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Erika Schiffer has completed

Intro to Statistics with R: Correlation and Linear Regression

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4 hr
2,350 XP
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Course Description

If you have ever taken a math or statistics class, you’ve probably heard the old adage "Correlation does not imply causation". The first part of this course explores this further, and will offer a broad overview on correlational analysis. In the second part you will leave descriptive statistics behind, and dive into regression, prediction and inferential statistics.
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  1. 1

    An introduction to Correlation Coefficients

    Free

    In the first chapter you will be given a broad overview on the concepts behind correlation as well as some examples. Furthermore, you will walk through the mathematical calculation of the correlation coefficient r, that is the Pearson product-moment correlation coefficient. Finally, there will be a part on the assumptions underlying a typical correlational analysis.

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    How are correlation coefficients calculated?
    50 xp
    Manual computation of correlation coefficients (1)
    100 xp
    Manual computation of correlation coefficients (2)
    100 xp
    Manual computation of correlation coefficients (3)
    100 xp
    The usefulness of correlation coefficients
    50 xp
    Creating scatterplots
    100 xp
    Correlation matrix
    100 xp
    Analysis scatterplots
    50 xp
    Get intuitive! #1
    50 xp
    Points of caution
    50 xp
    Non-representative data samples
    100 xp
    Get intuitive! #2
    50 xp
  2. 2

    An introduction to Linear Regression

    Free

    In this first chapter on linear regression, professor Conway will give you an overview on regression; What does it do? What is it used for? You will see how to build and execute a regression model in R, and what the effect is of adding additional regressors.

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    Introduction to regression
    50 xp
    Impact experiment
    100 xp
    Regression equations and the R-squared value
    50 xp
    Manual computation of a simple linear regression
    100 xp
    Executing a simple linear regression using R
    100 xp
    Coefficient of impulse control
    50 xp
    Correlation coefficient
    50 xp
    Multiple linear regression
    50 xp
    Executing a multiple regression in R
    100 xp
    Effect of adding an extra regressor
    50 xp
  3. 3

    Linear Regression Models continued

    Free

    In chapter three you will do the calculation of the regression coefficients yourself in R. Next, there will be a detailed study of the assumptions underlying a linear regression analysis. The end is reserved for Anscombe’s quartet, a famous statistical example that shows you the importance of graphing data before analyzing it.

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    Estimation of coefficients
    50 xp
    Calculating the sum of squared residuals
    100 xp
    Which model?
    50 xp
    Estimation of unstandardized and standardized regression coefficients
    50 xp
    Regression coefficient
    50 xp
    Standardized linear regression
    100 xp
    Assumptions of linear regression
    50 xp
    Assumptions?
    50 xp
    Distribution of Y
    50 xp
    Anscombe's quartet
    50 xp
    Plotting residuals
    100 xp
    Assumption of homoscedasticity?
    50 xp
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