Multicollinearity refers to a situation in which two or more explanatory variables in a multiple regression model are highly linearly related and can be linearly predicted from the others with a substantial degree of accuracy.
So, we look for variables that are correlated with each other.
Yes there are correlations that suggest the problem of multicollinearity.
Examples:
All these three correlations are significant at 0.01
Now what happens is there is some amount of variability explained in no. of bedrooms by no. of bathrooms(fraction) and then there is some varaibility explained by square ft of finished living space. The problem is since they both are correlated they explain variability in each other too. If we consider both to study variation in no. of bedrooms then we end up counting the same variation twice.
Similarly, another example is :
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Please help in solving this. Explain whether there are any correlations that suggest the problem of...
Explain whether there are any correlations that suggest the problem of multicollinearity. Why or why not? Use at least two examples from your correlation matrix as part of your explanation. Correlations Number of bedrooms Number of bathrooms (fractions) Number of floors Building Condition Year built Building Grade .376 Number of bedrooms Pearson Correlation 1 551" Lot size (square feet) .132" .000 796 Square feet of finished living space .637" .000 796 .711" 278 .049 233 .000 .000 .000 .000 796...
Explain whether there are any correlations that suggest the problem of multicollinearity. Why or why not? Use at least two examples from your correlation matrix as part of your explanation. Correlations Number of bedrooms Number of bathrooms (fractions) Number of floors Building Condition Year built Building Grade .376 Number of bedrooms Pearson Correlation 1 551" Lot size (square feet) .132" .000 796 Square feet of finished living space .637" .000 796 .711" 278 .049 233 .000 .000 .000 .000 796...
Explain the meanings of this Pearsons correlation Correlations Square Foota ge ListPrice N Square Footage Pearson Correlation 1 .841* Sig. (2-tailed) .000 245 245 ListPrice Pearson Correlation Sig. (2-tailed) .000 245 245 **. Correlation is significant at the 0.01 level (2-tailed). .841**
CAN BE DONE IN ANY LANGUAGE UID Values will be from 000 to 100: 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066...
WERE depression /METHOD-ENTER daddrink momdrink DIF. Regression 214 Descriptive Statistics Mean Std. Deviation 4.0514 4.13188 2.5234 3.09428 9673 1.73445 15.2103 6.02870 depression total fsmast momdrink DIF 214 214 214 Pearson Correlation depression total fsmast momdrink -104 .011 1.000 038 momdrink DIF 471 .111 038 1.000 .000 .053 291 DIF Sig. (1-tailed) 064 Correlations depression total fsmast 1.000 227 227 1.000 104 .011 -471 .111 .000 .000 .064 436 .000 .053 214 214 214 214 214 214 214 depression total fsmast...