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## Week 4 Participation Post 4 (variant)

by: smartwriter Notetaker

11

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# Week 4 Participation Post 4 (variant)

Marketplace > Week 4 Participation Post 4 variant
smartwriter Notetaker
CSU - Dominguez hills
GPA 3.0

No professor available

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Week 4 Participation Post 4 (variant)
COURSE
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## Popular in Department

This 0 page Study Guide was uploaded by smartwriter Notetaker on Monday November 16, 2015. The Study Guide belongs to a course at a university taught by a professor in Fall. Since its upload, it has received 11 views.

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Date Created: 11/16/15
Great response to this week39s DQ Regression analysis develops an equation to express the linear relationship between two variables Correlation analysis measures the strength of the relationship between two variables Regression analysis is not limited to simple linear regression In the real world it is not uncommon for a given model to have more than two independent variables to predict a numerical dependent variable In such a case we might develop a multiple regression model which is beyond the scope of this course Regression analysis and correlation analysis are two of the most frequently used statistical applications in business scenarios Regression assumptions include linearity independence constant variance and normality Remember there are three assumptions about random errors with respect to least square method 1 The errors are normally distributed 2 The errors have constant variance they are homoscedastic 3 The errors are independent they are nonautocorrelated What is a residual test and why is it performed You can review the Read Me First for this week and I found a good link for you Introduction to regression httpwwwnlregcomintrohm Is there a test to test whether the error is independent or nonautocorrelated read somewhere online about a test called the Durbin Watson test which has something to do with adding up the residuals or something That branches off to another question which is it that tells the residual count Also is the regression equation tted to data supposed to have the lowest residual total count than any other regression equation that can be tted to the data All these things I don39t quite understand

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