In this course, you will develop and test hypotheses about your data. You will learn a variety of statistical tests, as well as strategies to know how to apply the appropriate one to your specific data and question. Using your choice of two powerful statistical software packages (SAS or Python), you will explore ANOVA, Chi-Square, and Pearson correlation analysis. This course will guide you through basic statistical principles to give you the tools to answer questions you have developed. Throughout the course, you will share your progress with others to gain valuable feedback and provide insight to other learners about their work.
This course is part of the Data Analysis and Interpretation Specialization
Offered By
About this Course
Skills you will gain
- Chi-Squared (Chi-2) Distribution
- Data Analysis
- Statistical Hypothesis Testing
- Analysis Of Variance (ANOVA)
Offered by

Wesleyan University
Wesleyan University, founded in 1831, is a diverse, energetic liberal arts community where critical thinking and practical idealism go hand in hand. With our distinctive scholar-teacher culture, creative programming, and commitment to interdisciplinary learning, Wesleyan challenges students to explore new ideas and change the world. Our graduates go on to lead and innovate in a wide variety of industries, including government, business, entertainment, and science.
Syllabus - What you will learn from this course
Hypothesis Testing and ANOVA
This session starts where the Data Management and Visualization course left off. Now that you have selected a data set and research question, managed your variables of interest and visualized their relationship graphically, we are ready to test those relationships statistically. The first group of videos describe the process of hypothesis testing which you will use throughout this course to test relationships between different kinds of variables (quantitative and categorical). Next, we show you how to test hypotheses in the context of Analysis of Variance (when you have one quantitative variable and one categorical variable). Your task will be to write a program that manages any additional variables you may need and runs and interprets an Analysis of Variance test. Note that if your research question does not include one quantitative variable, you can use one from your data set just to get some practice with the tool. If your research question does not include a categorical variable, you can categorize one that is quantitative.
Chi Square Test of Independence
This session shows you how to test hypotheses in the context of a Chi-Square Test of Independence (when you have two categorical variables). Your task will be to write a program that manages any additional variables you may need and runs and interprets a Chi-Square Test of Independence. Note that if your research question only includes quantitative variables, you can categorize those just to get some practice with the tool.
Pearson Correlation
This session shows you how to test hypotheses in the context of a Pearson Correlation (when you have two quantitative variables). Your task will be to write a program that manages any additional variables you may need and runs and interprets a correlation coefficient. Note that if your research question only includes categorical variables, you can choose other variables from your data set just to get some practice with the tool.
Exploring Statistical Interactions
In this session, we will discuss the basic concept of statistical interaction (also known as moderation). In statistics, moderation occurs when the relationship between two variables depends on a third variable. The effect of a moderating variable is often characterized statistically as an interaction; that is, a third variable that affects the direction and/or strength of the relation between your explanatory (X) and response (Y) variable. Your task will be to test your own research question in the context of one or more potential moderating variables.
Reviews
- 5 stars68.81%
- 4 stars21.78%
- 3 stars5.69%
- 2 stars2.22%
- 1 star1.48%
TOP REVIEWS FROM DATA ANALYSIS TOOLS
Again, with no formal SAS training and minimal statistics background. I found taking the first course and then this course - week after week my knowledge grew in a consistent and organized fashion.
Quick, easy and Practical way to learning statistical programming and data analysis with SAS/ Python.
It's one of the best course for understanding all the statistical tools , used for data sciences. Thanks to entire team for making such a wonderful course content
This was good module. It covers the basics of inferential statistical techniques along with its application using SAS/Python. I would definitely recommend to take up if you are a beginner.
About the Data Analysis and Interpretation Specialization
Learn SAS or Python programming, expand your knowledge of analytical methods and applications, and conduct original research to inform complex decisions.

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