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The total squared error between the predicted y values and the actual y values

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How do you calculate regression equation?

what is the equation of the regression line for the given data(Age, Number of Accidents) (16, 6605), (17, 8932), (18, 8506), (19, 7349), (20, 6458), (21, 5974)


The random error in a regression equation?

includes both positive and negative terms.


Regression analysis is a statistical procedure for developing a mathematical equation that describes how?

one dependent and one or more independent variables are related.


How do you plot linear regression line?

Linear regression looks at the relationship between two variables, X and Y. The regression line is the "best" line though some data you that you or someone else has collected. You want to find the best slope and the best y intercept to be able to plot the line that will allow you to predict Y given a value of X. This is usually done by minimizing the sum of the squares. Regression Equation is y = a + bx Slope(b) = (NΣXY - (ΣX)(ΣY)) / (NΣX2 - (ΣX)2) Intercept(a) = (ΣY - b(ΣX)) / N In the equation above: X and Y are the variables given as an ordered pair (X,Y) b = The slope of the regression line a = The intercept point of the regression line and the y axis. N = Number of values or elements X = First Score Y = Second Score ΣXY = Sum of the product of first and Second Scores ΣX = Sum of First Scores ΣY = Sum of Second Scores ΣX2 = Sum of square First Scores Once you find the slope and the intercept, you plot it the same way you plot any other line!


How do you plot regression line?

Linear regression looks at the relationship between two variables, X and Y. The regression line is the "best" line though some data you that you or someone else has collected. You want to find the best slope and the best y intercept to be able to plot the line that will allow you to predict Y given a value of X. This is usually done by minimizing the sum of the squares. Regression Equation is y = a + bx Slope(b) = (NΣXY - (ΣX)(ΣY)) / (NΣX2 - (ΣX)2) Intercept(a) = (ΣY - b(ΣX)) / N In the equation above: X and Y are the variables given as an ordered pair (X,Y) b = The slope of the regression line a = The intercept point of the regression line and the y axis. N = Number of values or elements X = First Score Y = Second Score ΣXY = Sum of the product of first and Second Scores ΣX = Sum of First Scores ΣY = Sum of Second Scores ΣX2 = Sum of square First Scores Once you find the slope and the intercept, you plot it the same way you plot any other line!

Related Questions

What is the adjective of the word regression?

of, pertaining to, or determined by regression analysis: regression curve; regression equation. dictionary.com


What is the only condition under which (x y) regression equation solved for x gives the same predictions as the (y x) regression equation?

If the regression is a perfect fit.


What is regression coefficient and correlation coefficient?

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.


Given a linear regression equation of equals 20 - 1.5x where will the point 3 15.5 fall with respect to the regression line?

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


What does a regression equation look like?

It can look like any algebraic equation.


Is there any relation between regression and linear equation?

Yes.


Explain the concept of correlation and regression line as a forecasting tool?

once an equation for a regression is derived it can be used to predict possible future


What is the process for finding the least mean square fit in a regression analysis?

In regression analysis, the process for finding the least mean square fit involves minimizing the sum of the squared differences between the observed values and the values predicted by the regression model. This is typically done using mathematical techniques such as the method of least squares, which calculates the coefficients that best fit the data by minimizing the sum of the squared residuals.


Is the random error in a regression equation the predicted error?

pig benis


How do you calculate regression equation?

what is the equation of the regression line for the given data(Age, Number of Accidents) (16, 6605), (17, 8932), (18, 8506), (19, 7349), (20, 6458), (21, 5974)


The random error in a regression equation?

includes both positive and negative terms.


The value 11.7 represents the of the graph of the following linear regression equation?

slope