SAS/STAT

標題	等級	培訓格式
Applied Clustering Techniques 本課程對各種集群分析方法的概念與SAS 語法的實作進行全面性的介紹，其中包含資料的準備，變數的篩選，k-means分群法和階層式分群法。	4 Advanced
Bayesian Analyses Using SAS 本課程著重在利用不同模型程序進行貝氏分析，包含羅吉斯迴歸模型、Cox 比例涉險模型、廣義線性混合模型、零膨脹卜瓦松模型等。課程中也會提到交叉設計與整合分析中的貝氏分析原理。	4 Advanced
Categorical Data Analysis Using Logistic Regression 本課程著重於類別型資料分析方法，包含列聯表檢定，二元羅吉斯迴歸，多元羅吉斯迴歸等。	3 Intermediate
Conjoint Analysis: Evaluating Consumer Preferences Using SAS Software This course discusses a method in marketing research called conjoint analysis that is used to analyze consumer preferences for products and services.The e-learning version of this course includes data so that you can practice the software demonstration steps in your own SAS environment.	3 Intermediate
Discrete Choice Modeling Using SAS Software This marketing research course shows how to design a discrete choice experiment and how to analyze discrete choice data in SAS software. Analytical advice regarding number of choice sets, the number of alternatives, and number of subjects is also given. This course includes practice data and exercises.	3 Intermediate
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
Feature Engineering and Data Preparation for Analytics 本課程介紹如何利用SAS 程式語法進行特徵工程，萃取出有意義的輸入變數，以增進預測模型的表現。同時課程中也會對建模過程常見的資料選取陷阱進行說明，並提供避免踏入陷阱的適合策略。	4 Advanced
Fitting Poisson Regression Models Using the GENMOD Procedure This course is for those who analyze the number of occurrences of an event or the rate of occurrence of an event as a function of some predictor variables. For example, the rate of insurance claims, colony counts for bacteria or viruses, the number of equipment failures, and the incidence of disease can be modeled using Poisson regression models. This course includes practice data and exercises.	3 Intermediate
Forecasting Using SAS Forecast Server Software 本課程介紹如何利用SASForecast Studio 進行大量且自動化的時間序列預測與分析，課程包含實機操作講解與練習題。單機版或client/server 版使用者皆適合此課程。	1 Beginner
Introduction to Statistical Concepts This course covers basic statistical concepts that are critical for understanding and using statistical methods. This course explains what statistics is and why it is important to understand the characteristics of your data. The information in this course is a prerequisite for many other statistical courses that SAS Education offers. The course is appropriate for Base SAS and SAS Enterprise Guide users. Data, practices, and a case study are included.	1 Beginner
Managing SAS Analytical Models Using SAS Model Manager Version 14.2 This course focuses on the following key areas: managing SAS Model Manager data sources, creating a SAS Model Manager project, importing models into SAS Model Manager, using the SAS Model Manager Query Utility, creating scoring tasks, exporting models and projects into a SAS repository, and creating and configuring version life cycles. The course also covers generating SAS Model Manager model comparison reports, publishing and deploying SAS Model Manager models, creating SAS Model Manager production model monitoring reports, and creating user-defined reports. The self-study e-learning includes: Annotatable course notes in PDF format. Virtual Lab time to practice.	3 Intermediate
Mixed Models Analyses Using the SAS System	4 Advanced
Multivariate Statistics for Understanding Complex Data This course teaches how to apply and interpret a variety of multivariate statistical methods to research and business data. The course emphasizes understanding the results of the analysis and presenting your conclusions with graphs.	4 Advanced
Predictive Modeling Using Logistic Regression 本課程的範圍主要著重於使用SAS/STAT 中的LOGISTIC 程序建置預測模型。本課程亦將探討如何使用有效率的技巧處理大量資料，選擇變數、評估模型、處理遺失值的問題。	4 Advanced
Programming with SAS/IML Software This course teaches you how to use the IML procedure via the programming language. You benefit from this course if you plan to use SAS/IML for manipulating matrices, simulating data, writing custom statistical analyses, or working with R. The programs in this course require SAS/IML 12.3 or later to run. The self-study e-learning includes: Annotatable course notes in PDF format. Virtual lab time to practice.	1 Beginner
Robust Regression Techniques in SAS/STAT This course is designed for analysts, statisticians, modelers, and other professionals who have experience and knowledge in regression analysis and who want to learn available procedures in SAS/STAT software for robust regression. The two procedures addressed in the course are the ROBUSTREG procedure and the QUANTREG procedure. This course includes practice data.	4 Advanced
SAS Enterprise Guide: ANOVA, Regression, and Logistic Regression 本課程內容包含許多統計分析方法介紹，以及如何使用SAS Enterprise Guide執行這些統計分析，主題包括推論性統計介紹、變異數分析、多元迴歸、類別資料分析和邏輯斯迴歸。	3 Intermediate
SAS Programming for R Users This course is for experienced R users who want to apply their existing skills and extend them to the SAS environment. Emphasis is placed on programming and not statistical theory or interpretation. Students in this course should have knowledge of plotting, manipulating data, iterative processing, creating functions, applying functions, linear models, generalized linear models, mixed models, stepwise model selection, matrix algebra, and statistical simulations.	3 Intermediate
Statistical Analysis with the GLIMMIX Procedure This course focuses on the GLIMMIX procedure, a procedure for fitting generalized linear mixed models.	4 Advanced
Statistical Process Control Using SAS/QC Software This course is designed for professionals who use quality control or SPC methods to monitor, evaluate, and improve the quality of their processes. It is an ideal statistical training module to complement or supplement corporate quality training programs and Six Sigma programs. The self-study e-learning includes: Annotatable course notes in PDF format. Virtual Lab time to practice.	3 Intermediate
Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression 本課程涵蓋了一系列的統計主題和使用 SAS 軟體進行統計分析。課程著重於T 檢定，變異數分析(ANOVA)，線性迴歸和邏輯斯迴歸。	1 Beginner
Survival Analysis Using the Proportional Hazards Model 本課程對醫療保健領域的存活分析原理與分析方法進行討論，課程主要聚焦在Cox比例風險模型的理論與應用上。	3 Intermediate
Survival Data Mining: A Programming Approach This advanced course discusses predictive hazard modeling for customer history data. Designed for data analysts, the course uses SAS/STAT software to illustrate various survival data mining methods and their practical implementation. Note: Formerly titled Survival Data Mining: Predictive Hazard Modeling for Customer History Data, this course now includes hands-on exercises so that you can practice the techniques that you learn. Other additions include a chapter on recurrent events, new features in SAS/STAT software, and an expanded section that compares discrete time approach versus the continuous time models such as Cox Proportional Hazards models and fully parametric models such as Weibull.	4 Advanced