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OLS Assumptions are the Conditions that we need to consider them before performing Regression Analysis.
Some OLS Assumptions are:
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To detect autocorrelation
There is no remedy for Autocorrelation. Instead of linear regression, we can use
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Ordinary least squares, or linear least squares, estimates the parameters in a regression model by minimizing the sum of the squared residuals.
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In order to prevent Heteroscedasticity, we need to
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If the pattern doesn't looks like a Straight Line, then we need to apply
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To fix Multicollinearity:
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These are some of the Assumptions to be pondered while Applying Ordinary Least Square Method and Performing Regression Analysis.
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Not all assumptions need to be tested. To identify what is more critical at this moment, we can prioritise them on a quadrant, in which horizontally we have scale of evidence (how much evidence we have for it to be true) and vertically importance of the assumption (how critical it is to happen so...
Consider on what basis you can justify any claims. You may rely on external evidence, personal preferences or experiences. All of them will at some stage point to our assumptions we accept as fundamental.
Our assumptions are unexamined ideas. They are vital as they underpi...
Just because we know and understand the concept of conjunctive events bias, we are not automatically immune to it.
When we are planning, it is useful to run through our assumptions with this bias in mind. We should be more pessimistic about our plans and consider the worst-case s...
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