiveness studies, including two-stage modeling, interrupted time-series, regression discontinuity, and propensity score matching. These methods help address questions such as: Which medicine is more effective in the real world? Did an advertising program have an impact on sales? More generally, are the changes in outcomes causally related to the program being run? |
3 Intermediate |   |
|
统计学 2: 方差分析与回归
本课程教你如何分析连续响应数据和离散计数数据。线性回归、Poisson回归、负二项回归、Gamma回归、方差分析、指标变量线性回归、协方差分析和混合模型方差分析。 |
3 Intermediate |   |
|
Time Series Modeling Essentials
This course discusses the fundamentals of modeling time series data. The course focuses on the applied use of the three main model types used to analyze univariate time series: exponential smoothing, autoregressive integrated moving average with exogenous variables (ARIMAX), and unobserved components (UCM). The e-learning format of this course includes Virtual Lab time to practice. |
3 Intermediate | |
|
Stationarity Testing and Other Time Series Topics
This course addresses a basic question in time series modeling and forecasting: whether a time series is nonstationary. This question is addressed by the unit root tests. One of the most common tests, the Dickey-Fuller test, is discussed in this lecture. |
4 Advanced | |
|
State Space Modeling Essentials Using the SSM Procedure in SAS/ETS
This course covers the fundamentals of building and applying state space models using the SSM procedure (SAS/ETS). Students are presented with an overview of the model and learn advantages of the State Space approach. The course also describes fundamental model details, presents some straightforward examples of specifying and fitting models using the SSM procedure, and considers estimation in SSM, focusing on the Kalman filter and related details. The course concludes with a variety of SSM modeling applications, focused mainly on time series. |
4 Advanced | |
|
Forecasting Using SAS Software: A Programming Approach
This course teaches analysts how to use SAS/ETS software to diagnose systematic variation in data collected over time, create forecast models to capture the systematic variation, evaluate a given forecast model for goodness of fit and accuracy, and forecast future values using the model. Topics include Box-Jenkins ARIMA models, dynamic regression models, and exponential smoothing models. |
4 Advanced | |