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Experimental Design in R

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

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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!
  1. 1

    Introduction to Experimental Design

    Free

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

    Play Chapter Now
    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. 2

    Basic Experiments

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

    Play Chapter Now
    ANOVA, single, and multiple factor experiments
    50 xp
    Exploratory Data Analysis (EDA) Lending Club
    100 xp
    How does loan purpose affect amount funded?
    100 xp
    Which loan purpose mean is different?
    100 xp
    Multiple Factor Experiments
    100 xp
    Model validation
    50 xp
    Pre-modeling EDA
    100 xp
    Post-modeling validation plots + variance
    100 xp
    Kruskal-Wallis rank sum test
    100 xp
    A/B testing
    50 xp
    Which post-A/B test test?
    50 xp
    Sample size for A/B test
    100 xp
    Basic A/B test
    100 xp
    A/B tests vs. multivariable experiments
    100 xp
  3. 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.

    Play Chapter Now
    Intro to NHANES and sampling
    50 xp
    NHANES dataset construction
    100 xp
    NHANES EDA
    100 xp
    NHANES Data Cleaning
    100 xp
    Resampling NHANES data
    100 xp
    Randomized Complete Block Designs (RCBD)
    50 xp
    Which is NOT a good blocking factor?
    50 xp
    Drawing RCBDs with Agricolae
    100 xp
    NHANES RCBD
    100 xp
    RCBD Model Validation
    100 xp
    Balanced Incomplete Block Designs (BIBD)
    50 xp
    Is a BIBD even possible?
    50 xp
    Drawing BIBDs with agricolae
    100 xp
    BIBD - cat's kidney function
    100 xp
    NHANES BIBD
    100 xp
  4. 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.

    Play Chapter Now
    Latin squares
    50 xp
    NYC SAT Scores EDA
    100 xp
    Dealing with Missing Test Scores
    100 xp
    Drawing Latin Squares with agricolae
    100 xp
    Latin Square with NYC SAT Scores
    100 xp
    Graeco-Latin squares
    50 xp
    NYC SAT Scores Data Viz
    100 xp
    Drawing Graeco-Latin Squares with agricolae
    100 xp
    Graeco-Latin Square with NYC SAT Scores
    100 xp
    Factorial experiments
    50 xp
    NYC SAT Scores Factorial EDA
    100 xp
    Factorial Experiment with NYC SAT Scores
    100 xp
    Evaluating the NYC SAT Scores Factorial Model
    100 xp
    What's next in experimental design?
    50 xp

In the following tracks

Data Scientist with RData Scientist Professional with RStatistician with R

Datasets

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

Collaborators

Collaborator's avatar
Becca Robins
Collaborator's avatar
Richie Cotton

Prerequisites

Hypothesis Testing in R
Joanne Xiong HeadshotJoanne Xiong

Data Literacy & Essentials Content Developer

Joanne is a Data Literacy & Essentials Content Developer at DataCamp. She holds a Master’s degree in Statistical Science from the University of Oxford. Her passion is applying statistics and data science to a broad range of fields, ranging from clinical statistics to quantitative psychology.
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