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Md. Saif Kabir Asif has completed

# Correlation and Regression in R

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4 hours
4,200 XP

## 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.

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.

4. 4

### Interpreting regression models

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

5. 5

### Model Fit

In this final chapter, you'll learn how to assess the "fit" of a simple linear regression model.

Collaborators

Prerequisites

Exploratory Data Analysis in R
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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