Q: What does the y-intercept represent in a linear regression equation?

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on the lineGiven a linear regression equation of = 20 - 1.5x, where will the point (3, 15) fall with respect to the regression line?Below the line

Yes.

The strength of the linear relationship between the two variables in the regression equation is the correlation coefficient, r, and is always a value between -1 and 1, inclusive. The regression coefficient is the slope of the line of the regression equation.

A linear regression

A linear equation is an equation that in math. It is a line. Liner equations have no X2. An example of a linear equation is x-2 A linear equation also equals y=mx+b. It has a slope and a y-intercept. A non-linear equation is also an equation in math. It can have and x2 and it is not a line. An example is y=x2+3x+4 Non linear equations can be quadratics, absolute value or expodentail equations.

Related questions

on the lineGiven a linear regression equation of = 20 - 1.5x, where will the point (3, 15) fall with respect to the regression line?Below the line

Yes.

slope

The strength of the linear relationship between the two variables in the regression equation is the correlation coefficient, r, and is always a value between -1 and 1, inclusive. The regression coefficient is the slope of the line of the regression equation.

A linear regression

They are used in statistics to predict things all the time. It is called linear regression.

A linear equation is an equation that in math. It is a line. Liner equations have no X2. An example of a linear equation is x-2 A linear equation also equals y=mx+b. It has a slope and a y-intercept. A non-linear equation is also an equation in math. It can have and x2 and it is not a line. An example is y=x2+3x+4 Non linear equations can be quadratics, absolute value or expodentail equations.

A straight line.

Regression :The average Linear or Non linear relationship between Variables.

No. It is an estimated equation that defines the best linear relationship between two variables (or their transforms). If the two variables, x and y were the coordinates of a circle, for example, any method for calculating the regression equation would fail hopelessly.

Linear regression can be used in statistics in order to create a model out a dependable scalar value and an explanatory variable. Linear regression has applications in finance, economics and environmental science.

It represents all solutions to the linear equation.