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- Statistics with SAS
Intermediate
Online
5 Weeks
Free
Quick facts
particular | details | |
---|---|---|
Medium of instructions
English
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Mode of learning
Self study
|
Mode of Delivery
Video and Text Based
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Course and certificate fees
Type of course
certificate availability
certificate providing authority
certificate fees
The syllabus
Week 1: Course Overview and Data Setup
Videos
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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