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Multicollinearity exists in multiple regression

Multicollinearity exists in multiple regression



1) The coefficient of determination (r2) measures the percentage of variation in X (the independent variable) explained by Y (the dependent variable). 

2) Generally a coefficient of determination (r2) of 0.30 or higher passes a goodness of fit test. 


3) Goodness of fit has meaning only if the relationship between the cost drivers and costs is economically plausible.


4) Multicollinearity exists in multiple regression when two or more independent variables are highly correlated with each other. 

5) The t-value of a coefficient measures how large the value of the estimated coefficient is relative to its standard error.


6) The standard error of the estimated coefficient indicates how much the estimated value, b, is likely to be affected by random factors.




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30 Mar 2016

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  1. Genius

    Multicollinearity exists in multiple regression

    Multicollinearity exists in multiple regression Multicoll ****** ******
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