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Course
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Statistical Modeling and Hypothesis Testing in R
Statistical analysis is key to extracting insights from data, but choosing the right methods and interpreting results correctly can be complex. This course will teach you how to make data-driven decisions with confidence.
What you'll learn
Making data-driven decisions requires more than just collecting data—it requires applying the right statistical methods and correctly interpreting results. In this course, Statistical Modeling and Hypothesis Testing in R, you’ll gain the ability to perform hypothesis testing, build statistical models, and effectively communicate findings using R. First, you’ll explore fundamental hypothesis testing techniques, including t-tests, ANOVA, MANOVA, and Chi-square tests, to compare groups and analyze categorical data. Next, you’ll discover how to build and interpret statistical models, from linear regression to generalized linear models (GLMs) for binary and count data, ensuring robust predictions and data analysis. Finally, you’ll learn how to apply advanced statistical techniques such as mixed-effects models for hierarchical data and survival analysis for time-to-event modeling. When you’re finished with this course, you’ll have the skills and knowledge of statistical analysis in R needed to confidently analyze data, assess model assumptions, and make informed, data-driven decisions.
Table of contents
About the author
A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.
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