Introduction to Statistical Analysis: Hypothesis Testing
This course is part of SAS Statistical Business Analyst Professional Certificate.
This comprehensive course introduces SAS users to essential statistical analysis techniques for hypothesis testing and data interpretation. You'll gain practical skills in t-tests, ANOVA, and regression analysis using SAS/STAT software. The curriculum begins with fundamental statistical concepts including sampling distributions, hypothesis testing, p-values, and confidence intervals. You'll then apply these principles through one-sample and two-sample t-tests to validate or reject hypotheses. As you progress, you'll explore graphical tools and correlation analyses to identify potential predictors and assess relationships between variables. The course expands into ANOVA and regression techniques to evaluate the quality of relationships between response and predictor variables. Advanced topics include two-factor analysis of variance, multiple regression with multiple predictors, and interpretation of complex models. By course completion, you'll have developed practical skills in statistical modeling that can be applied to business analysis and data-driven decision making. The course is designed for intermediate learners with some statistical background and provides hands-on experience with SAS software.
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What you'll learn
Understand fundamental statistical concepts including sampling distributions, p-values, and confidence intervals
Apply one-sample and two-sample t-tests to validate or reject hypotheses
Use graphical tools and correlation analyses to identify relationships between variables
Implement ANOVA to assess differences between group means
Perform simple and multiple linear regression analysis
Interpret statistical results and understand their practical significance
Skills you'll gain
This course includes:
2.75 Hours PreRecorded video
28 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 4 modules in this course
This course provides a comprehensive introduction to statistical analysis using SAS/STAT software, focusing on hypothesis testing and data modeling techniques. Beginning with fundamentals of statistical concepts like sampling distributions and p-values, learners progress to practical applications of t-tests for validating hypotheses. The course then explores graphical tools and correlation analyses to identify potential predictors and relationships between variables. Students master ANOVA techniques to assess relationship quality between response and predictors, followed by regression analysis from simple linear models to multiple regression. The curriculum expands to cover two-way ANOVA, interaction effects, and multiple predictor models, equipping students with the skills to fit and interpret complex statistical models for business analysis applications.
Course Overview and Data Setup
Module 1 · 1 Hours to complete
Introduction and Review of Concepts
Module 2 · 2 Hours to complete
ANOVA and Regression
Module 3 · 4 Hours to complete
More Complex Linear Models
Module 4 · 2 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: SAS Statistical Business Analyst Professional Certificate
Instructor
Statistical Forecasting Expert and Programming Education Innovator
Dr. Jordan Bakerman serves as an Analytical Training Consultant at SAS, where he specializes in bridging open-source and SAS analytics platforms. His Ph.D. research at North Carolina State University focused on leveraging social media data to forecast real-world events, including civil unrest and influenza rates. As the creator of the widely-used "SAS Programming for R Users" course, he developed an innovative cookbook-style approach to help R programmers transition efficiently to SAS. His teaching portfolio includes courses on statistical analysis, regression modeling, and API integration between SAS Viya and open-source platforms. Through his Coursera courses "Introduction to Statistical Analysis: Hypothesis Testing," "Regression Modeling Fundamentals," and "Using SAS Viya REST APIs with Python and R," he shares his expertise in statistical programming and analysis. His work focuses on making advanced statistical concepts accessible while helping professionals integrate open-source tools with SAS technologies.
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