Introduction to Statistical Analysis: Hypothesis Testing

This course is part of SAS Statistical Business Analyst Professional Certificate

Instructor: Jordan Bakerman

Access provided by Lok Jagruti University

23,393 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

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

4 modules

Gain insight into a topic and learn the fundamentals.

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

Skills you'll gain

Tools you'll learn

SAS (Software)

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

28 assignments

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

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 4 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.

In this module you learn about the course and the data you analyze in this course. Then you set up the data you need to do the practices in the course.

What's included

2 videos6 readings

2 videos Total 13 minutes

Welcome and Meet the Instructor 2 minutes
Demo: Exploring Ames Housing Data 11 minutes

6 readings Total 66 minutes

Learner Prerequisites 1 minute
Access SAS Software for this Course 10 minutes
Follow These Instructions to Set Up Data for This Course (REQUIRED) 30 minutes
Completing Demos and Practices 10 minutes
Using the Community Forum and Getting Help 5 minutes
Frequently Asked Questions 10 minutes

In this module you learn about the models required to analyze different types of data and the difference between explanatory vs predictive modeling. Then you 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

17 videos2 readings9 assignments

17 videos Total 41 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

2 readings Total 20 minutes

Parameters and Statistics 10 minutes
Normal Distribution 10 minutes

9 assignments Total 100 minutes

Introduction and Review of Concepts 30 minutes
Question 1.01 5 minutes
Question 1.02 5 minutes
Question 1.03 5 minutes
Question 1.04 5 minutes
Question 1.05 5 minutes
Practice - Using PROC TTEST to Perform a One-Sample t Test 20 minutes
Question 1.06 5 minutes
Practice - Using PROC TTEST to Compare Groups 20 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

ANOVA and Regression 30 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

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.