Correlation and Regression
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- 4 hours
- 18 Videos
- 58 Exercises
- 6,992 Participants
- 4200 XP
This course is part of these tracks:
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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Nick Carchedi
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Tom Jeon
Prerequisites
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
Visualizing two variables
FreeIn this chapter, you will learn techniques for exploring bivariate relationships.
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Correlation
This chapter introduces correlation as a means of quantifying bivariate relationships.
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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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Interpreting regression models
This chapter looks at how to interpret the coefficients in a regression model.
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Model Fit
In this final chapter, you'll learn how to assess the "fit" of a simple linear regression model.
