SAS/STAT
| 名称 | 水平 | 培训形式 |
|---|---|---|
统计学 1: 方差分析回归与逻辑回归简介 
|
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 | |
|
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:
|
1 Beginner | |
|
Forecasting Using SAS Forecast Server Software
This course prepares you to generate large volumes of forecasts automatically using the SAS Forecast Studio interactive interface. This course includes practice data and exercises. This course supports both the desktop and client/server versions. Additional topics for students that license the client/server version of SAS Forecast Studio include producing reports using sample stored processes and a demonstration of SAS Time Series Studio. The self-study e-learning includes:
|
1 Beginner | |
|
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 | |
|
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 | |
|
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:
|
3 Intermediate | |
|
Categorical Data Analysis Using Logistic Regression
This course focuses on analyzing categorical response data in scientific fields. The SAS/STAT procedures addressed are PROC FREQ, PROC LOGISTIC, PROC VARCLUS, and PROC GENMOD. The ODS Statistical Graphics procedures used are PROC SGPLOT and PROC SGPANEL. The course is not designed for predictive modelers in business fields, although predictive modelers can benefit from the content of this course. |
3 Intermediate | |
|
SAS Enterprise Guide: 方差分析，回归与逻辑回归
本课程为使用SAS Enterprise Guide 执行统计学分析的用户定制。本课程为SAS9.4版本下的SAS Enterprise Guide 7.1 定制，但学员可以使用之前的SAS Enterprise Guide版本学习本课程。一个电子课程对An e- SAS Enterprise Guide 5.1 与 SAS Enterprise Guide 4.3 仍然有效。 |
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 | |
|
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 | |
|
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:
|
3 Intermediate | |
Multilevel Modeling of Hierarchical and Longitudinal Data Using SAS 
This course teaches how to identify complex and dynamic patterns within multilevel data to inform a variety of decision-making needs. The course provides a conceptual understanding of multilevel linear models (MLM) and multilevel generalized linear models (MGLM) and their appropriate use in a variety of settings. The self-study e-learning includes:
|
4 Advanced | |
|
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 | |
|
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 | |
|
Probability Surveys 1: Design, Descriptive Statistics, and Analysis
This course focuses on designing business and household surveys and analyzing data collected under complex survey designs. The course addresses the SAS procedures POWER, SURVEYSELECT, SURVEYMEANS, SURVEYFREQ, SURVEYREG, SURVEYLOGISTIC, and SURVEYIMPUTE. In addition, the graphing procedures GPLOT, SGPLOT, and SGPANEL are also covered. |
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 | |
|
Longitudinal Data Analysis Using Discrete and Continuous Responses
This course is for scientists and analysts who want to analyze observational data collected over time. It is not for SAS users who have collected data in a complicated experimental design. They should take the Mixed Models Analyses Using SAS course instead. The self-study e-learning includes:
|
4 Advanced | |
|
Statistical Analysis with the GLIMMIX Procedure
This course focuses on the GLIMMIX procedure, a procedure for fitting generalized linear mixed models. |
4 Advanced | |
|
聚类技术应用
本课程聚焦于SAS中广泛使用的集群技术的理论和实际意义。所关注的技术包括聚类预处理、变量聚类、k-均值聚类和分层聚类。 |
4 Advanced | |
|
使用逻辑回归进行预测建模
|
4 Advanced | |
|
Feature Engineering and Data Preparation for Analytics
This course introduces programming techniques to craft and feature engineer meaningful inputs to improve predictive modeling performance. In addition, this course provides strategies to preemptively spot and avoid common pitfalls that compromise the integrity of the data being used to build a predictive model. This course relies heavily on SAS programming techniques to accomplish the desired objectives. The self-study e-learning includes:
|
4 Advanced | |
|
Mixed Models Analyses Using SAS
This course teaches you how to analyze linear mixed models using the MIXED procedure. A brief introduction to analyzing generalized linear mixed models using the GLIMMIX procedure is also included. |
4 Advanced | |
|
Bayesian Analyses Using SAS
The course focuses on Bayesian analyses using the PHREG, GENMOD, and MCMC procedures. The examples include logistic regression, Cox proportional hazards model, general linear mixed model, zero-inflated Poisson model, and data containing missing values. A Bayesian analysis of a crossover design and a meta-analysis are also shown. The self-study e-learning includes:
|
4 Advanced | |