SAS/STAT

コース名	レベル	受講形態
Applied Clustering Techniques The course looks at the theoretical and practical implications of a wide array of clustering techniques that are currently available in SAS. The techniques considered include cluster preprocessing, variable clustering, k-means clustering, and hierarchical clustering.	4 上級
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 中級
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 中級
Design of Experiments for Direct Marketing This course deals with the concepts and techniques that are used in the design and analysis of experiments. The course primarily focuses on direct marketing applications, but it is also relevant for someone interested in designing experiments in the fields of physical, chemical, biological, medical, economic, social, psychological, and industrial sciences; engineering; or agriculture. This course teaches you how to design efficient marketing experiments with more than one factor, analyze the results that your experiments yield, and maximize the information that is gleaned from a marketing campaign. Factorial and fractional factorial designs are discussed in greater detail.	4 上級
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 中級
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 中級
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: Annotatable course notes in PDF format. Virtual Lab time to practice.	4 上級
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 中級
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: Annotatable course notes in PDF format. Virtual Lab time to practice.	1 入門
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 入門
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 SASによる混合効果モデル course instead. The self-study e-learning includes: Annotatable course notes in PDF format. Virtual lab time to practice.	4 上級
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 中級
Models for Time Series and Sequential Data This course teaches students to build, refine, extrapolate, and, in some cases, interpret models designed for a single, sequential series. There are three modeling approaches presented. The traditional, Box-Jenkins approach for modeling time series is covered in the first part of the course. This presentation moves students from models for stationary data (or ARMA) to models for trend and seasonality (ARIMA) and concludes with information about specifying transfer function components in an ARIMAX, or time series regression, model. A Bayesian approach to modeling time series is considered next. The basic Bayesian framework is extended to accommodate autoregressive variation in the data as well as dynamic input variable effects. Machine learning algorithms for time series is the third approach. Gradient boosting and recurrent neural network algorithms are particularly well suited for accommodating nonlinear relationships in the data. Examples are provided to build intuition on the effective use of these algorithms. The course concludes by considering how forecasting precision can be improved by combining the strengths of the different approaches. The final lesson includes demonstrations of creating combined (or ensemble) and hybrid model forecasts.	3 中級
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: Annotatable course notes in PDF format. Virtual lab time to practice.	4 上級
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 上級
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 上級
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 入門
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 上級
SAS Enterprise Guide: ANOVA, Regression, and Logistic Regression This course is designed for SAS Enterprise Guide users who want to perform statistical analyses. The course is written for SAS Enterprise Guide 8 along with SAS 9.4, but students with previous SAS Enterprise Guide versions will also get value from this course.	3 中級
SAS Enterprise Guideによる分散分析本コースは、統計分析をSAS Enterprise Guideで行いたい方向けのコースです。分散分析に焦点が当てられています。	1 入門
SAS Enterprise Guideによる回帰分析本コースは、統計分析をSAS Enterprise Guideで行いたい方向けに作成されています。	1 入門
SAS Enterprise Guide による統計解析本コースは、統計分析をSAS Enterprise Guideで行いたい方向けのコースです。統計学の基礎に焦点が当てられています。	1 入門
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 中級
SASによるベイズ解析【注意】本コースは、英語版テキストを使用し日本語で説明を行います。本コースは、PHREG、GENMOD、MCMCプロシジャを使用した、ベイズ分析に焦点をあてています。多くの事例は、臨床試験の分野からご紹介します。	4 上級
SASによる一般化線形混合効果モデル（PROC GLIMMIX）【注意】本コースは、英語版テキストを使用し日本語で説明を行います。またサービスチケットのご利用はいただけません。	4 上級
SASによる分散分析本コースは、SAS/STAT®を使用して統計解析を行うSASのユーザー向けに用意されており、t検定や分散分析に焦点が当てられています。本コースの（またはそれと同等の）知識は、統計解析のカリキュラムにおける他のコースで必須の条件となります。本コースは、SAS認定資格「SAS® Certified Professional:Clinical Trials Programming Using SAS 9.4」および「SAS Certified Statistical Business Analyst Using SAS®9: Regression and Modeling」の準備にも適しています。	0 －
SASによる回帰分析本コースは、SAS/STAT®を使用して統計解析を行うSASのユーザー向けです。線形回帰、およびロジスティック回帰に焦点が当てられています。本コースの（またはそれと同等の）知識は、統計解析のカリキュラムにおけるコースの多くで必須な条件となります。本コースは、SAS認定資格「SAS® Certified Professional:Clinical Trials Programming Using SAS 9.4」および「SAS Certified Statistical Business Analyst Using SAS®9: Regression and Modeling」の準備にも適しています。	0 －
SASによる因果推論入門：傾向スコア解析ランダム化比較試験は、治療や介入の効果を検証するための最も強力な研究デザインの一つですが、現実にはランダム化が不可能だったり、結果の一般化には不十分だったりします。そこで、ランダム化を伴わない観察研究もこうした評価に不可欠となりますが、「交絡」とよばれる現象のために結果にバイアスを生じる可能性があります。本コースでは、「交絡」の調整を目的として観察研究で使用例の増えている傾向スコア（プロペンシティスコア）の正しい理解と、基本的な解析方法（層別、マッチング、重み付け）の理論的・実践的解説を行います。また、特別コース「SASによる応用的因果解析」の入門的位置づけとして、Proc PSMATCHも一部演習に取り入れます。講師は、「東京理科大学工学部情報工学科　講師篠崎智大氏」が担当します。	3 中級
SASによる応用的因果解析「SASよる因果推論入門：傾向スコア解析」では、ランダム化を伴わない観察研究における「交絡」という現象と、それに対処するための統計解析手法として、特に傾向スコア（プロペンシティスコア）の働く原理、またそれを用いた交絡調整法（マッチング、条件付け、重み付け）をSASプログラムとともに解説しています。本コースでは、これらの知識を下敷に、より踏み込んだ解析手法の習得を目的とします。SAS/STAT 14.2より実装されたPSMATCHプロシジャ（広範なマッチングまたは重み付け手法の実行）、CAUSALTRTプロシジャ（平均因果効果のセミパラメトリック推定）の紹介とともに、これらのプロシジャだけでは対応できない、１）時間とともに変化する曝露変数の効果推定や、２）アウトカムに打ち切りを含む場合の解析手法など解説します。	4 上級
SASによる混合効果モデル【注意】本コースは、英語版テキストを使用し日本語で説明を行います。	4 上級
SASによる統計解析本コースは、SAS/STAT® を使用して統計解析を行うSASのユーザー向けです。t検定、線形回帰、及び集計表の解析に焦点が当てられています。本コースの（またはそれと同等の）知識は、統計解析のカリキュラムにおけるコースの多くで必須な条件となります。本コースは、SAS認定資格「SAS® Certified Professional:Clinical Trials Programming Using SAS 9.4」および「SAS Certified Statistical Business Analyst Using SAS®9: Regression and Modeling」の準備にも適しています。	0 －
SASによる統計解析1：基礎編（回帰分析、分散分析）本コースは、SAS/STAT®を使用して統計解析を行うSASユーザーを対象とした入門コースです。 t検定、ANOVA、線形回帰に焦点を当て、ロジスティック回帰の簡単な紹介も含まれています。本コース（または同等の知識）は、統計解析カリキュラムの多くのコースの前提条件となっています。より高度な分散分析および回帰分析については、「SASによる統計解析2：分散分析、回帰分析」のコースで扱います（2023年8月以降リリース予定）。ロジスティック回帰のより高度な取り扱いは、「ロジスティック回帰による予測モデリング」で行われます。	1 入門
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 中級
Statistics 2: ANOVA and Regression This course teaches you how to analyze continuous response data and discrete count data. Linear regression, Poisson regression, negative binomial regression, gamma regression, analysis of variance, linear regression with indicator variables, analysis of covariance, and mixed models ANOVA are presented in the course.	3 中級
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 上級
Using SAS Forecast Server Procedures This course teaches you how to create and manage a complete forecasting system using the SAS Forecast Server procedures, giving you the power to confidently plan your business operations.	3 中級
ロジスティック回帰による予測モデリング本コースでは、SAS/STAT®、特にLOGISTICプロシジャの利用に焦点をあてて予測モデリングを行います。変数選択、モデルの評価、欠損値の扱い、大規模データに対して有効なテクニックなどについても議論しています。本コースは、SAS認定試験「SAS®認定プロフェッショナル Statistical Business Analyst Using SAS®9 :Regression and Modeling」の準備にも適しています。なお、e-Learningの内容は本ページ記載の内容と異なるため、こちらのカタログ P43をご参照ください。	4 上級
医薬向けカテゴリカルデータ解析 2 x 2表の解析（相対リスク、オッズ比など）や、r x c 表の解析（χ2乗検定）、また層別因子がある場合の解析（CMH検定、調整済オッズ比など）を、FREQプロシジャを用いて実行する方法について説明します。	3 中級
比例ハザードモデルを使用した生存時間解析【注意】本コースは、英語版テキストを使用し日本語で説明を行います。本コースでは、ヘルスケアの問題に重点を置いて、生存時間解析の概念について説明します。パラメトリックモデルではなく、Cox比例ハザードモデルに焦点を当てており、予測モデラー向けに設計されていません。	3 中級