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In section 13.4 of the textbook for this module, the assumptions of linear regression are discussed. They include:

Linearity: X and Y have a linear relationship.
Homoscedasticity: The errors are constant.
Independence: The X variables are independent of each other.
Normality: Y is normally distributed.
Based on the discussion regarding these assumptions in this textbook section as well as your own additional research, discuss the potential impact of assumption violations on the interpretation of regression results. In other words, what happens to the regression output and the value of the interpretation if one of the assumptions does not hold? Choose ONE assumption and discuss how the violation of this ONE assumption impacts the validity of the regression results.

Keep in mind: you should put the discussion into your own words. Do not just copy and paste technical information from the textbook or another source.

Really strive to understand what the assumption means and how the violation of it impacts the output from a practical perspective.

The primary resource for this module is Introductory Business Statistics by Alexander, Illowsky, and Dean.

Alexander, H., Illowsky, B., & Dean, S. (2017). Introductory Business Statistics. Openstax. Retrieved from

For Module 3, you should read through the following material in this textbook.

Chapter 13: Linear Regression and Correlation
Sections 13.1, 13.2, 13.3 and 13.7 only
This chapter introduces correlation coefficients and linear regression analysis. Section 13.7 explains how to create regression estimates in Excel. There is also tutorial link below that explains how to use that tool. We will cover the remaining sections in Module 4.

You are now familiar with several tools in the Analysis Toolpak. Regression analysis is just another one of those tools. Please review the following tutorial for help in generating regression estimates in Excel:

Business Statistics
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