Page 71 - Statistics II for Dummies

Chapter 4

Getting in Line with Simple

Linear Regression

In This Chapter
▶ Using scatterplots and correlation coefficients to examine relationships
▶ Building a simple linear regression model to estimate y from x
▶ Testing how well the model fits
▶ Interpreting the results and making good predictions

ooking for relationships and making predictions is one of the staples of
Ldata analysis. Everyone wants to answer questions like, “Can I predict
how many units I’ll sell if I spend x amount of advertising dollars?”; or “Does
drinking more diet cola really relate to more weight gain?”; or “Do children’s
backpacks seem to get heavier with each year of school, or is it just me?”

Linear regression tries to find relationships between two or more variables
and comes up with a model that tries to describe that relationship, much
like the way the line y = 2x + 3 explains the relationship between x and y.
But unlike in math, where functions like y = 2x + 3 tell the entire story about
the two variables, in statistics things don’t come out that perfectly; some
variability and error is involved (that’s what makes it fun!).

This chapter is partly a review of the concepts of simple linear regression
presented in a typical Stats I textbook. But the fun doesn’t stop there. I
expand on the ideas about regression that you picked up in your Stats I
course and set you up for some of the other types of regression models you
see in Chapters 5 through 8.

In this chapter, you see how to build a simple linear regression model that
examines the relationship between two variables. You also see how simple
linear regression works from a model-building standpoint.

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