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Experimental Design in R

via Datacamp


In this course you'll learn about basic experimental design, a crucial part of any data analysis.

Experimental design is a crucial part of data analysis in any field, whether you work in business, health or tech. If you want to use data to answer a question, you need to design an experiment! In this course you will learn about basic experimental design, including block and factorial designs, and commonly used statistical tests, such as the t-tests and ANOVAs. You will use built-in R data and real world datasets including the CDC NHANES survey, SAT Scores from NY Public Schools, and Lending Club Loan Data. Following the course, you will be able to design and analyze your own experiments!


Introduction to Experimental Design
-An introduction to key parts of experimental design plus some power and sample size calculations.

Basic Experiments
-Explore the Lending Club dataset plus build and validate basic experiments, including an A/B test.

Randomized Complete (& Balanced Incomplete) Block Designs
-Use the NHANES data to build a RCBD and BIBD experiment, including model validation and design tips to make sure the BIBD is valid.

Latin Squares, Graeco-Latin Squares, & Factorial experiments
-Evaluate the NYC SAT scores data and deal with its missing values, then evaluate Latin Square, Graeco-Latin Square, and Factorial experiments.

Taught by

kaelen medeiros

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