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.
proposé par
SAS
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
11 heure pour terminer
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 vidéos (Total 13 min), 5 lectures
5 lectures
Learner Prerequisites10 min
Choosing and Setting Up SAS Software for this Course10 min
Follow These Instructions to Set Up Data for This Course (REQUIRED)30 min
Completing Demos and Practices10 min
Using Forums and Getting Help10 min
3 heures pour terminer
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 vidéos (Total 41 min), 2 lectures, 9 quiz
17 vidéos
Overview1 min
Statistical Modeling: Types of Variables1 min
Overview of Models3 min
Explanatory versus Predictive Modeling1 min
Population Parameters and Sample Statistics1 min
Normal (Gaussian) Distribution2 min
Standard Error of the Mean51s
Confidence Intervals2 min
Statistical Hypothesis Test4 min
p-Value: Effect Size and Sample Size Influence3 min
Scenario48s
Performing a t Test4 min
Demo: Performing a One-Sample t Test Using PROC TTEST3 min
Scenario1 min
Assumptions for the Two-Sample t Test2 min
Testing for Equal and Unequal Variances2 min
Demo: Performing a Two-Sample t Test Using PROC TTEST4 min
2 lectures
Parameters and Statistics10 min
Normal Distribution10 min
9 exercices pour s'entraîner
Question 1.015 min
Question 1.025 min
Question 1.035 min
Question 1.045 min
Question 1.055 min
Practice - Using PROC TTEST to Perform a One-Sample t Test20 min
Question 1.065 min
Practice - Using PROC TTEST to Compare Groups20 min
Introduction and Review of Concepts30 min
Semaine
24 heures pour terminer
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 vidéos (Total 70 min), 2 lectures, 14 quiz
29 vidéos
Overview1 min
Scenario58s
Identifying Associations in ANOVA with Box Plots1 min
Demo: Exploring Associations Using PROC SGPLOT1 min
Identifying Associations in Linear Regression with Scatter Plots1 min
Demo: Exploring Associations Using PROC SGSCATTER2 min
Scenario56s
The ANOVA Hypothesis1 min
Partitioning Variability in ANOVA2 min
Coefficient of Determination1 min
F Statistic and Critical Values1 min
The ANOVA Model2 min
Demo: Performing a One-Way ANOVA Using PROC GLM6 min
Scenario49s
Multiple Comparison Methods2 min
Tukey's and Dunnett's Multiple Comparison Methods1 min
Diffograms and Control Plots1 min
Demo: Performing a Post Hoc Pairwise Comparison Using PROC GLM6 min
Scenario53s
Using Correlation to Measure Relationships between Continuous Variables1 min
Hypothesis Testing for a Correlation1 min
Avoiding Common Errors When Interpreting Correlations5 min
Demo: Producing Correlation Statistics and Scatter Plots Using PROC CORR6 min
Scenario45s
The Simple Linear Regression Model1 min
How SAS Performs Simple Linear Regression1 min
Comparing the Regression Model to a Baseline Model2 min
Hypothesis Testing and Assumptions for Linear Regression1 min
Demo: Performing Simple Linear Regression Using PROC REG7 min
2 lectures
What Does a CLASS Statement Do?10 min
Correlation Analysis and Model Building10 min
14 exercices pour s'entraîner
Question 2.015 min
Question 2.025 min
Question 2.035 min
Question 2.045 min
Practice - Performing a One-Way ANOVA20 min
Question 2.055 min
Question 2.065 min
Practice - Using PROC GLM to Perform Post Hoc Parwise Comparisons20 min
Question 2.075 min
Question 2.085 min
Practice - Describing the Relationship between Continuous Variables20 min
Question 2.095 min
Practice - Using PROC REG to Fit a Simple Linear Regression Model20 min
ANOVA and Regression30 min
Semaine
32 heures pour terminer
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 vidéos (Total 43 min), 1 lecture, 5 quiz
13 vidéos
Overview1 min
Scenario1 min
Applying the Two-Way ANOVA Model3 min
Demo: Performing a Two-Way ANOVA Using PROC GLM7 min
Interactions3 min
Demo: Performing a Two-Way ANOVA With an Interaction Using PROC GLM5 min
Demo: Performing Post-Processing Analysis Using PROC PLM4 min
Scenario44s
The Multiple Linear Regression Model2 min
Hypothesis Testing for Multiple Regression1 min
Multiple Linear Regression versus Simple Linear Regression2 min
Adjusted R-Square1 min
Demo: Fitting a Multiple Linear Regression Model Using PROC REG7 min
1 lecture
The STORE Statement10 min
5 exercices pour s'entraîner
Question 3.015 min
Practice - Performing a Two-Way ANOVA Using PROC GLM20 min
Question 3.025 min
Practice - Performing Multiple Regression Using PROC REG20 min
More Complex Linear Models30 min
2 heures pour terminer
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 vidéos (Total 28 min), 3 lectures, 4 quiz
11 vidéos
Overview47s
Scenario1 min
Approaches to Selecting Models2 min
The All-Possible Regressions Approach to Model Building1 min
The Stepwise Selection Approach to Model Building3 min
Interpreting p-Values and Parameter Estimates2 min
Demo: Performing Stepwise Regression Using PROC GLMSELECT7 min
Scenario37s
Information Criteria2 min
Adjusted R-Square and Mallows' Cp56s
Demo: Performing Model Selection Using PROC GLMSELECT5 min
3 lectures
Activity - Optional Stepwise Selection Method Code10 min
Information Criteria Penalty Components10 min
All-Possible Selection
4 exercices pour s'entraîner
Question 4.015 min
Practice - Using PROC GLMSELECT to Perform Stepwise Selection20 min
Practice - Using PROC GLMSELECT to Perform Other Model Selection Techniques20 min
Model Building and Effect Selection20 min
Semaine
43 heures pour terminer
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 vidéos (Total 46 min), 7 quiz
18 vidéos
Overview1 min
Scenario40s
Assumptions for Regression2 min
Verifying Assumptions Using Residual Plots3 min
Demo: Examining Residual Plots Using PROC REG5 min
Scenario47s
Identifying Influential Observations1 min
Checking for Outliers with STUDENT Residuals1 min
Checking for Influential Observations2 min
Detecting Influential Observations with DFBETAS1 min
Demo: Looking for Influential Observations Using PROC GLMSELECT and PROC REG5 min
Demo: Examining the Influential Observations Using PROC PRINT6 min
Handling Influential Observations1 min
Scenario38s
Exploring Collinearity1 min
Visualizing Collinearity2 min
Demo: Calculating Collinearity Diagnostics Using PROC REG5 min
Using an Effective Modeling Cycle1 min
7 exercices pour s'entraîner
Practice: Using PROC REG to Examine Residuals20 min
Question 5.015 min
Practice: Using PROC REG to Generate Potential Outliers20 min
Question 5.025 min
Question 5.035 min
Practice: Using PROC REG to Assess Collinearity20 min
Model Post-Fitting for Inference30 min
2 heures pour terminer
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 vidéos (Total 27 min), 1 lecture, 4 quiz
11 vidéos
Overview1 min
Scenario27s
Predictive Modeling Terminology1 min
Model Complexity51s
Building a Predictive Model2 min
Model Assessment and Selection1 min
Demo: Building a Predictive Model Using PROC GLMSELECT10 min
Scenario29s
Preparing for Scoring1 min
Methods of Scoring1 min
Demo: Scoring Data Using PROC PLM4 min
1 lecture
Partitioning a Data Set Using PROC GLMSELECT10 min
4 exercices pour s'entraîner
Question 6.015 min
Practice: Building a Predictive Model Using PROC GLMSELECT20 min
Practice: Scoring Using the SCORE Statement in PROC GLMSELECT20 min
Model Building for Scoring and Prediction30 min
4.9
5 avisPrincipaux examens pour Statistics with SAS
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
Enseignant
Jordan Bakerman
Analytical Training ConsultantEducation
À propos de 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.
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