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 NES Mathematics (304): Practice & Study Guide
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Ch 57: NES Math: Regression & Correlation

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About This Chapter

Use our lessons to renew your understanding of formulas and problem solving with regression and correlations as you study for the NES Math exam. Our instructors will guide you through lively videos that offer information on test topics.

NES Math: Regression & Correlation - Chapter Summary

Let the lessons in this chapter aid in your review of regression, residuals, correlations and other related material. Use our snappy videos and other study aids to refresh your understanding of NES Math test topics such as:

  • How to create and interpret scatterplots
  • Simple linear regression
  • Solving problems with linear regression
  • How to analyze residuals
  • Formula for the correlation coefficient
  • Differences in causation and correlation
  • How to transform nonlinear data

These lively lessons, packed with examples, are about five minutes long on average and available anytime on computer or mobile device. After watching, you can assess your learning with the short lesson quizzes and a comprehensive chapter exam. Each video has a clickable Timeline that makes it easy to locate specific parts of the lesson for additional study.

NES Math: Regression & Correlation Chapter Objectives

Passing the NES Math test is one of the required steps for certification to teach math in five states. The test is administered by computer and consists of multiple-choice questions divided into five sections, called content domains. Questions on the material covered in our Regression and Correlation chapter are in the Statistics, Probability and Discrete Mathematics content domain of the NES Math test, which accounts for 19% of the total score.

The lesson quizzes and chapter exam in this study guide are also made up of multiple-choice questions. Completing them will show you the format you'll encounter when you take the NES Math test.

7 Lessons in Chapter 57: NES Math: Regression & Correlation
Chapter Practice Test
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Creating & Interpreting Scatterplots: Process & Examples

1. Creating & Interpreting Scatterplots: Process & Examples

Scatterplots are a great visual representation of two sets of data. In this lesson, you will learn how to interpret bivariate data to create scatterplots and understand the relationship between the two variables.
Simple Linear Regression: Definition, Formula & Examples

2. Simple Linear Regression: Definition, Formula & Examples

Simple linear regression is a great way to make observations and interpret data. In this lesson, you will learn to find the regression line of a set of data using a ruler and a graphing calculator.
Problem Solving Using Linear Regression: Steps & Examples

3. Problem Solving Using Linear Regression: Steps & Examples

Linear regression can be a powerful tool for predicting and interpreting information. Learn to use two common formulas for linear regression in this lesson.
Analyzing Residuals: Process & Examples

4. Analyzing Residuals: Process & Examples

Can you tell what's normal or independent and what's not? Sometimes, we need to figure this out in the world of statistics. This lesson shows you how as it explains residuals and regression assumptions in the context of linear regression analysis.
The Correlation Coefficient: Definition, Formula & Example

5. The Correlation Coefficient: Definition, Formula & Example

The correlation coefficient is an equation that is used to determine the strength of the relationship between two variables. This lesson helps you understand it by breaking the equation down.
Correlation vs. Causation: Differences & Definition

6. Correlation vs. Causation: Differences & Definition

When conducting experiments and analyzing data, many people often confuse the concepts of correlation and causation. In this lesson, you will learn the differences between the two and how to identify one over the other.
Transforming Nonlinear Data: Steps & Examples

7. Transforming Nonlinear Data: Steps & Examples

Sometimes we have data sets that we need to analyze and interpret, but it's difficult because the data is nonlinear. This lesson will teach you how to transform nonlinear data sets into more linear graphs.
Chapter Practice Exam
Chapter Practice Exam
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Practice Final Exam
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