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Suppose you’re interested in what causes the spread of a disease, or how likely an applicant is to default on a bank loan. But how do you answer these questions from your data? We’re going to start off in Chapter by exploring our data and learning about the SAS tools that can help us answer our questions.

It’s often impossible to gather data on an entire population, such as every single person who gets sick or defaults on a loan, so we’ll learn how to make inferences from our data samples of those populations. These inferences will help us answer our questions so we can make decisions about future research or business strategies.

We’ll begin by briefly discussing the models required to analyze different types of data and the difference between explanatory vs predictive modeling. We’ll then move into a review of fundamental statistical concepts, such as the sampling distribution of a mean, hypothesis testing, p-values, and confidence intervals.

After reviewing these concepts, we’ll apply one-sample and two-sample t tests to our Ames Housing data to confirm or reject preconceived hypotheses.

Course Curriculum

Introduction and Review of Concepts
Introduction and Review of Concepts Details 00:10:00
Statistical Modeling: Types of Variables
Statistical Modeling: Types of Variables Details 00:35:00
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Overview of Models
Overview of Models Details 00:45:00
Explanatory versus Predictive Modeling
Explanatory versus Predictive Modeling Details 00:50:00
Population Parameters and Sample Statistics
Population Parameters and Sample Statistics Details 00:00:00
Read about It: Parameters and Statistics
Read about It: Parameters and Statistics Details 00:30:00
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Normal (Gaussian) Distribution
Normal (Gaussian) Distribution Details 00:20:00
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Read about It: Normal Distribution
Read about It: Normal Distribution Details 00:45:00
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Standard Error of the Mean
Standard Error of the Mean Details 00:00:00
Confidence Intervals
Confidence Intervals Details 00:00:00
Statistical Hypothesis Test
Statistical Hypothesis Test Details FREE 00:45:00
p-Value: Effect Size and Sample Size Influence
p-Value: Effect Size and Sample Size Influence Details 00:25:00
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One-Sample t Tests Scenario
One-Sample t Tests Scenario Details 00:40:00
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Performing a t Test
Performing a t Test Details 01:00:00
Demo: Performing a One-Sample t Test Using PROC TTEST
Demo: Performing a One-Sample t Test Using PROC TTEST Details 00:20:00
Practice: Performing a One-Sample t Test
Practice: Performing a One-Sample t Test Details FREE 00:30:00
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Two-Sample t Tests Scenario
Two-Sample t Tests Details 00:20:00
Assumptions for the Two-Sample t Test
Assumptions for the Two-Sample t Test Details 00:45:00
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Testing for Equal and Unequal Variances
Testing for Equal and Unequal Variances Details 00:30:00
Demo: Performing a Two-Sample t Test Using PROC TTEST
Demo: Performing a Two-Sample t Test Using PROC TTEST Details 00:30:00
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Practice: Comparing Groups
Practice: Comparing Groups Details 00:12:00
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Lesson 1 Text Version
Lesson 1 Text Version Details 00:10:00
Lesson 1 Text Version
ANOVA and Regression OverView
ANOVA and Regression OverView Details 00:10:00
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Graphical Analysis of Associations Scenario
Graphical Analysis of Associations Scenario Details 00:20:00
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Identifying Associations in ANOVA with Box Plots
Identifying Associations in ANOVA with Box Plots Details 01:00:00
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Demo: Exploring Associations Using PROC SGPLOT
Demo: Exploring Associations Using PROC SGPLOT Details 00:35:00
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Identifying Associations in Linear Regression with Scatter Plots
Identifying Associations in Linear Regression with Scatter Plots Details 00:30:00
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Demo: Exploring Associations Using PROC SGSCATTER
Demo: Exploring Associations Using PROC SGSCATTER Details 00:22:00
One-Way ANOVA Scenario
One-Way ANOVA Scenario Details 00:20:00
The ANOVA Hypothesis
The ANOVA Hypothesis Details 00:30:00
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Partitioning Variability in ANOVA
Partitioning Variability in ANOVA Details 00:35:00
Coefficient of Determination
Coefficient of Determination Details FREE 00:12:00
F Statistic and Critical Values
F Statistic and Critical Values Details 00:20:00
The ANOVA Model
The ANOVA Model Details 00:45:00
Demo: Performing a One-Way ANOVA Using PROC GLM
Demo: Performing a One-Way ANOVA Using PROC GLM Details 00:45:00
Read about It: What Does a CLASS Statement Do?
Read about It: What Does a CLASS Statement Do? Details 00:22:00
Practice: Performing a One-Way ANOVA
Practice: Performing a One-Way ANOVA Details 00:10:00
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ANOVA Post Hoc Tests Scenario
ANOVA Post Hoc Tests Scenario Details FREE 00:20:00
Multiple Comparison Methods
Multiple Comparison Methods Details 00:20:00
Tukey's and Dunnett's Multiple Comparison Methods
Tukey’s and Dunnett’s Multiple Comparison Methods Details FREE 00:30:00
Diffograms and Control Plots
Diffograms and Control Plots Details FREE 00:40:00
Demo: Performing a Post Hoc Pairwise Comparison Using PROC GLM
Demo: Performing a Post Hoc Pairwise Comparison Using PROC GLM Details FREE 00:40:00
Practice: Post Hoc Pairwise Comparisons
Practice: Post Hoc Pairwise Comparisons Details FREE 00:30:00
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Pearson Correlation Scenario
Pearson Correlation Scenario Details 00:35:00
Using Correlation to Measure Relationships between Continuous Variables
Using Correlation to Measure Relationships between Continuous Variables Details 00:05:00
Hypothesis Testing for a Correlation
Hypothesis Testing for a Correlation Details 00:30:00
Avoiding Common Errors When Interpreting Correlations
Avoiding Common Errors When Interpreting Correlations Details 00:30:00
Demo: Producing Correlation Statistics and Scatter Plots Using PROC CORR
Demo: Producing Correlation Statistics and Scatter Plots Using PROC CORR Details 00:20:00
Read about It: Correlation Analysis and Model Building
Read about It: Correlation Analysis and Model Building Details 00:25:00
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Practice: Describing the Relationship between Continuous Variables
Practice: Describing the Relationship between Continuous Variables Details 00:30:00
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Simple Linear Regression Scenario
Simple Linear Regression Scenario Details 00:30:00
The Simple Linear Regression Model
The Simple Linear Regression Model Details 00:10:00
How SAS Performs Simple Linear Regression
How SAS Performs Simple Linear Regression Details 00:00:00
Comparing the Regression Model to a Baseline Model
Comparing the Regression Model to a Baseline Model Details FREE 00:15:00
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Hypothesis Testing and Assumptions for Linear Regression
Hypothesis Testing and Assumptions for Linear Regression Details 00:10:00
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Demo: Performing Simple Linear Regression Using PROC REG
Demo: Performing Simple Linear Regression Using PROC REG Details 00:35:00
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Practice: Fitting a Simple Linear Regression Model
Practice: Fitting a Simple Linear Regression Model Details 00:40:00
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Lesson 2 Text Version
Lesson 2 Text Version Details 00:30:00
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More Complex Linear Models Introduction
More Complex Linear Models Introduction Details FREE 00:40:00
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Two-Way ANOVA and Interactions Scenario
Two-Way ANOVA and Interactions Scenario Details 00:30:00
Applying the Two-Way ANOVA Model
Applying the Two-Way ANOVA Model Details 00:20:00
Demo: Performing a Two-Way ANOVA Using PROC GLM
Demo: Performing a Two-Way ANOVA Using PROC GLM Details 00:22:00
Interactions
Interactions Details 00:40:00
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Demo: Performing a Two-Way ANOVA With an Interaction Using PROC GLM
Demo: Performing a Two-Way ANOVA With an Interaction Using PROC GLM Details FREE 00:40:00
Read about It: STORE Statement person reading
Read about It: STORE Statement person reading Details 01:00:00
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Demo: Performing Post-Processing Analysis Using PROC PLM
Demo: Performing Post-Processing Analysis Using PROC PLM Details 00:20:00
Practice: Performing a Two-Way ANOVA
Practice: Performing a Two-Way ANOVA Details 00:30:00
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Multiple Regression Scenario
Multiple Regression Scenario Details FREE 00:20:00
The Multiple Linear Regression Model
The Multiple Linear Regression Model Details 00:30:00
Hypothesis Testing for Multiple Regression
Hypothesis Testing for Multiple Regression Details 00:20:00
Multiple Linear Regression versus Simple Linear Regression
Multiple Linear Regression versus Simple Linear Regression Details 00:25:00
Adjusted R-Square
Adjusted R-Square Details 00:30:00
Demo: Fitting a Multiple Linear Regression Model Using PROC REG
Demo: Fitting a Multiple Linear Regression Model Using PROC REG Details 00:55:00
Practice: Performing Multiple Regression
Practice: Performing Multiple Regression Details 00:30:00
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Lesson 3 Text Version
Lesson 3 Text Version Details 00:20:00
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Model Building and Effect Selection Introduction
Model Building and Effect Selection Introduction Details 00:30:00
Stepwise Selection Using Significance Level Scenario
Stepwise Selection Using Significance Level Scenario Details 00:20:00
Approaches to Selecting Models
Approaches to Selecting Models Details 00:30:00
The All-Possible Regressions Approach to Model Building
The All-Possible Regressions Approach to Model Building Details 00:40:00
The Stepwise Selection Approach to Model Building
The Stepwise Selection Approach to Model Building Details 00:20:00
Interpreting p-Values and Parameter Estimates
Interpreting p-Values and Parameter Estimates Details 00:20:00
Demo: Performing Stepwise Regression Using PROC GLMSELECT
Demo: Performing Stepwise Regression Using PROC GLMSELECT Details 00:20:00
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Activity: Optional Stepwise Selection Method Code
Activity: Optional Stepwise Selection Method Code Details 00:35:00
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Practice: Using Significance-Level Model Selection Techniques
Practice: Using Significance-Level Model Selection Techniques Details 00:00:00
Information Criterion and Other Selection Options Scenario
Information Criterion and Other Selection Options Scenario Details 00:00:00
Information Criteria
Information Criteria Details 00:00:00
Read About It: Information Criteria Penalty Components
Read About It: Information Criteria Penalty Components Details 00:00:00
Adjusted R-Square and Mallows' Cp
Adjusted R-Square and Mallows’ Cp Details 00:00:00
Demo: Performing Model Selection Using PROC GLMSELECT
Demo: Performing Model Selection Using PROC GLMSELECT Details 00:00:00
Practice: Using Other Model Selection Techniques
Practice: Using Other Model Selection Techniques Details 00:00:00
Chapter 4 Model Building and Effect Selection
Chapter 4 Model Building and Effect Selection Details 00:00:00
Lesson 4 Text Version
Lesson 4 Text Version Details 00:00:00
Model Post-Fitting for Inference Introduction
Model Post-Fitting for Inference Introduction Details 00:00:00
Examining Residuals Scenario
Examining Residuals Scenario Details 00:00:00
Assumptions for Regression
Assumptions for Regression Details 00:00:00
Verifying Assumptions Using Residual Plots
Verifying Assumptions Using Residual Plots Details 00:00:00
Demo: Examining Residual Plots Using PROC REG
Demo: Examining Residual Plots Using PROC REG Details 00:00:00
Practice: Examining Residuals
Practice: Examining Residuals Details 00:00:00
Influential Observations Scenario
Influential Observations Scenario Details 00:00:00
Identifying Influential Observations
Identifying Influential Observations Details 00:00:00
Checking for Outliers with STUDENT Residuals
Checking for Outliers with STUDENT Residuals Details 00:00:00
Checking for Influential Observations
Checking for Influential Observations Details 00:00:00
Detecting Influential Observations with DFBETAS
Detecting Influential Observations with DFBETAS Details 00:00:00
Demo: Looking for Influential Observations Using PROC GLMSELECT and PROC REG
Demo: Looking for Influential Observations Using PROC GLMSELECT and PROC REG Details 00:00:00
Demo: Examining the Influential Observations Using PROC PRINT
Demo: Examining the Influential Observations Using PROC PRINT Details 00:00:00
Handling Influential Observations
Handling Influential Observations Details 00:00:00
Practice: Generating Potential Outliers
Practice: Generating Potential Outliers Details 00:00:00
Collinearity Scenario
Collinearity Scenario Details 00:00:00
Exploring Collinearity
Exploring Collinearity Details 00:00:00
Visualizing Collinearity
Visualizing Collinearity Details 00:00:00
Demo: Calculating Collinearity Diagnostics Using PROC REG
Demo: Calculating Collinearity Diagnostics Using PROC REG Details 00:00:00
Using an Effective Modeling Cycle
Using an Effective Modeling Cycle Details 00:00:00
Practice: Assessing Collinearity
Practice: Assessing Collinearity Details 00:00:00
Lesson 5 Text Version
Lesson 5 Text Version Details 00:00:00
Model Building for Scoring and Prediction Introduction
Model Building for Scoring and Prediction Introduction Details 00:00:00
Brief Introduction to Predictive Modeling Scenario
Brief Introduction to Predictive Modeling Scenario Details 00:00:00
Predictive Modeling Terminology
Predictive Modeling Terminology Details 00:00:00
Model Complexity
Model Complexity Details 00:00:00
Building a Predictive Model
Building a Predictive Model Details 00:00:00
Model Assessment and Selection
Model Assessment and Selection Details 00:00:00
Demo: Building a Predictive Model Using PROC GLMSELECT
Demo: Building a Predictive Model Using PROC GLMSELECT Details 00:00:00
Read About It: Partitioning a Data Set Using PROC GLMSELECT
Read About It: Partitioning a Data Set Using PROC GLMSELECT Details 00:00:00
Practice: Building a Predictive Model Using PRC GLMSELECT to Partition
Practice: Building a Predictive Model Using PRC GLMSELECT to Partition Details 00:00:00
Scoring Predictive Models Scenario
Scoring Predictive Models Scenario Details 00:00:00
Preparing for Scoring
Preparing for Scoring Details 00:00:00
Methods of Scoring
Methods of Scoring Details 00:00:00
Demo: Scoring Data Using PROC PLM
Demo: Scoring Data Using PROC PLM Details 00:00:00
Practice: Using the SCORE Statement in PROC GLMSELECT
Practice: Using the SCORE Statement in PROC GLMSELECT Details 00:00:00
Lesson 6 Text Version
Lesson 6 Text Version Details 00:00:00
Categorical Data Analysis Introduction
Categorical Data Analysis Introduction Details 00:00:00
Describing Categorical Data Scenario
Describing Categorical Data Scenario Details 00:00:00
Associations between Categorical Variables
Associations between Categorical Variables Details 00:00:00
Demo: Examining the Distribution of Categorical Variables Using PROC FREQ and PROC UNIVARIATE
Demo: Examining the Distribution of Categorical Variables Using PROC FREQ and PROC UNIVARIATE Details 00:00:00
Practice: Examining Distributions
Practice: Examining Distributions Details 00:00:00
Tests of Association Scenario
Tests of Association Scenario Details 00:00:00
The Pearson Chi-Square Test
The Pearson Chi-Square Test Details 00:00:00
Odds Ratios
Odds Ratios Details 00:00:00
Demo: Performing a Pearson Chi-Square Test of Association Using PROC FREQ
Demo: Performing a Pearson Chi-Square Test of Association Using PROC FREQ Details 00:00:00
Scenario
Scenario Details 00:00:00
The Mantel-Haenszel Chi-Square Test
The Mantel-Haenszel Chi-Square Test Details 00:00:00
The Spearman Correlation Statistic
The Spearman Correlation Statistic Details 00:00:00
Demo: Detecting Ordinal Associations Using PROC FREQ
Demo: Detecting Ordinal Associations Using PROC FREQ Details 00:00:00
Practice: Performing Tests and Measures of Association
Practice: Performing Tests and Measures of Association Details 00:00:00
Introduction to Logistic Regression Scenario
Introduction to Logistic Regression Scenario Details 00:00:00
Modeling a Binary Response
Modeling a Binary Response Details 00:00:00
Demo: Fitting a Binary Logistic Regression Model Using PROC LOGISTIC
Demo: Fitting a Binary Logistic Regression Model Using PROC LOGISTIC Details 00:00:00
Interpreting the Odds Ratio
Interpreting the Odds Ratio Details 00:00:00
Comparing Pairs to Assess the Fit of a Logistic Regression Model
Comparing Pairs to Assess the Fit of a Logistic Regression Model Details 00:00:00
Practice: Performing a Binary Logistic Regression Analysis
Practice: Performing a Binary Logistic Regression Analysis Details 00:00:00
Logistic Regression with Categorical Predictors Scenario
Logistic Regression with Categorical Predictors Scenario Details 00:00:00
Specifying a Parameterization Method
Specifying a Parameterization Method Details 00:00:00
Demo: Fitting a Multiple Logistic Regression Model with Categorical Predictors Using PROC LOGISTIC
Demo: Fitting a Multiple Logistic Regression Model with Categorical Predictors Using PROC LOGISTIC Details 00:00:00
Practice: Performing a Multiple Logistic Regression Analysis with Categorical Variables
Practice: Performing a Multiple Logistic Regression Analysis with Categorical Variables Details 00:00:00
Stepwise Selection with Interactions and Predictions Scenario
Stepwise Selection with Interactions and Predictions Scenario Details 00:00:00
Interactions between Variables
Interactions between Variables Details 00:00:00
Demo: Fitting a Multiple Logistic Regression Model with Interactions Using PROC LOGISTIC
Demo: Fitting a Multiple Logistic Regression Model with Interactions Using PROC LOGISTIC Details 00:00:00
Demo: Fitting a Multiple Logistic Regression Model with All Odds Ratios Using PROC LOGISTIC
Demo: Fitting a Multiple Logistic Regression Model with All Odds Ratios Using PROC LOGISTIC Details 00:00:00
Demo: Generating Predictions Using PROC PLM
Demo: Generating Predictions Using PROC PLM Details 00:00:00
Practice: Fitting a Multiple Logistic Regression Model, Saving Analysis Results, and Generating Predictions
Practice: Fitting a Multiple Logistic Regression Model, Saving Analysis Results, and Generating Predictions Details 00:00:00
Lesson 7 Text Version
Lesson 7 Text Version Details 00:00:00

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