In multiple regression,:
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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
Transcribed Image Text:• 1. The formula for multiple regression standard error of the estimate is
identical to that of the SLR standard error of the estimate, except that
the denominator in the multiple regression standard error of the
estimate contains an additional numeric, k, that counts the number of
explanatory variables in the multiple regression equation.
• 2. In multiple regression, as in simple linear regression, we use ANOVA
to test individual partial regression coefficients for their significance in
explaining the data.
• 3. Multicollinearity is a condition where two or more of the independent
variables in the data are correlated. One of the ways that this can show
up in data analysis is in a correlation matrix.
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