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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.
Visualizing two variablesFree
In this chapter, you will learn techniques for exploring bivariate relationships.
This chapter introduces correlation as a means of quantifying bivariate relationships.Quantifying the strength of bivariate relationships50 xpUnderstanding correlation scale50 xpUnderstanding correlation sign50 xpComputing correlation100 xpThe Anscombe dataset50 xpExploring Anscombe100 xpPerception of correlation50 xpPerception of correlation (2)100 xpInterpretation of Correlation50 xpInterpreting correlation in context50 xpCorrelation and causation50 xpSpurious correlations50 xpSpurious correlation in random data100 xp
Simple linear regression
With the notion of correlation under your belt, we'll now turn our attention to simple linear models in this chapter.Visualization of Linear Models50 xpThe "best fit" line100 xpUniqueness of least squares regression line100 xpUnderstanding Linear Models50 xpRegression model terminology50 xpRegression model output terminology50 xpFitting a linear model "by hand"100 xpRegression vs. regression to the mean50 xpRegression to the mean100 xp"Regression" in the parlance of our time50 xp
Interpreting regression models
This chapter looks at how to interpret the coefficients in a regression model.Interpretation of Regression50 xpInterpretation of coefficients50 xpInterpretation in context50 xpFitting simple linear models100 xpUnits and scale50 xpYour linear model object50 xpThe lm summary output100 xpFitted values and residuals100 xpTidying your linear model100 xpUsing your linear model50 xpMaking predictions100 xpAdding a regression line to a plot manually100 xp
In this final chapter, you'll learn how to assess the "fit" of a simple linear regression model.Assessing Model Fit50 xpRMSE50 xpStandard error of residuals100 xpComparing model fits50 xpAssessing simple linear model fit100 xpInterpretation of R^250 xpLinear vs. average100 xpUnusual Points50 xpLeverage100 xpInfluence100 xpDealing with Outliers50 xpRemoving outliers100 xpHigh leverage points100 xpConclusion50 xp
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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