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More than just fitting a linear line through a cloud of data points is involved in linear regression analysis. It has three stages: (1) examining the data for correlation and directionality, (2) estimating the model, i.e. fitting the line, and (3) evaluating the model's validity and utility.

A linear regression model is predicated on four assumptions: Linearity refers to the relationship between X and the mean of Y. Homoscedasticity: For every value of X, the variance of the residual is the same. Independent observations: Observations are not reliant on one another.

y = b0 + b1*x + e is the linear regression mathematical formula, where b0 and b1 are known as the regression beta coefficients or parameters: The anticipated value when x = 0 is b0, which is the intercept of the regression line. The slope of the regression line is b1.

Linear Regression is a supervised machine learning technique with a continuous and constant slope projected output. Rather than aiming to classify data into categories, it's used to predict values within a continuous range (e.g. sales, price) (e.g. cat, dog).

These are some considerations to make when selecting a linear model:

  • Comparing linear models for the same dataset is the only way to go.
  • Look for a model with a high R2 adjusted.
  • Ascertain that the residuals in this model are evenly distributed around zero.
  • Ascertain that the model's faults are contained within a narrow bandwidth.

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