Statistics with SAS

SAS Institute via Coursera

Intermediate

Online

3 Weeks

Free

particular

details

                                    Medium of instructions
                                    English

                                    Mode of learning
                                    Self study

                                    Mode of Delivery
                                    Video and Text Based

                                    Learning efforts
                                    6 Hours Per Week
                                

Week 1: Course Overview and Data Setup

Videos

Welcome and Meet the Instructor
Demo: Exploring Ames Housing Data

Readings

Learner Prerequisites
Access SAS Software and Set Up Practice Files (REQUIRED)
Completing Demos and Practices
Using Forums and Getting Help

Week 1: Introduction and Review of Concepts

Videos

Overview
Statistical Modeling: Types of Variables
Overview of Models
Explanatory versus Predictive Modeling
Population Parameters and Sample Statistics
Normal (Gaussian) Distribution
Standard Error of the Mean
Confidence Intervals
Statistical Hypothesis Test
p-Value: Effect Size and Sample Size Influence
Scenario
Performing a t Test
Demo: Performing a One-Sample t Test Using PROC TTEST
Scenario
Assumptions for the Two-Sample t Test
Testing for Equal and Unequal Variances
Demo: Performing a Two-Sample t Test Using PROC TTEST

Readings

Parameters and Statistics
Normal Distribution

Practice Exercise

Question 1.01
Question 1.02
Question 1.03
Question 1.04
Question 1.05
Practice - Using PROC TTEST to Perform a One-Sample t Test
Question 1.06
Practice - Using PROC TTEST to Compare Groups
Introduction and Review of Concepts

Week 2: ANOVA and Regression

Videos

Overview
Scenario
Identifying Associations in ANOVA with Box Plots
Demo: Exploring Associations Using PROC SGPLOT
Identifying Associations in Linear Regression with Scatter Plots
Demo: Exploring Associations Using PROC SGSCATTER
Scenario
The ANOVA Hypothesis
Partitioning Variability in ANOVA
Coefficient of Determination
F Statistic and Critical Values
The ANOVA Model
Demo: Performing a One-Way ANOVA Using PROC GLM
Scenario
Multiple Comparison Methods
Tukey's and Dunnett's Multiple Comparison Methods
Diffograms and Control Plots
Demo: Performing a Post Hoc Pairwise Comparison Using PROC GLM
Scenario
Using Correlation to Measure Relationships between Continuous Variables
Hypothesis Testing for a Correlation
Avoiding Common Errors When Interpreting Correlations
Demo: Producing Correlation Statistics and Scatter Plots Using PROC CORR
Scenario
The Simple Linear Regression Model
How SAS Performs Simple Linear Regression
Comparing the Regression Model to a Baseline Model
Hypothesis Testing and Assumptions for Linear Regression
Demo: Performing Simple Linear Regression Using PROC REG

Readings

What Does a CLASS Statement Do?
Correlation Analysis and Model Building

Practice Exercise

Question 2.01
Question 2.02
Question 2.03
Question 2.04
Practice - Performing a One-Way ANOVA
Question 2.05
Question 2.06
Practice - Using PROC GLM to Perform Post Hoc Parwise Comparisons
Question 2.07
Question 2.08
Practice - Describing the Relationship between Continuous Variables
Question 2.09
Practice - Using PROC REG to Fit a Simple Linear Regression Model
ANOVA and Regression

Week 3: More Complex Linear Models

Videos

Overview
Scenario
Applying the Two-Way ANOVA Model
Demo: Performing a Two-Way ANOVA Using PROC GLM
Interactions
Demo: Performing a Two-Way ANOVA With an Interaction Using PROC GLM
Demo: Performing Post-Processing Analysis Using PROC PLM
Scenario
The Multiple Linear Regression Model
Hypothesis Testing for Multiple Regression
Multiple Linear Regression versus Simple Linear Regression
Adjusted R-Square
Demo: Fitting a Multiple Linear Regression Model Using PROC REG

Readings

The STORE Statement

Practice Exercise

Question 3.01
Practice - Performing a Two-Way ANOVA Using PROC GLM
Question 3.02
Practice - Performing Multiple Regression Using PROC REG
More Complex Linear Models

Week 3: Model Building and Effect Selection

Videos

Overview
Scenario
Approaches to Selecting Models
The All-Possible Regressions Approach to Model Building
The Stepwise Selection Approach to Model Building
Interpreting p-Values and Parameter Estimates
Demo: Performing Stepwise Regression Using PROC GLMSELECT
Scenario
Information Criteria
Adjusted R-Square and Mallows' Cp
Demo: Performing Model Selection Using PROC GLMSELECT

Readings

Activity - Optional Stepwise Selection Method Code
Information Criteria Penalty Components
All-Possible Selection

Practice Exercise

Question 4.01
Practice - Using PROC GLMSELECT to Perform Stepwise Selection
Practice - Using PROC GLMSELECT to Perform Other Model Selection Techniques
Model Building and Effect Selection

Week 4: Model Post-Fitting for Inference

Videos

Overview
Scenario
Assumptions for Regression
Verifying Assumptions Using Residual Plots
Demo: Examining Residual Plots Using PROC REG
Scenario
Identifying Influential Observations
Checking for Outliers with STUDENT Residuals
Checking for Influential Observations
Detecting Influential Observations with DFBETAS
Demo: Looking for Influential Observations Using PROC GLMSELECT and PROC REG
Demo: Examining the Influential Observations Using PROC PRINT
Handling Influential Observations
Scenario
Exploring Collinearity
Visualizing Collinearity
Demo: Calculating Collinearity Diagnostics Using PROC REG
Using an Effective Modeling Cycle

Practice Exercise

Practice: Using PROC REG to Examine Residuals
Question 5.01
Practice: Using PROC REG to Generate Potential Outliers
Question 5.02
Question 5.03
Practice: Using PROC REG to Assess Collinearity
Model Post-Fitting for Inference

Week 4: Model Building for Scoring and Prediction

Videos

Overview
Scenario
Predictive Modeling Terminology
Model Complexity
Building a Predictive Model
Model Assessment and Selection
Demo: Building a Predictive Model Using PROC GLMSELECT
Scenario
Preparing for Scoring
Methods of Scoring
Demo: Scoring Data Using PROC PLM

Readings

Partitioning a Data Set Using PROC GLMSELECT

Practice Exercise

Question 6.01
Practice: Building a Predictive Model Using PROC GLMSELECT
Practice: Scoring Using the SCORE Statement in PROC GLMSELECT
Model Building for Scoring and Prediction

Week 5: Categorical Data Analysis

Videos

Overview
Scenario
Associations between Categorical Variables
Demo: Examining the Distribution of Categorical Variables Using PROC FREQ and PROC UNIVARIATE
Scenario
The Pearson Chi-Square Test
Odds Ratios
Demo: Performing a Pearson Chi-Square Test of Association Using PROC FREQ
Scenario
The Mantel-Haenszel Chi-Square Test
The Spearman Correlation Statistic
Demo: Detecting Ordinal Associations Using PROC FREQ
Scenario
Modeling a Binary Response
Demo: Fitting a Binary Logistic Regression Model Using PROC LOGISTIC
Interpreting the Odds Ratio
Comparing Pairs to Assess the Fit of a Logistic Regression Model
Scenario
Specifying a Parameterization Method
Demo: Fitting a Multiple Logistic Regression Model with Categorical Predictors Using PROC LOGISTIC
Scenario
Interactions between Variables
Demo: Fitting a Multiple Logistic Regression Model with Interactions Using PROC LOGISTIC
Demo: Fitting a Multiple Logistic Regression Model with All Odds Ratios Using PROC LOGISTIC
Demo: Generating Predictions Using PROC PLM

Practice Exercise

Question 7.01
Question 7.02
Practice: Using PROC FREQ to Examine Distributions
Question 7.03
Question 7.04
Question 7.05
Question 7.06
Practice: Using PROC FREQ to Perform Tests and Measures of Association
Question 7.07
Question 7.08
Practice: Using PROC LOGISTIC to Perform a Binary Logistic Regression Analysis
Question 7.09
Question 7.10
Practice: Using PROC LOGISTIC to Perform a Multiple Logistic Regression Analysis with Categorical Variables
Question 7.11
Question 7.12
Practice: Using PROC LOGISTIC to Perform Backward Elimination and PROC PLM to Generate Predictions
Categorical Data Analysis

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