Ch 57: NMTA Math: Regression & Correlation

About This Chapter

Watch these quick, engaging videos to explore the regression and correlation concepts you need to know for the NMTA Math assessment. Use the video transcripts for a second investigation of the important ideas, which you'll see emphasized.

NMTA Math: Regression & Correlation - Chapter Summary

As you work through the fun, accessible videos in this chapter, most of them just a few minutes long, you'll examine and practice the regression and correlation concepts you're expected to know for the NMTA Math assessment. Lessons demonstrate:

  • Scatterplot structure and interpretation
  • Processes for simple linear regression
  • Applications of linear regression
  • Techniques for residuals analysis
  • The correlation coefficient
  • The relationship between correlation and causation
  • How to transform nonlinear data

Use the lesson quizzes to solve regression and correlation problems on your own and see how those types of problems might be framed on the exam. Let our instructors know if you have any questions about regression, correlation, or the methods in the chapter.

Objectives of the NMTA Math: Regression & Correlation Chapter

This chapter is designed to help you master regression and correlation topics to help you succeed on the NMTA Math certification assessment. Quick, entertaining videos make study appealing and lesson quizzes let you practice linear regression techniques and analysis of residuals, among other methods.

The NMTA Math exam is designed to judge your abilities in five mathematical domains. Measurement and geometry; math processes and number sense; stats, probability and discrete math; and trig and calculus each account for 19% of your score. Patterns, algebra and functions take up the remainder. You'll be timed as you work through the 150-question exam, and will have to stop at four hours and fifteen minutes whether or not you've completed your assessment. Use the lesson quizzes in this chapter to test your exam pace.

7 Lessons in Chapter 57: NMTA Math: Regression & Correlation
Test your knowledge with a 30-question chapter practice test
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
Test your knowledge of this chapter with a 30 question practice chapter exam.
Not Taken
Practice Final Exam
Test your knowledge of the entire course with a 50 question practice final exam.
Not Taken

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