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Chapter 11: Linear Regression and Chi-Squared

Difficulty Level: At Grade Created by: CK-12

In this chapter, you will learn how to use observed data to predict the value of an observation that you do not have. One way to do this is via linear regression, the process of calculating a line that represents the best average rate of change of points in a scatter plot.

You will learn to evaluate a set of data to see if it supports a hypothesized distribution, and you will also learn to create contingency tables to organize information to compare variables and see if they are related. The chi-squared (χ2) statistic is a value you will learn to use for this purpose.

Linear regression of a data set

Chapter Outline

Chapter Summary

Students were introduced to the concept of linear regression and Pearson’s correlation coefficient. Students learned to calculate a line of best fit using the least squares method.

After a review of the creation of contingency tables, students practiced extracting data from them to calculate the Chi-Squared statistic. Finally the students learned to use chi-square statistic to run chi-square tests of goodness of fit and variable independence.

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Difficulty Level:

At Grade

Date Created:

Jun 21, 2013

Last Modified:

Jan 26, 2015
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