# which of the following is not a valid assumption for performing linear regression

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## Which of the following is not an assumption of the regression model? a. The error terms have constant variance. b. The model is linear. c. The error terms decrease as x values increase. d. The error terms are independent.

Answer to: Which of the following is not an assumption of the regression model? a. The error terms have constant variance. b. The model is linear....

Regression analysis

## Which of the following is not an assumption of the regression model? a. The error terms have...

Which of the following is not an assumption of the regression model? a. The error terms have... Question:

Which of the following is not an assumption of the regression model?

A. The error terms have constant variance.

B. The model is linear.

C. The error terms decrease as x values increase.

D. The error terms are independent.

## Regression Model:

A **regression model** is the result of a regression analysis. It **allows researchers to be able to make predictive statements regarding the variables included in the regression model.**The regression model is always phrased to equal the predicted value of the** y-variable**. The equation itself will include a coefficient associated with the **y-intercept**, along with a coefficient associated with a **slope** value for each independent variable included within the model.

## Answer and Explanation:

The correct answer to the given question is represented by option **C. The error terms decrease as x values increase.**

The other three options listed are all specific requirements that must be met in order for the results of a regression model to be considered valid. The idea that error terms decrease as the x-values increase, would be highly problematic and render the resulting regression model to be unusable. It is therefore, certainly not a requirement of the regression model.

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Regression Analysis: Definition & Examples

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Chapter 21 / Lesson 4

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Regression analysis is used in graph analysis to help make informed predictions on a bunch of data. With examples, explore the definition of regression analysis and the importance of finding the best equation and using outliers when gathering data.

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