includes both positive and negative terms.
The total squared error between the predicted y values and the actual y values
This is a difficult question to answer. The pure answer is no. In reality, it depends on the level of randomness in the data. If you plot the data, it will give you an idea of the randomness. Even with 10 data points, 1 or 2 outliers can significantly change the regression equation. I am not aware of a rule of thumb on the minimum number of data points. Obviously, the more the better. Also, calculate the correlation coefficient. Be sure to follow the rules of regression. See the following website: http:/www.duke.edu/~rnau/testing.htm
one dependent and one or more independent variables are related.
Least squares regression is one of several statistical techniques that could be applied.
of, pertaining to, or determined by regression analysis: regression curve; regression equation. dictionary.com
If the regression is a perfect fit.
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.
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
It can look like any algebraic equation.
Yes.
once an equation for a regression is derived it can be used to predict possible future
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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.
includes both positive and negative terms.
slope
Yes