Statistics with SAS

  • 4.8
Approx. 20 hours to complete
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Course Summary

Learn how to use SAS to analyze statistical data and create reports. This course covers topics such as data manipulation, regression analysis, and hypothesis testing.

Key Learning Points

  • SAS is a powerful statistical software used by many industries for data analysis
  • Learn how to manipulate and analyze data using SAS
  • Create reports and visualizations to communicate your findings

Related Topics for further study


Learning Outcomes

  • Use SAS to manipulate and analyze data
  • Understand statistical analysis and hypothesis testing
  • Create reports and visualizations to communicate findings

Prerequisites or good to have knowledge before taking this course

  • Basic understanding of statistics
  • Ability to use a computer and navigate software

Course Difficulty Level

Intermediate

Course Format

  • Online self-paced course
  • Video lectures
  • Hands-on exercises
  • Quizzes

Similar Courses

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  • Data Analysis and Interpretation

Related Education Paths


Related Books

Description

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.

Outline

  • Course Overview and Data Setup
  • Welcome and Meet the Instructor
  • Demo: Exploring Ames Housing Data
  • Learner Prerequisites
  • Access SAS Software and Set Up Practice Files (REQUIRED)
  • Completing Demos and Practices
  • Using Forums and Getting Help

  • Introduction and Review of Concepts
  • 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
  • Parameters and Statistics
  • Normal Distribution
  • 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

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

  • More Complex Linear Models
  • 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
  • The STORE Statement
  • 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

  • Model Building and Effect Selection
  • 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
  • Activity - Optional Stepwise Selection Method Code
  • Information Criteria Penalty Components
  • 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

  • Model Post-Fitting for Inference
  • 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: 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

  • Model Building for Scoring and Prediction
  • 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
  • Partitioning a Data Set Using PROC GLMSELECT
  • 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

  • Categorical Data Analysis
  • 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
  • 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

Summary of User Reviews

The SAS Statistics course on Coursera has received high praise from users. Many have found it to be a comprehensive and accessible resource for learning SAS.

Key Aspect Users Liked About This Course

The course's excellent instructors are frequently cited as a key strength.

Pros from User Reviews

  • Great instructors who are knowledgeable and engaging
  • Easy-to-follow lessons and exercises
  • Comprehensive coverage of SAS statistics
  • Effective use of real-world examples
  • Flexible pacing and scheduling

Cons from User Reviews

  • Some users found the course too basic or slow-paced
  • Limited interaction with instructors and peers
  • Occasional technical difficulties with course materials
  • Not suitable for those without prior SAS experience
  • Lack of hands-on practice or real-world projects

Keywords

English
Available now
Approx. 20 hours to complete
Jordan Bakerman
SAS
Coursera

Instructor

Jordan Bakerman

  • 4.8 Raiting

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