Course: Statistics with SAS
Modules:
Welcome and Meet the Instructor, Demo: Exploring Ames Housing Data
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
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
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
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
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
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
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