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.
このコースについて
25,088 最近の表示
このCourseを受講している学習者は
- Data Analysts
- Scientists
- Medical Doctors
- Economists
- Data Scientists
シラバス - 本コースの学習内容
週
11時間で修了
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.
2件のビデオ （合計13分）, 5 readings
5件の学習用教材
Learner Prerequisites10 分
Choosing and Setting Up SAS Software for this Course10 分
Follow These Instructions to Set Up Data for This Course (REQUIRED)30 分
Completing Demos and Practices10 分
Using Forums and Getting Help10 分
3時間で修了
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.
17件のビデオ （合計41分）, 2 readings, 9 quizzes
17件のビデオ
Overview1 分
Statistical Modeling: Types of Variables1 分
Overview of Models3 分
Explanatory versus Predictive Modeling1 分
Population Parameters and Sample Statistics1 分
Normal (Gaussian) Distribution2 分
Standard Error of the Mean51
Confidence Intervals2 分
Statistical Hypothesis Test4 分
p-Value: Effect Size and Sample Size Influence3 分
Scenario48
Performing a t Test4 分
Demo: Performing a One-Sample t Test Using PROC TTEST3 分
Scenario1 分
Assumptions for the Two-Sample t Test2 分
Testing for Equal and Unequal Variances2 分
Demo: Performing a Two-Sample t Test Using PROC TTEST4 分
2件の学習用教材
Parameters and Statistics10 分
Normal Distribution10 分
9の練習問題
Question 1.015 分
Question 1.025 分
Question 1.035 分
Question 1.045 分
Question 1.055 分
Practice - Using PROC TTEST to Perform a One-Sample t Test20 分
Question 1.065 分
Practice - Using PROC TTEST to Compare Groups20 分
Introduction and Review of Concepts30 分
週
24時間で修了
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.
29件のビデオ （合計70分）, 2 readings, 14 quizzes
29件のビデオ
Overview1 分
Scenario58
Identifying Associations in ANOVA with Box Plots1 分
Demo: Exploring Associations Using PROC SGPLOT1 分
Identifying Associations in Linear Regression with Scatter Plots1 分
Demo: Exploring Associations Using PROC SGSCATTER2 分
Scenario56
The ANOVA Hypothesis1 分
Partitioning Variability in ANOVA2 分
Coefficient of Determination1 分
F Statistic and Critical Values1 分
The ANOVA Model2 分
Demo: Performing a One-Way ANOVA Using PROC GLM6 分
Scenario49
Multiple Comparison Methods2 分
Tukey's and Dunnett's Multiple Comparison Methods1 分
Diffograms and Control Plots1 分
Demo: Performing a Post Hoc Pairwise Comparison Using PROC GLM6 分
Scenario53
Using Correlation to Measure Relationships between Continuous Variables1 分
Hypothesis Testing for a Correlation1 分
Avoiding Common Errors When Interpreting Correlations5 分
Demo: Producing Correlation Statistics and Scatter Plots Using PROC CORR6 分
Scenario45
The Simple Linear Regression Model1 分
How SAS Performs Simple Linear Regression1 分
Comparing the Regression Model to a Baseline Model2 分
Hypothesis Testing and Assumptions for Linear Regression1 分
Demo: Performing Simple Linear Regression Using PROC REG7 分
2件の学習用教材
What Does a CLASS Statement Do?10 分
Correlation Analysis and Model Building10 分
14の練習問題
Question 2.015 分
Question 2.025 分
Question 2.035 分
Question 2.045 分
Practice - Performing a One-Way ANOVA20 分
Question 2.055 分
Question 2.065 分
Practice - Using PROC GLM to Perform Post Hoc Parwise Comparisons20 分
Question 2.075 分
Question 2.085 分
Practice - Describing the Relationship between Continuous Variables20 分
Question 2.095 分
Practice - Using PROC REG to Fit a Simple Linear Regression Model20 分
ANOVA and Regression30 分
週
32時間で修了
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.
13件のビデオ （合計43分）, 1 reading, 5 quizzes
13件のビデオ
Overview1 分
Scenario1 分
Applying the Two-Way ANOVA Model3 分
Demo: Performing a Two-Way ANOVA Using PROC GLM7 分
Interactions3 分
Demo: Performing a Two-Way ANOVA With an Interaction Using PROC GLM5 分
Demo: Performing Post-Processing Analysis Using PROC PLM4 分
Scenario44
The Multiple Linear Regression Model2 分
Hypothesis Testing for Multiple Regression1 分
Multiple Linear Regression versus Simple Linear Regression2 分
Adjusted R-Square1 分
Demo: Fitting a Multiple Linear Regression Model Using PROC REG7 分
1件の学習用教材
The STORE Statement10 分
5の練習問題
Question 3.015 分
Practice - Performing a Two-Way ANOVA Using PROC GLM20 分
Question 3.025 分
Practice - Performing Multiple Regression Using PROC REG20 分
More Complex Linear Models30 分
2時間で修了
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.
11件のビデオ （合計28分）, 3 readings, 4 quizzes
11件のビデオ
Overview47
Scenario1 分
Approaches to Selecting Models2 分
The All-Possible Regressions Approach to Model Building1 分
The Stepwise Selection Approach to Model Building3 分
Interpreting p-Values and Parameter Estimates2 分
Demo: Performing Stepwise Regression Using PROC GLMSELECT7 分
Scenario37
Information Criteria2 分
Adjusted R-Square and Mallows' Cp56
Demo: Performing Model Selection Using PROC GLMSELECT5 分
3件の学習用教材
Activity - Optional Stepwise Selection Method Code10 分
Information Criteria Penalty Components10 分
All-Possible Selection
4の練習問題
Question 4.015 分
Practice - Using PROC GLMSELECT to Perform Stepwise Selection20 分
Practice - Using PROC GLMSELECT to Perform Other Model Selection Techniques20 分
Model Building and Effect Selection20 分
週
43時間で修了
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.
18件のビデオ （合計46分）, 7 quizzes
18件のビデオ
Overview1 分
Scenario40
Assumptions for Regression2 分
Verifying Assumptions Using Residual Plots3 分
Demo: Examining Residual Plots Using PROC REG5 分
Scenario47
Identifying Influential Observations1 分
Checking for Outliers with STUDENT Residuals1 分
Checking for Influential Observations2 分
Detecting Influential Observations with DFBETAS1 分
Demo: Looking for Influential Observations Using PROC GLMSELECT and PROC REG5 分
Demo: Examining the Influential Observations Using PROC PRINT6 分
Handling Influential Observations1 分
Scenario38
Exploring Collinearity1 分
Visualizing Collinearity2 分
Demo: Calculating Collinearity Diagnostics Using PROC REG5 分
Using an Effective Modeling Cycle1 分
7の練習問題
Practice: Using PROC REG to Examine Residuals20 分
Question 5.015 分
Practice: Using PROC REG to Generate Potential Outliers20 分
Question 5.025 分
Question 5.035 分
Practice: Using PROC REG to Assess Collinearity20 分
Model Post-Fitting for Inference30 分
2時間で修了
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.
11件のビデオ （合計27分）, 1 reading, 4 quizzes
11件のビデオ
Overview1 分
Scenario27
Predictive Modeling Terminology1 分
Model Complexity51
Building a Predictive Model2 分
Model Assessment and Selection1 分
Demo: Building a Predictive Model Using PROC GLMSELECT10 分
Scenario29
Preparing for Scoring1 分
Methods of Scoring1 分
Demo: Scoring Data Using PROC PLM4 分
1件の学習用教材
Partitioning a Data Set Using PROC GLMSELECT10 分
4の練習問題
Question 6.015 分
Practice: Building a Predictive Model Using PROC GLMSELECT20 分
Practice: Scoring Using the SCORE Statement in PROC GLMSELECT20 分
Model Building for Scoring and Prediction30 分
4.9
8件のレビューStatistics with SAS からの人気レビュー
The best course for statistics I've ever seen. I've learned statistics here not in university. Big like to all those people provide this valuable course for us. Thanks a million.
This course is really fantastic!\n\nI love SAS, and I love analysis\n\nHope this will help me in my Ph. D studying career
講師
Jordan Bakerman
Analytical Training ConsultantEducation
SASについて
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.
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