Free course# Intro to Statistics with R: Correlation and Linear Regression

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4 Hours10 Videos34 Exercises28,453 Learners

2350 XPor

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).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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### 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.

How are correlation coefficients calculated?50 xpManual computation of correlation coefficients (1)100 xpManual computation of correlation coefficients (2)100 xpManual computation of correlation coefficients (3)100 xpThe usefulness of correlation coefficients50 xpCreating scatterplots100 xpCorrelation matrix100 xpAnalysis scatterplots50 xpGet intuitive! #150 xpPoints of caution50 xpNon-representative data samples100 xpGet intuitive! #250 xp - 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.

Introduction to regression50 xpImpact experiment100 xpRegression equations and the R-squared value50 xpManual computation of a simple linear regression100 xpExecuting a simple linear regression using R100 xpCoefficient of impulse control50 xpCorrelation coefficient50 xpMultiple linear regression50 xpExecuting a multiple regression in R100 xpEffect of adding an extra regressor50 xp - 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.

Estimation of coefficients50 xpCalculating the sum of squared residuals100 xpWhich model?50 xpEstimation of unstandardized and standardized regression coefficients50 xpRegression coefficient50 xpStandardized linear regression100 xpAssumptions of linear regression50 xpAssumptions?50 xpDistribution of Y50 xpAnscombe's quartet50 xpPlotting residuals100 xpAssumption of homoscedasticity?50 xp

Andrew Conway is a Psychology Professor in the Division of Behavioral and Organizational Sciences at Claremont Graduate University in Claremont, California. He has been teaching introduction to statistics for undergraduate students and advanced statistics for graduate students for 20 years, at a variety of institutions, including the University of South Carolina, the University of Illinois in Chicago, and Princeton University.

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By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA. You confirm you are at least 16 years old (13 if you are an authorized Classrooms user).