Want this question answered?

Q: How do you create a residual plot with a linear regression equation and data?

Write your answer...

Submit

Still have questions?

Continue Learning about Math & Arithmetic

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.

+ Linear regression is a simple statistical process and so is easy to carry out. + Some non-linear relationships can be converted to linear relationships using simple transformations. - The error structure may not be suitable for regression (independent, identically distributed). - The regression model used may not be appropriate or an important variable may have been omitted. - The residual error may be too large.

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.

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.

slope

One of the main reasons for doing so is to check that the assumptions of the errors being independent and identically distributed is true. If that is not the case then the simple linear regression is not an appropriate model.

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.

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

+ Linear regression is a simple statistical process and so is easy to carry out. + Some non-linear relationships can be converted to linear relationships using simple transformations. - The error structure may not be suitable for regression (independent, identically distributed). - The regression model used may not be appropriate or an important variable may have been omitted. - The residual error may be too large.

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.

It represents the value of the y variable when the x variable is zero.

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.