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.. _introduction_LOBF:

Introduction
============

In the last section, you learned that correlation in scatter plots measures the
linear relationship between two quantitative variables. The closer the
coefficient of determination (or :math:`R^2` value) is to 1, and the closer the
points of the scatter plot are to a straight line, the more reliable your
predictions will be. But what does this line actually mean and how can it help
you make predictions?

This line is called the **line of best fit**, or a **regression line**. This
line can be used to predict information about values that you may not have data
for. A line of best fit for a scatter plot could look something like the
following.

.. image:: figures/average_sat_score_completion_rate.png
   :align: center
   :alt: Scatter plot with a line of best fit.

In this section, you’ll learn how to create a line of best fit in Sheets, use
the equation of the line of best fit to make predictions, and explain how changes
in one variable may impact the other. Here are some questions a line of best fit
helps to answer.

-  If a school has an average SAT score of 1200, what is its predicted
   completion rate?
-  If two schools have a difference of 100 points in average SAT score, will
   their graduates make different salaries after graduation? If so, by how much?
-  How does the percentage of students receiving federal loans impact completion
   rates?

You will work through some examples throughout this section to find answers to
these questions.