Regression:
Regression is the statistical method that helps us estimate or predict the unknown value of one variable from the known value of the related variable.
In the words of M.M.Blair:
“Regression is the measure of the average relationship between two or more variables in terms of the original units of the data”.
In the case of linear regression, the regression equations represent straight lines and there is a linear regression between the variables.
The regression equation of Y on X is
The regression equation of X on Y is
The point lies on both the lines of regression. Thus to find we have to solve the above two equations.
are called regression coefficients.
The correlation coefficients
A line that is drawn to pass as close as possible to all the plotted points on a scatter graph is called the line of best fit.
The sign of correlation coefficient is according to the sign of regression coefficients. If both the regression coefficients are positive, the correlation coefficient is positive. If both the regression coefficients are negative, the sign of the correlation coefficient is negative.
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