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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!
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In the following Tracks

Certification Available

Associate Data Scientist in R

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Statistician with R

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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.

    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
For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more

In the following Tracks

Certification Available

Associate Data Scientist in R

Go To Track

Statistician with R

Go To Track

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
Joanne Xiong HeadshotJoanne 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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