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

4 Hours13 Videos53 Exercises

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Course Description

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!

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1. 1

Introduction to Experimental Design

Free

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

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Introduction to experimental design
50 xp
A basic experiment
100 xp
Randomization
100 xp
Replication and blocking
50 xp
Replication
100 xp
Blocking
100 xp
Hypothesis testing
50 xp
One sided vs. Two-sided tests
100 xp
pwr package Help Docs exploration
50 xp
Power & Sample Size Calculations
100 xp
2. 3

Randomized Complete and 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.

3. 4

Latin Squares, Graeco-Latin Squares, and Factorial Experiments

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

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Statistician with R

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Datasets

sample of Lending Club dataNHANES Body MeasuresNHANES DemographicsNHANES final combined datasetNHANES Medical ConditionsNYC SAT Scores

Collaborators

Joanne Xiong

Consultant & Data Analyst

Joanne is a Consultant and Data Analyst working in the financial industry. She holds a Master’s degree in Statistical Science from the University of Oxford. Her passion is applying statistics, data science, and AI to a broad range of fields, ranging from fintech to quantitative psychology.
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