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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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To detect autocorrelation
There is no remedy for Autocorrelation. Instead of linear regression, we can use
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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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OLS Assumptions are the Conditions that we need to consider them before performing Regression Analysis.
Some OLS Assumptions are:
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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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Analyse if the KPIs and the business objective of the model are achieved. If the parameters are not met, consider changing the model or improving the quality and quantity of the data.
Before deployment:
Polynomial regression is a unique case of linear regression:
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