Simple linear regression model. Geometrical representation
Key Takeaways
The video explains the simple linear regression equation and its geometrical representation, covering the regression line, intercept, slope, and residuals.
Full Transcript
Let's check out the simple linear regression equation once again. Here it is. You have probably heard of the regression line, right? When we plot the data points on an XY plane, the regression line is the best fitting line through the data points. Okay, here's a plot with some data points. We plot the line based on the regression equation. The gray points that are scattered are the observed values. B 0 as we said earlier is a constant and is the intercept of the regression line with the yaxis. B1 is the slope of the regression line. It shows how much y changes for each unit change of x. The distance between the observed values and the regression line is the estimator of the error term epsilon. Its point estimate is called residual. Now if you draw a perpendicular from an observed point to the regression line, the intercept between that perpendicular and the regression line is a point with a y value equal to y hat. As we said earlier, given an x, yhat is the value predicted by the regression line. For more videos like this one, please subscribe.
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Let's check out the simple linear regression equation once again. You have probably heard of the regression line. When we plot the data points on an XY plane the regression line is the best fitting line through the data points.
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