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If the regression sum of squares is large relative to the error sum of squares is the regression equation useful for making predictions?
Wiki User
∙ 14y ago
If the regression sum of squares is the explained sum of squares. That is, the sum of squares generated by the regression line. Then you would want the regression sum of squares to be as big as possible since, then the regression line would explain the dispersion of the data well.
Alternatively, use the R^2 ratio, which is the ratio of the explained sum of squares to the total sum of squares. (which ranges from 0 to 1) and hence a large number (0.9) would be preferred to (0.2).
Wiki User
∙ 14y ago
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What is quantile regression?
Quantile regression is considered a natural extension of ordinary least squares. Instead of estimating the mean of the regressand for a given set of regressors, and instead of minimizing sum of squares, it estimates different values of the regressand across its distribution, and minimizes instead the absolute distances between observations.
What negative correlation indicate?
the negative sign on correlation just means that the slope of the Least Squares Regression Line is negative.
Write a multiplication equation that compares 12 squares to 4 triangles?
4x12
What shape can 4 rectangles and 2 squares make?
The answer depends on their relative dimensions.
How many squares are in a standard checkerboard?
64 squares. EDIT There are 64 1x1 squares on a standard checkerboard, but there are also squares of other sizes. There are; 64 1x1 squares 49 2x2 squares 36 3x3 squares 25 4x4 squares 16 5x5 squares 9 6x6 squares 4 7x7 squares 1 8x8 square So in total there are 204 squares on a standard checkerboard.
What characteristic makes regression line of best fit?
The equation of the regression line is calculated so as to minimise the sum of the squares of the vertical distances between the observations and the line. The regression line represents the relationship between the variables if (and only if) that relationship is linear. The equation of this line ensures that the overall discrepancy between the actual observations and the predictions from the regression are minimised and, in that respect, the line is the best that can be fitted to the data set. Other criteria for measuring the overall discrepancy will result in different lines of best fit.
What should you use to find the equation for a line of fit for a scatter plot?
Least squares regression is one of several statistical techniques that could be applied.
Weighted Least Squares regression?
Yes, it does exist.
What does f statistic mean?
The F-statistic is a test on ratio of the sum of squares regression and the sum of squares error (divided by their degrees of freedom). If this ratio is large, then the regression dominates and the model fits well. If it is small, the regression model is poorly fitting.
What does a large F-statistic mean?
The F-statistic is a test on ratio of the sum of squares regression and the sum of squares error (divided by their degrees of freedom). If this ratio is large, then the regression dominates and the model fits well. If it is small, the regression model is poorly fitting.
What is another name for the regression line?
It is often called the "Least Squares" line.
What is the variation attributable to factors other than the relationship between the independent variables and the explained variable in a regression analysis is represented by?
Regression mean squares
What has the author Naihua Duan written?
Naihua Duan has written: 'The adjoint projection pursuit regression' -- subject(s): Least squares, Regression analysis
Is the least-squares regression line resistant?
No, it is not resistant.It can be pulled toward influential points.
What is the equation for quadrilaterals?
An equation for the sum of squares of side lengths is:
Why are there two regression lines?
There are two regression lines if there are two variables - one line for the regression of the first variable on the second and another line for the regression of the second variable on the first. If there are n variables you can have n*(n-1) regression lines. With the least squares method, the first of two line focuses on the vertical distance between the points and the regression line whereas the second focuses on the horizontal distances.
What is F variate?
The F-variate, named after the statistician Ronald Fisher, crops up in statistics in the analysis of variance (amongst other things). Suppose you have a bivariate normal distribution. You calculate the sums of squares of the dependent variable that can be explained by regression and a residual sum of squares. Under the null hypothesis that there is no linear regression between the two variables (of the bivariate distribution), the ratio of the regression sum of squares divided by the residual sum of squares is distributed as an F-variate. There is a lot more to it, but not something that is easy to explain in this manner - particularly when I do not know your knowledge level.
