Statistics with SAS

SAS

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최근 조회 43,061회

This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression.

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중급 단계

완료하는 데 약 21시간 필요

영어

자막: 프랑스어, 포르투갈어 (브라질), 러시아어, 영어, 스페인어

강사

강사 평가

4.61/5 (28개의 평가)

Jordan Bakerman

Analytical Training Consultant

Education

13,759명의 학습자

4 Courses

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100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 21시간 필요

영어

자막: 프랑스어, 포르투갈어 (브라질), 러시아어, 영어, 스페인어

제공자:

SAS

Through innovative software and services, SAS empowers and inspires customers around the world to transform data into intelligence. SAS is a trusted analytics powerhouse for organizations seeking immediate value from their data. A deep bench of analytics solutions and broad industry knowledge keep our customers coming back and feeling confident. With SAS®, you can discover insights from your data and make sense of it all. Identify what’s working and fix what isn’t. Make more intelligent decisions. And drive relevant change.

강의 계획 - 이 강좌에서 배울 내용

주

주 1

완료하는 데 1시간 필요

Course Overview and Data Setup

In this module you learn about the course and the data you analyze in this course. Then you set up the data you need to do the practices in the course.

완료하는 데 1시간 필요

2개 동영상 (총 13분), 5 개의 읽기 자료

2개의 동영상

Welcome and Meet the Instructor1m

Demo: Exploring Ames Housing Data10m

5개의 읽기 자료

Learner Prerequisites1m

Choosing and Setting Up SAS Software for this Course10m

Follow These Instructions to Set Up Data for This Course (REQUIRED)30m

Completing Demos and Practices10m

Using Forums and Getting Help10m

완료하는 데 3시간 필요

Introduction and Review of Concepts

In this module you learn about the models required to analyze different types of data and the difference between explanatory vs predictive modeling. Then you review fundamental statistical concepts, such as the sampling distribution of a mean, hypothesis testing, p-values, and confidence intervals. After reviewing these concepts, you apply one-sample and two-sample t tests to data to confirm or reject preconceived hypotheses.

완료하는 데 3시간 필요

17개 동영상 (총 41분), 2 개의 읽기 자료, 9 개의 테스트

17개의 동영상

Overview1m

Statistical Modeling: Types of Variables1m

Overview of Models3m

Explanatory versus Predictive Modeling1m

Population Parameters and Sample Statistics1m

Normal (Gaussian) Distribution2m

Standard Error of the Mean51

Confidence Intervals2m

Statistical Hypothesis Test4m

p-Value: Effect Size and Sample Size Influence3m

Scenario48

Performing a t Test4m

Demo: Performing a One-Sample t Test Using PROC TTEST3m

Scenario1m

Assumptions for the Two-Sample t Test2m

Testing for Equal and Unequal Variances2m

Demo: Performing a Two-Sample t Test Using PROC TTEST4m

2개의 읽기 자료

Parameters and Statistics10m

Normal Distribution10m

9개 연습문제

Question 1.015m

Question 1.025m

Question 1.035m

Question 1.045m

Question 1.055m

Practice - Using PROC TTEST to Perform a One-Sample t Test20m

Question 1.065m

Practice - Using PROC TTEST to Compare Groups20m

Introduction and Review of Concepts30m

주

주 2

완료하는 데 4시간 필요

ANOVA and Regression

In this module you learn to use graphical tools that can help determine which predictors are likely or unlikely to be useful. Then you learn to augment these graphical explorations with correlation analyses that describe linear relationships between potential predictors and our response variable. After you determine potential predictors, tools like ANOVA and regression help you assess the quality of the relationship between the response and predictors.

완료하는 데 4시간 필요

29개 동영상 (총 70분), 2 개의 읽기 자료, 14 개의 테스트

29개의 동영상

Overview1m

Scenario58

Identifying Associations in ANOVA with Box Plots1m

Demo: Exploring Associations Using PROC SGPLOT1m

Identifying Associations in Linear Regression with Scatter Plots1m

Demo: Exploring Associations Using PROC SGSCATTER2m

Scenario56

The ANOVA Hypothesis1m

Partitioning Variability in ANOVA2m

Coefficient of Determination1m

F Statistic and Critical Values1m

The ANOVA Model2m

Demo: Performing a One-Way ANOVA Using PROC GLM6m

Scenario49

Multiple Comparison Methods2m

Tukey's and Dunnett's Multiple Comparison Methods1m

Diffograms and Control Plots1m

Demo: Performing a Post Hoc Pairwise Comparison Using PROC GLM6m

Scenario53

Using Correlation to Measure Relationships between Continuous Variables1m

Hypothesis Testing for a Correlation1m

Avoiding Common Errors When Interpreting Correlations5m

Demo: Producing Correlation Statistics and Scatter Plots Using PROC CORR6m

Scenario45

The Simple Linear Regression Model1m

How SAS Performs Simple Linear Regression1m

Comparing the Regression Model to a Baseline Model2m

Hypothesis Testing and Assumptions for Linear Regression1m

Demo: Performing Simple Linear Regression Using PROC REG7m

2개의 읽기 자료

What Does a CLASS Statement Do?10m

Correlation Analysis and Model Building10m

14개 연습문제

Question 2.015m

Question 2.025m

Question 2.035m

Question 2.045m

Practice - Performing a One-Way ANOVA20m

Question 2.055m

Question 2.065m

Practice - Using PROC GLM to Perform Post Hoc Parwise Comparisons20m

Question 2.075m

Question 2.085m

Practice - Describing the Relationship between Continuous Variables20m

Question 2.095m

Practice - Using PROC REG to Fit a Simple Linear Regression Model20m

ANOVA and Regression30m

주

주 3

완료하는 데 2시간 필요

More Complex Linear Models

In this module you expand the one-way ANOVA model to a two-factor analysis of variance and then extend simple linear regression to multiple regression with two predictors. After you understand the concepts of two-way ANOVA and multiple linear regression with two predictors, you'll have the skills to fit and interpret models with many variables.

완료하는 데 2시간 필요

13개 동영상 (총 43분), 1 개의 읽기 자료, 5 개의 테스트

13개의 동영상

Overview1m

Scenario1m

Applying the Two-Way ANOVA Model3m

Demo: Performing a Two-Way ANOVA Using PROC GLM7m

Interactions3m

Demo: Performing a Two-Way ANOVA With an Interaction Using PROC GLM5m

Demo: Performing Post-Processing Analysis Using PROC PLM4m

Scenario44

The Multiple Linear Regression Model2m

Hypothesis Testing for Multiple Regression1m

Multiple Linear Regression versus Simple Linear Regression2m

Adjusted R-Square1m

Demo: Fitting a Multiple Linear Regression Model Using PROC REG7m

1개의 읽기 자료

The STORE Statement10m

5개 연습문제

Question 3.015m

Practice - Performing a Two-Way ANOVA Using PROC GLM20m

Question 3.025m

Practice - Performing Multiple Regression Using PROC REG20m

More Complex Linear Models30m

완료하는 데 2시간 필요

Model Building and Effect Selection

In this module you explore several tools for model selection. These tools help limit the number of candidate models so that you can choose an appropriate model that's based on your expertise and research priorities.

완료하는 데 2시간 필요

11개 동영상 (총 28분), 3 개의 읽기 자료, 4 개의 테스트

11개의 동영상

Overview47

Scenario1m

Approaches to Selecting Models2m

The All-Possible Regressions Approach to Model Building1m

The Stepwise Selection Approach to Model Building3m

Interpreting p-Values and Parameter Estimates2m

Demo: Performing Stepwise Regression Using PROC GLMSELECT7m

Scenario37

Information Criteria2m

Adjusted R-Square and Mallows' Cp56

Demo: Performing Model Selection Using PROC GLMSELECT5m

3개의 읽기 자료

Activity - Optional Stepwise Selection Method Code10m

Information Criteria Penalty Components10m

All-Possible Selection

4개 연습문제

Question 4.015m

Practice - Using PROC GLMSELECT to Perform Stepwise Selection20m

Practice - Using PROC GLMSELECT to Perform Other Model Selection Techniques20m

Model Building and Effect Selection20m

주

주 4

완료하는 데 3시간 필요

Model Post-Fitting for Inference

In this module you learn to verify the assumptions of the model and diagnose problems that you encounter in linear regression. You learn to examine residuals, identify outliers that are numerically distant from the bulk of the data, and identify influential observations that unduly affect the regression model. Finally, you learn to diagnose collinearity to avoid inflated standard errors and parameter instability in the model.

완료하는 데 3시간 필요

18개 동영상 (총 46분)

18개의 동영상

Overview1m

Scenario40

Assumptions for Regression2m

Verifying Assumptions Using Residual Plots3m

Demo: Examining Residual Plots Using PROC REG5m

Scenario47

Identifying Influential Observations1m

Checking for Outliers with STUDENT Residuals1m

Checking for Influential Observations2m

Detecting Influential Observations with DFBETAS1m

Demo: Looking for Influential Observations Using PROC GLMSELECT and PROC REG5m

Demo: Examining the Influential Observations Using PROC PRINT6m

Handling Influential Observations1m

Scenario38

Exploring Collinearity1m

Visualizing Collinearity2m

Demo: Calculating Collinearity Diagnostics Using PROC REG5m

Using an Effective Modeling Cycle1m

7개 연습문제

Practice: Using PROC REG to Examine Residuals20m

Question 5.015m

Practice: Using PROC REG to Generate Potential Outliers20m

Question 5.025m

Question 5.035m

Practice: Using PROC REG to Assess Collinearity20m

Model Post-Fitting for Inference30m

완료하는 데 2시간 필요

Model Building for Scoring and Prediction

In this module you learn how to transition from inferential statistics to predictive modeling. Instead of using p-values, you learn about assessing models using honest assessment. After you choose the best performing model, you learn about ways to deploy the model to predict new data.

완료하는 데 2시간 필요

11개 동영상 (총 27분), 1 개의 읽기 자료, 4 개의 테스트

11개의 동영상

Overview1m

Scenario27

Predictive Modeling Terminology1m

Model Complexity51

Building a Predictive Model2m

Model Assessment and Selection1m

Demo: Building a Predictive Model Using PROC GLMSELECT10m

Scenario29

Preparing for Scoring1m

Methods of Scoring1m

Demo: Scoring Data Using PROC PLM4m

1개의 읽기 자료

Partitioning a Data Set Using PROC GLMSELECT10m

4개 연습문제

Question 6.015m

Practice: Building a Predictive Model Using PROC GLMSELECT20m

Practice: Scoring Using the SCORE Statement in PROC GLMSELECT20m

Model Building for Scoring and Prediction30m

주

주 5

완료하는 데 4시간 필요

Categorical Data Analysis

In this module you look for associations between predictors and a binary response using hypothesis tests. Then you build a logistic regression model and learn about how to characterize the relationship between the response and predictors. Finally, you learn how to use logistic regression to build a model, or classifier, to predict unknown cases.

완료하는 데 4시간 필요

25개 동영상 (총 73분)

25개의 동영상

Overview1m

Scenario59

Associations between Categorical Variables2m

Demo: Examining the Distribution of Categorical Variables Using PROC FREQ and PROC UNIVARIATE6m

Scenario43

The Pearson Chi-Square Test3m

Odds Ratios4m

Demo: Performing a Pearson Chi-Square Test of Association Using PROC FREQ5m

Scenario22

The Mantel-Haenszel Chi-Square Test1m

The Spearman Correlation Statistic40

Demo: Detecting Ordinal Associations Using PROC FREQ2m

Scenario49

Modeling a Binary Response3m

Demo: Fitting a Binary Logistic Regression Model Using PROC LOGISTIC6m

Interpreting the Odds Ratio3m

Comparing Pairs to Assess the Fit of a Logistic Regression Model4m

Scenario50

Specifying a Parameterization Method4m

Demo: Fitting a Multiple Logistic Regression Model with Categorical Predictors Using PROC LOGISTIC7m

Scenario35

Interactions between Variables2m

Demo: Fitting a Multiple Logistic Regression Model with Interactions Using PROC LOGISTIC3m

Demo: Fitting a Multiple Logistic Regression Model with All Odds Ratios Using PROC LOGISTIC3m

Demo: Generating Predictions Using PROC PLM2m

18개 연습문제

Question 7.015m

Question 7.025m

Practice: Using PROC FREQ to Examine Distributions20m

Question 7.035m

Question 7.045m

Question 7.055m

Question 7.065m

Practice: Using PROC FREQ to Perform Tests and Measures of Association20m

Question 7.075m

Question 7.085m

Practice: Using PROC LOGISTIC to Perform a Binary Logistic Regression Analysis20m

Question 7.095m

Question 7.105m

Practice: Using PROC LOGISTIC to Perform a Multiple Logistic Regression Analysis with Categorical Variables20m

Question 7.115m

Question 7.125m

Practice: Using PROC LOGISTIC to Perform Backward Elimination and PROC PLM to Generate Predictions20m

Categorical Data Analysis30m

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Statistics with SAS