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Author Question: Why is it important to consider all the independent variables together in one model, rather than in ... (Read 29 times)

tichca

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on: Jun 24, 2018
Why is it important to consider all the independent variables together in one model, rather than in separate simple linear regression models?
 
  What will be an ideal response?

Question 2

A finite population correction factor should be used to adjust the margin of error when the sample taken is larger than ________ of the entire population.
 
  A) 10
  B) 1
  C) 5
  D) 50
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jlaineee

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Reply #1 on: Jun 24, 2018
Answer to Question 1

If an important explanatory variable is not included in the regression model, any conclusions drawn about the effects of other independent variables can be seriously misleading. In particular, adding a relevant variable could well alter the impression of a significant negative association to the conclusion of significant positive association. Further insight can be gained from casual inspection of the data. The only legitimate way to disentangle the separate effects of two or more independent variables on the dependent variable is to model them jointly in a regression equation. Multiple regression models are more accurate than simple linear regression equations when there is more than one relevant independent variable.

Answer to Question 2

C


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