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⇱ Introduction to Statistical Analysis: Hypothesis Testing | Coursera

Introduction to Statistical Analysis: Hypothesis Testing

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Introduction to Statistical Analysis: Hypothesis Testing

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Gain insight into a topic and learn the fundamentals.
4.7

175 reviews

Intermediate level
Some related experience required
Flexible schedule
1 week at 10 hours a week
Learn at your own pace
96%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
4.7

175 reviews

Intermediate level
Some related experience required
Flexible schedule
1 week at 10 hours a week
Learn at your own pace
96%
Most learners liked this course

Build your Data Analysis expertise

This course is part of the SAS Statistical Business Analyst Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate from SAS

There are 3 modules in this course

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.

Welcome! We'll get started with a brief overview of the course and set up data you need to do the practices in this course. Then, you will learn about the models required to analyze different types of data and the difference between explanatory vs predictive modeling. You will review fundamental statistical concepts, such as the sampling distribution of a mean, hypothesis testing, p-values, and confidence intervals. After reviewing these concepts, you apply one-sample and two-sample t tests to data to confirm or reject preconceived hypotheses.

What's included

19 videos6 readings8 assignments

19 videos•Total 54 minutes
  • Welcome and Meet the Instructor•2 minutes
  • Demo: Exploring Ames Housing Data•11 minutes
  • Overview•1 minute
  • Statistical Modeling: Types of Variables•1 minute
  • Overview of Models•3 minutes
  • Explanatory versus Predictive Modeling•1 minute
  • Population Parameters and Sample Statistics•2 minutes
  • Normal (Gaussian) Distribution•3 minutes
  • Standard Error of the Mean•1 minute
  • Confidence Intervals•2 minutes
  • Statistical Hypothesis Test•4 minutes
  • p-Value: Effect Size and Sample Size Influence•3 minutes
  • Scenario•1 minute
  • Performing a t Test•4 minutes
  • Demo: Performing a One-Sample t Test Using PROC TTEST•4 minutes
  • Scenario•1 minute
  • Assumptions for the Two-Sample t Test•2 minutes
  • Testing for Equal and Unequal Variances•2 minutes
  • Demo: Performing a Two-Sample t Test Using PROC TTEST•5 minutes
6 readings•Total 51 minutes
  • Learner Prerequisites•1 minute
  • Access SAS Software and Set up Data (REQUIRED)•10 minutes
  • Completing Demos and Practices•10 minutes
  • Course Logistics FAQs•10 minutes
  • Parameters and Statistics•10 minutes
  • Normal Distribution•10 minutes
8 assignments•Total 145 minutes
  • Knowledge Check: Statistical Modeling•15 minutes
  • Knowledge Check: Statistical Concepts•15 minutes
  • Knowledge Check: Hypothesis Test & p-value•15 minutes
  • Knowledge Check: One-Sample t Tests•15 minutes
  • Using PROC TTEST to Perform a One-Sample t Test•20 minutes
  • Knowledge Check: Two-Sample t Tests•15 minutes
  • Practice - Using PROC TTEST to Compare Groups•20 minutes
  • Introduction and Review of Concepts•30 minutes

In this module you learn to use graphical tools that can help determine which predictors are likely or unlikely to be useful. Then you learn to augment these graphical explorations with correlation analyses that describe linear relationships between potential predictors and our response variable. After you determine potential predictors, tools like ANOVA and regression help you assess the quality of the relationship between the response and predictors.

What's included

29 videos2 readings14 assignments

29 videos•Total 70 minutes
  • Overview•1 minute
  • Scenario•1 minute
  • Identifying Associations in ANOVA with Box Plots•2 minutes
  • Demo: Exploring Associations Using PROC SGPLOT•2 minutes
  • Identifying Associations in Linear Regression with Scatter Plots•1 minute
  • Demo: Exploring Associations Using PROC SGSCATTER•2 minutes
  • Scenario•1 minute
  • The ANOVA Hypothesis•1 minute
  • Partitioning Variability in ANOVA•2 minutes
  • Coefficient of Determination•1 minute
  • F Statistic and Critical Values•2 minutes
  • The ANOVA Model•3 minutes
  • Demo: Performing a One-Way ANOVA Using PROC GLM•7 minutes
  • Scenario•1 minute
  • Multiple Comparison Methods•3 minutes
  • Tukey's and Dunnett's Multiple Comparison Methods•2 minutes
  • Diffograms and Control Plots•1 minute
  • Demo: Performing a Post Hoc Pairwise Comparison Using PROC GLM•7 minutes
  • Scenario•1 minute
  • Using Correlation to Measure Relationships between Continuous Variables•1 minute
  • Hypothesis Testing for a Correlation•1 minute
  • Avoiding Common Errors When Interpreting Correlations•5 minutes
  • Demo: Producing Correlation Statistics and Scatter Plots Using PROC CORR•6 minutes
  • Scenario•1 minute
  • The Simple Linear Regression Model•1 minute
  • How SAS Performs Simple Linear Regression•1 minute
  • Comparing the Regression Model to a Baseline Model•2 minutes
  • Hypothesis Testing and Assumptions for Linear Regression•1 minute
  • Demo: Performing Simple Linear Regression Using PROC REG•7 minutes
2 readings•Total 20 minutes
  • What Does a CLASS Statement Do?•10 minutes
  • Correlation Analysis and Model Building•10 minutes
14 assignments•Total 155 minutes
  • Question 2.01•5 minutes
  • Question 2.02•5 minutes
  • Question 2.03•5 minutes
  • Question 2.04•5 minutes
  • Practice - Performing a One-Way ANOVA•20 minutes
  • Question 2.05•5 minutes
  • Question 2.06•5 minutes
  • Practice - Using PROC GLM to Perform Post Hoc Parwise Comparisons•20 minutes
  • Question 2.07•5 minutes
  • Question 2.08•5 minutes
  • Practice - Describing the Relationship between Continuous Variables•20 minutes
  • Question 2.09•5 minutes
  • Practice - Using PROC REG to Fit a Simple Linear Regression Model•20 minutes
  • ANOVA and Regression•30 minutes

In this module you expand the one-way ANOVA model to a two-factor analysis of variance and then extend simple linear regression to multiple regression with two predictors. After you understand the concepts of two-way ANOVA and multiple linear regression with two predictors, you'll have the skills to fit and interpret models with many variables.

What's included

13 videos1 reading5 assignments

13 videos•Total 43 minutes
  • Overview•2 minutes
  • Scenario•1 minute
  • Applying the Two-Way ANOVA Model•4 minutes
  • Demo: Performing a Two-Way ANOVA Using PROC GLM•7 minutes
  • Interactions•3 minutes
  • Demo: Performing a Two-Way ANOVA With an Interaction Using PROC GLM•6 minutes
  • Demo: Performing Post-Processing Analysis Using PROC PLM•4 minutes
  • Scenario•1 minute
  • The Multiple Linear Regression Model•3 minutes
  • Hypothesis Testing for Multiple Regression•1 minute
  • Multiple Linear Regression versus Simple Linear Regression•3 minutes
  • Adjusted R-Square•2 minutes
  • Demo: Fitting a Multiple Linear Regression Model Using PROC REG•7 minutes
1 reading•Total 10 minutes
  • The STORE Statement•10 minutes
5 assignments•Total 80 minutes
  • Question 3.01•5 minutes
  • Practice - Performing a Two-Way ANOVA Using PROC GLM•20 minutes
  • Question 3.02•5 minutes
  • Practice - Performing Multiple Regression Using PROC REG•20 minutes
  • More Complex Linear Models•30 minutes

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4.6 (58 ratings)
SAS
4 Courses•61,611 learners

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SB
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Reviewed on Dec 6, 2025

excellent course - moreless a refresher course for me but still an excellent overall very thorough study

PS
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Reviewed on Sep 29, 2023

The Course was excellent. The study materials were very clear and understandable.

RM
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Reviewed on Jun 14, 2021

Thank you so much to the instructor, Jordan Bakerman for teaching this course.

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