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Does the coefficient of determination represents the percent of variation in y that is not explained by variation in x using the regression line?
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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.
How can pi be explained?
3.14159265359, an infinite number
What is the explained equation for 9317 divided by 95?
98.0737
How much variance has been explained by a correlation of .9?
81%
How much variance has been explained by a Pearson correlation of 0.9?
81
What measures the percentage of total variation in the response variable that is explained by the least squares regression line?
The coefficient of determination, also known as R-squared, measures the proportion of the variance in the dependent variable that is predictable from the independent variable(s) in a regression model. It ranges from 0 to 1, with higher values indicating a better fit of the model to the data.
The percent variance in Y explained by variability in X is called the?
r2, the coefficient of determination
What is the difference between the coefficient of simple determination and that of multiple determination?
The coefficient of simple determination tells the proportion of variance in one variable that can be accounted for (or explained) by variance in another variable. The coefficient of multiple determination is the Proportion of variance X and Y share with Z; or proportion of variance in Z that can be explained by X & Y.
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
If the regression sum of squares is large relative to the error sum of squares is the regression equation useful for making predictions?
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).
The proportion of the variation in the dependent variable y that is explained by the estimated regression equation is measured by the?
confidence interval estimate
What is an idea or object that represents something that is being explained?
model
How can you tell if it appears to have a good r-square?
r^2 , the square of the correlation coefficient represents the percentage of variation explained by the independent variable of the dependent variable. It varies between 0 and 100 percent. The user has to make his/her own judgment as to whether the obtained value of r^2 is good enough for him/her.
What is an r squared value?
R squared also called the coefficient of determination is the portion (%) of the total variation of the dependent variable that is explained by the variation in the independent variable. This is found by dividing the sum of squared regression (SSR) by the total sum of square errors (SST) that is R^2 = SSR / SST.When there is a perfect linear relationship between the variation of the dependent variable y and the variation of the independent variable x R^2 is equal to 1.The R^2 for any weaker linear relationships will range between 0 and 1 exclusive.Finally when there is no relationship between the variations of the y as a result of the variation in x R^2 is equal to 0.
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.
What is stochastic error term?
A Stochastic error term is a term that is added to a regression equation to introduce all of the variation in Y that cannot be explained by the included Xs. It is, in effect, a symbol of the econometrician's ignorance or inability to model all the movements of the dependent variable.
How do you come up with a logo?
Typically, you choose something that represents your business and doesn't need to be explained. It should be simple and recognizable. A symbol or a letter, perhaps. Use your imagination.
