Correlation and Regression in R

Learn how to describe relationships between two numerical quantities and characterize these relationships graphically.
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Clock4 HoursPlay18 VideosCode58 ExercisesGroup65,020 Learners
Database4200 XP

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Course Description

Ultimately, data analysis is about understanding relationships among variables. Exploring data with multiple variables requires new, more complex tools, but enables a richer set of comparisons. In this course, you will learn how to describe relationships between two numerical quantities. You will characterize these relationships graphically, in the form of summary statistics, and through simple linear regression models.

  1. 1

    Visualizing two variables

    Free
    In this chapter, you will learn techniques for exploring bivariate relationships.
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  2. 2

    Correlation

    This chapter introduces correlation as a means of quantifying bivariate relationships.
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  3. 3

    Simple linear regression

    With the notion of correlation under your belt, we'll now turn our attention to simple linear models in this chapter.
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  4. 4

    Interpreting regression models

    This chapter looks at how to interpret the coefficients in a regression model.
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  5. 5

    Model Fit

    In this final chapter, you'll learn how to assess the "fit" of a simple linear regression model.
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In the following tracks
Data Analyst Data ScientistStatisticianStatistics Fundamentals
Collaborators
Nick CarchediTom Jeon
Ben Baumer Headshot

Ben Baumer

Assistant Professor at Smith College
Ben is an Assistant Professor in the Statistical & Data Sciences Program at Smith College. He completed his Ph.D. in Mathematics at the Graduate Center of the City University of New York in 2012. He is an Accredited Professional Statistician™ by the American Statistical Association and was previously the Statistical Analyst for the Baseball Operations department of the New York Mets.
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