Introduction to Statistical Analysis: Hypothesis Testing
- 4.8
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Approx. 10 hours to complete
Course Summary
Learn how to use SAS software to perform statistical analysis and hypothesis testing in this comprehensive course.Key Learning Points
- Understand the fundamental concepts of statistical analysis and hypothesis testing
- Learn how to use SAS software to perform statistical analysis
- Gain knowledge of different types of statistical tests and their applications
Job Positions & Salaries of people who have taken this course might have
- USA: $62,000
- India: ₹400,000
- Spain: €30,000
- USA: $62,000
- India: ₹400,000
- Spain: €30,000
- USA: $59,000
- India: ₹350,000
- Spain: €28,000
- USA: $62,000
- India: ₹400,000
- Spain: €30,000
- USA: $59,000
- India: ₹350,000
- Spain: €28,000
- USA: $72,000
- India: ₹500,000
- Spain: €35,000
Related Topics for further study
Learning Outcomes
- Ability to perform statistical analysis and hypothesis testing using SAS software
- Understanding of different types of statistical tests and their applications
- Knowledge of how to interpret and communicate statistical results
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics
- Familiarity with SAS software
Course Difficulty Level
IntermediateCourse Format
- Online self-paced
- Video lectures
- Hands-on exercises
- Quizzes and assessments
Similar Courses
- Data Analysis and Statistical Inference
- Applied Data Science with Python
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 for this Course
- Follow These Instructions to Set Up Data for This Course (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
Summary of User Reviews
Read reviews for the Statistical Analysis & Hypothesis Testing course on Coursera. Users rave about the practical real-world examples, making the course easy to understand. Overall, the course has a high rating.Key Aspect Users Liked About This Course
Real-world examples that make the course easy to understandPros from User Reviews
- The course is very practical and applicable to real-life scenarios
- The lectures are well-structured and easy to follow
- The course covers a wide range of statistical concepts and techniques
- The instructors are knowledgeable and responsive to questions
- The course provides a good foundation for further statistical analysis
Cons from User Reviews
- Some users found the course to be too basic and not challenging enough
- The course may be difficult for those with no prior statistical knowledge
- Some users found the course to be too focused on SAS software
- The quizzes and assignments could be more challenging
- The course may not be suitable for those looking for a more theoretical approach to statistics
Jordan Bakerman
4.8 Raiting
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