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Richard Hussey has completed

Correlation and Regression in R

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4 hours
4,200 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.
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  1. 1

    Visualizing two variables

    Free

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

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    Visualizing bivariate relationships
    50 xp
    Scatterplots
    100 xp
    Boxplots as discretized/conditioned scatterplots
    100 xp
    Characterizing bivariate relationships
    50 xp
    Creating scatterplots
    100 xp
    Characterizing scatterplots
    50 xp
    Transformations
    100 xp
    Outliers
    50 xp
    Identifying outliers
    100 xp
  2. 2

    Correlation

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

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    Quantifying the strength of bivariate relationships
    50 xp
    Understanding correlation scale
    50 xp
    Understanding correlation sign
    50 xp
    Computing correlation
    100 xp
    The Anscombe dataset
    50 xp
    Exploring Anscombe
    100 xp
    Perception of correlation
    50 xp
    Perception of correlation (2)
    100 xp
    Interpretation of Correlation
    50 xp
    Interpreting correlation in context
    50 xp
    Correlation and causation
    50 xp
    Spurious correlations
    50 xp
    Spurious correlation in random data
    100 xp
  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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    Visualization of Linear Models
    50 xp
    The "best fit" line
    100 xp
    Uniqueness of least squares regression line
    100 xp
    Understanding Linear Models
    50 xp
    Regression model terminology
    50 xp
    Regression model output terminology
    50 xp
    Fitting a linear model "by hand"
    100 xp
    Regression vs. regression to the mean
    50 xp
    Regression to the mean
    100 xp
    "Regression" in the parlance of our time
    50 xp
  4. 4

    Interpreting regression models

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

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    Interpretation of Regression
    50 xp
    Interpretation of coefficients
    50 xp
    Interpretation in context
    50 xp
    Fitting simple linear models
    100 xp
    Units and scale
    50 xp
    Your linear model object
    50 xp
    The lm summary output
    100 xp
    Fitted values and residuals
    100 xp
    Tidying your linear model
    100 xp
    Using your linear model
    50 xp
    Making predictions
    100 xp
    Adding a regression line to a plot manually
    100 xp
  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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    Assessing Model Fit
    50 xp
    RMSE
    50 xp
    Standard error of residuals
    100 xp
    Comparing model fits
    50 xp
    Assessing simple linear model fit
    100 xp
    Interpretation of R^2
    50 xp
    Linear vs. average
    100 xp
    Unusual Points
    50 xp
    Leverage
    100 xp
    Influence
    100 xp
    Dealing with Outliers
    50 xp
    Removing outliers
    100 xp
    High leverage points
    100 xp
    Conclusion
    50 xp

Collaborators

Collaborator's avatar
Nick Carchedi
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Tom Jeon

Prerequisites

Exploratory Data Analysis in R
Ben Baumer HeadshotBen 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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