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It doesn't have much impact on the reliability of the model, but adds to the noise with unnecessary overfitting. Multicollinearity impacts on your assessment of which factors are really influential. Factors that are redundant should be dropped in good model design. For example, you could come up with a fairly good linear model predicting fuel economy that includes engine capacity and engine weight. But since capacity and weight are correlated one is redundant and should be dropped.
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What is the difference between Multicollinearity and Autocorrelation?
The difference between multicollinearity and auto correlation is that multicollinearity is a linear relationship between 2 or more explanatory variables in a multiple regression while while auto-correlation is a type of correlation between values of a process at different points in time, as a function of the two times or of the time difference.
What is a linear model?
A model in which your mother.
What is linear mathematical model?
Calculus
Can A correlation matrix can be used to assess multicollinearity between independent variables?
yes
How can you model data with a linear equation?
by figuring out the equation
