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Jordan Bakerman

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Statistics with SAS

SAS

课程信息

37,391 次近期查看

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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100% 在线

立即开始，按照自己的计划学习。

中级

完成时间大约为20 小时

英语（English）

字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 德语（German）, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）

可灵活调整截止日期

根据您的日程表重置截止日期。

可分享的证书

完成后获得证书

100% 在线

立即开始，按照自己的计划学习。

中级

完成时间大约为20 小时

英语（English）

授课教师

授课教师评分

4.75/5 （84 个评分）

Jordan Bakerman

Analytical Training Consultant

Education

32,878 个学生

4 门课程

提供方

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.

授课大纲 - 您将从这门课程中学到什么

内容评分94%（1,398 个评分）

周

周 1

完成时间为 1 小时

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.

完成时间为 1 小时

2 个视频（总计 13 分钟）, 4 个阅读材料

完成时间为 3 小时

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.

完成时间为 3 小时

17 个视频（总计 41 分钟）, 2 个阅读材料, 9 个测验

17 个视频

Overview1分钟

Statistical Modeling: Types of Variables1分钟

Overview of Models3分钟

Explanatory versus Predictive Modeling1分钟

Population Parameters and Sample Statistics1分钟

Normal (Gaussian) Distribution2分钟

Standard Error of the Mean51

Confidence Intervals2分钟

Statistical Hypothesis Test4分钟

p-Value: Effect Size and Sample Size Influence3分钟

Scenario48

Performing a t Test4分钟

Demo: Performing a One-Sample t Test Using PROC TTEST3分钟

Scenario1分钟

Assumptions for the Two-Sample t Test2分钟

Testing for Equal and Unequal Variances2分钟

Demo: Performing a Two-Sample t Test Using PROC TTEST4分钟

2 个阅读材料

Parameters and Statistics10分钟

Normal Distribution10分钟

9 个练习

Question 1.015分钟

Question 1.025分钟

Question 1.035分钟

Question 1.045分钟

Question 1.055分钟

Practice - Using PROC TTEST to Perform a One-Sample t Test20分钟

Question 1.065分钟

Practice - Using PROC TTEST to Compare Groups20分钟

Introduction and Review of Concepts30分钟

周

周 2

完成时间为 4 小时

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.

完成时间为 4 小时

29 个视频（总计 70 分钟）, 2 个阅读材料, 14 个测验

29 个视频

Overview1分钟

Scenario58

Identifying Associations in ANOVA with Box Plots1分钟

Demo: Exploring Associations Using PROC SGPLOT1分钟

Identifying Associations in Linear Regression with Scatter Plots1分钟

Demo: Exploring Associations Using PROC SGSCATTER2分钟

Scenario56

The ANOVA Hypothesis1分钟

Partitioning Variability in ANOVA2分钟

Coefficient of Determination1分钟

F Statistic and Critical Values1分钟

The ANOVA Model2分钟

Demo: Performing a One-Way ANOVA Using PROC GLM6分钟

Scenario49

Multiple Comparison Methods2分钟

Tukey's and Dunnett's Multiple Comparison Methods1分钟

Diffograms and Control Plots1分钟

Demo: Performing a Post Hoc Pairwise Comparison Using PROC GLM6分钟

Scenario53

Using Correlation to Measure Relationships between Continuous Variables1分钟

Hypothesis Testing for a Correlation1分钟

Avoiding Common Errors When Interpreting Correlations5分钟

Demo: Producing Correlation Statistics and Scatter Plots Using PROC CORR6分钟

Scenario45

The Simple Linear Regression Model1分钟

How SAS Performs Simple Linear Regression1分钟

Comparing the Regression Model to a Baseline Model2分钟

Hypothesis Testing and Assumptions for Linear Regression1分钟

Demo: Performing Simple Linear Regression Using PROC REG7分钟

2 个阅读材料

What Does a CLASS Statement Do?10分钟

Correlation Analysis and Model Building10分钟

14 个练习

Question 2.015分钟

Question 2.025分钟

Question 2.035分钟

Question 2.045分钟

Practice - Performing a One-Way ANOVA20分钟

Question 2.055分钟

Question 2.065分钟

Practice - Using PROC GLM to Perform Post Hoc Parwise Comparisons20分钟

Question 2.075分钟

Question 2.085分钟

Practice - Describing the Relationship between Continuous Variables20分钟

Question 2.095分钟

Practice - Using PROC REG to Fit a Simple Linear Regression Model20分钟

ANOVA and Regression30分钟

周

周 3

完成时间为 2 小时

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.

完成时间为 2 小时

13 个视频（总计 43 分钟）, 1 个阅读材料, 5 个测验

13 个视频

Overview1分钟

Scenario1分钟

Applying the Two-Way ANOVA Model3分钟

Demo: Performing a Two-Way ANOVA Using PROC GLM7分钟

Interactions3分钟

Demo: Performing a Two-Way ANOVA With an Interaction Using PROC GLM5分钟

Demo: Performing Post-Processing Analysis Using PROC PLM4分钟

Scenario44

The Multiple Linear Regression Model2分钟

Hypothesis Testing for Multiple Regression1分钟

Multiple Linear Regression versus Simple Linear Regression2分钟

Adjusted R-Square1分钟

Demo: Fitting a Multiple Linear Regression Model Using PROC REG7分钟

1 个阅读材料

The STORE Statement10分钟

5 个练习

Question 3.015分钟

Practice - Performing a Two-Way ANOVA Using PROC GLM20分钟

Question 3.025分钟

Practice - Performing Multiple Regression Using PROC REG20分钟

More Complex Linear Models30分钟

完成时间为 2 小时

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.

完成时间为 2 小时

11 个视频（总计 28 分钟）, 3 个阅读材料, 4 个测验

11 个视频

Overview47

Scenario1分钟

Approaches to Selecting Models2分钟

The All-Possible Regressions Approach to Model Building1分钟

The Stepwise Selection Approach to Model Building3分钟

Interpreting p-Values and Parameter Estimates2分钟

Demo: Performing Stepwise Regression Using PROC GLMSELECT7分钟

Scenario37

Information Criteria2分钟

Adjusted R-Square and Mallows' Cp56

Demo: Performing Model Selection Using PROC GLMSELECT5分钟

3 个阅读材料

Activity - Optional Stepwise Selection Method Code10分钟

Information Criteria Penalty Components10分钟

All-Possible Selection

4 个练习

Question 4.015分钟

Practice - Using PROC GLMSELECT to Perform Stepwise Selection20分钟

Practice - Using PROC GLMSELECT to Perform Other Model Selection Techniques20分钟

Model Building and Effect Selection20分钟

周

周 4

完成时间为 3 小时

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.

完成时间为 3 小时

18 个视频（总计 46 分钟）

18 个视频

Overview1分钟

Scenario40

Assumptions for Regression2分钟

Verifying Assumptions Using Residual Plots3分钟

Demo: Examining Residual Plots Using PROC REG5分钟

Scenario47

Identifying Influential Observations1分钟

Checking for Outliers with STUDENT Residuals1分钟

Checking for Influential Observations2分钟

Detecting Influential Observations with DFBETAS1分钟

Demo: Looking for Influential Observations Using PROC GLMSELECT and PROC REG5分钟

Demo: Examining the Influential Observations Using PROC PRINT6分钟

Handling Influential Observations1分钟

Scenario38

Exploring Collinearity1分钟

Visualizing Collinearity2分钟

Demo: Calculating Collinearity Diagnostics Using PROC REG5分钟

Using an Effective Modeling Cycle1分钟

7 个练习

Practice: Using PROC REG to Examine Residuals20分钟

Question 5.015分钟

Practice: Using PROC REG to Generate Potential Outliers20分钟

Question 5.025分钟

Question 5.035分钟

Practice: Using PROC REG to Assess Collinearity20分钟

Model Post-Fitting for Inference30分钟

完成时间为 2 小时

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.

完成时间为 2 小时

11 个视频（总计 27 分钟）, 1 个阅读材料, 4 个测验

11 个视频

Overview1分钟

Scenario27

Predictive Modeling Terminology1分钟

Model Complexity51

Building a Predictive Model2分钟

Model Assessment and Selection1分钟

Demo: Building a Predictive Model Using PROC GLMSELECT10分钟

Scenario29

Preparing for Scoring1分钟

Methods of Scoring1分钟

Demo: Scoring Data Using PROC PLM4分钟

1 个阅读材料

Partitioning a Data Set Using PROC GLMSELECT10分钟

4 个练习

Question 6.015分钟

Practice: Building a Predictive Model Using PROC GLMSELECT20分钟

Practice: Scoring Using the SCORE Statement in PROC GLMSELECT20分钟

Model Building for Scoring and Prediction30分钟

周

周 5

完成时间为 4 小时

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.

完成时间为 4 小时

25 个视频（总计 73 分钟）

25 个视频

Overview1分钟

Scenario59

Associations between Categorical Variables2分钟

Demo: Examining the Distribution of Categorical Variables Using PROC FREQ and PROC UNIVARIATE6分钟

Scenario43

The Pearson Chi-Square Test3分钟

Odds Ratios4分钟

Demo: Performing a Pearson Chi-Square Test of Association Using PROC FREQ5分钟

Scenario22

The Mantel-Haenszel Chi-Square Test1分钟

The Spearman Correlation Statistic40

Demo: Detecting Ordinal Associations Using PROC FREQ2分钟

Scenario49

Modeling a Binary Response3分钟

Demo: Fitting a Binary Logistic Regression Model Using PROC LOGISTIC6分钟

Interpreting the Odds Ratio3分钟

Comparing Pairs to Assess the Fit of a Logistic Regression Model4分钟

Scenario50

Specifying a Parameterization Method4分钟

Demo: Fitting a Multiple Logistic Regression Model with Categorical Predictors Using PROC LOGISTIC7分钟

Scenario35

Interactions between Variables2分钟

Demo: Fitting a Multiple Logistic Regression Model with Interactions Using PROC LOGISTIC3分钟

Demo: Fitting a Multiple Logistic Regression Model with All Odds Ratios Using PROC LOGISTIC3分钟

Demo: Generating Predictions Using PROC PLM2分钟

18 个练习

Question 7.015分钟

Question 7.025分钟

Practice: Using PROC FREQ to Examine Distributions20分钟

Question 7.035分钟

Question 7.045分钟

Question 7.055分钟

Question 7.065分钟

Practice: Using PROC FREQ to Perform Tests and Measures of Association20分钟

Question 7.075分钟

Question 7.085分钟

Practice: Using PROC LOGISTIC to Perform a Binary Logistic Regression Analysis20分钟

Question 7.095分钟

Question 7.105分钟

Practice: Using PROC LOGISTIC to Perform a Multiple Logistic Regression Analysis with Categorical Variables20分钟

Question 7.115分钟

Question 7.125分钟

Practice: Using PROC LOGISTIC to Perform Backward Elimination and PROC PLM to Generate Predictions20分钟

Categorical Data Analysis30分钟

审阅

4.8

70 条评论

5 stars
83.13%
4 stars
12.85%
3 stars
2%
2 stars
0.80%
1 star
1.20%

来自STATISTICS WITH SAS的热门评论

由 HH 提供Jul 12, 2021

He knows statistical concepts very well and is able to explain in a clear and concise manner. Better than other top school classes filled up with jargons and group homework.

由 AA 提供Feb 22, 2021

I love the examples and the way each topic where thought.

由 ZY 提供Jun 30, 2019

This course is really fantastic! I love SAS, and I love analysis\n\nHope this will help me in my Ph. D studying career

由 RP 提供Mar 2, 2021

Excellent, it's very valuable statistics course !! Thanks SAS

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Statistics with SAS

Statistics with SAS

课程信息

授课教师

Jordan Bakerman

提供方

SAS

授课大纲 - 您将从这门课程中学到什么

周 1

Course Overview and Data Setup

Introduction and Review of Concepts

周 2

ANOVA and Regression

周 3

More Complex Linear Models

Model Building and Effect Selection

周 4

Model Post-Fitting for Inference

Model Building for Scoring and Prediction

周 5

Categorical Data Analysis

审阅

来自STATISTICS WITH SAS的热门评论

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Statistics with SAS

Statistics with SAS

课程信息

授课教师

Jordan Bakerman

提供方

SAS

授课大纲 - 您将从这门课程中学到什么

周 1

Course Overview and Data Setup

Introduction and Review of Concepts

周 2

ANOVA and Regression

周 3

More Complex Linear Models

Model Building and Effect Selection

周 4

Model Post-Fitting for Inference

Model Building for Scoring and Prediction

周 5

Categorical Data Analysis

审阅

来自STATISTICS WITH SAS的热门评论

常见问题

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开拓职业生涯或促进职业发展

浏览热门主题

热门课程和文章

在线获得学位或证书

Coursera

社区

更多