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What else can I help you with?
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).
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 F-ratio?
The F-ratio is a statistical ratio which arises as the ratio of two chi-square distributions.If X and Y are two random variables which are independent and approximately normally distributed, then their variances have chi-squared distributions. The ration of these chi-square distributions, appropriately scaled, is called the F-ratio.The F-ratio is used extensively in analysis of variance to determine what proportion of the variation in the dependent variable is explained by an explanatory variable (and the model being tested).
How much is two fifths in muntues?
It would help if you explained what "MUNTUES" were.
Similarities of the geocentric and heliocentric theory?
They both explained which body is in the center of the solar system
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.
What is a measure of the amount of variation in the observed values of the response variable explained by the regression?
The measure of the amount of variation in the observed values of the response variable explained by the regression is known as the coefficient of determination, denoted as ( R^2 ). This statistic quantifies the proportion of the total variability in the response variable that can be attributed to the predictor variables in the model. An ( R^2 ) value closer to 1 indicates a better fit, meaning that a larger proportion of the variance is explained by the regression model. Conversely, an ( R^2 ) value near 0 suggests that the model does not explain much of the variation.
What is the role of the stochastic error term in regression analysis?
Regression analysis is based on the assumption that the dependent variable is distributed according some function of the independent variables together with independent identically distributed random errors. If the error terms were not stochastic then some of the properties of the regression analysis are not valid.
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).
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.
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 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 a difference between F statistics F distribution?
An F-statistic is a measure that is calculated from a sample. It is a ratio of two lots of sums of squares of Normal variates. The sampling distribution of this ratio follows the F distribution. The F-statistic is used to test whether the variances of two samples, or a sample and population, are the same. It is also used in the analysis of variance (ANOVA) to determine what proportion of the variance can be "explained" by regression.
What was John Dalton contributes in atomic theory?
It explained law of conservation of mass and law of constant proportion and laid foundation to atomic physics n chemistry...
What is a perfect correlation?
If all variations in the dependent variable can be fully explained by the independent variables - so that there is no residual "error" - the correlation is said to be perfect.
How do you calculate r2?
R², or the coefficient of determination, is calculated by taking the ratio of the variance explained by the regression model to the total variance in the dependent variable. It is computed as ( R^2 = 1 - \frac{SS_{res}}{SS_{tot}} ), where ( SS_{res} ) is the sum of squares of residuals (the differences between observed and predicted values) and ( SS_{tot} ) is the total sum of squares (the differences between observed values and their mean). A higher R² value indicates a better fit of the model to the data.
