This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The course introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data
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The purpose of this series of courses is to teach the basics of Computational Statistics for the purpose of performing inference to aspiring or new Data Scientists....
This course for practicing and aspiring data scientists and statisticians. It is the fourth of a four-course sequence introducing the fundamentals of Bayesian statistics....
In this course, you will learn to analyze measurement systems for process stability and capability and why having a stable measurement process is imperative prior...
By the end of this project, you will be able to analyze and data and answer three different questions by Data wrangling which is the process of gathering, selecting,...