Search results
Results from the WOW.Com Content Network
In statistics, explained variation measures the proportion to which a mathematical model accounts for the variation (dispersion) of a given data set. Often, variation is quantified as variance; then, the more specific term explained variance can be used. The complementary part of the total variation is called unexplained or residual variation ...
The predicted response distribution is the predicted distribution of the residuals at the given point xd. So the variance is given by. The second line follows from the fact that is zero because the new prediction point is independent of the data used to fit the model. Additionally, the term was calculated earlier for the mean response.
In predictive analytics, a table of confusion (sometimes also called a confusion matrix) is a table with two rows and two columns that reports the number of true positives, false negatives, false positives, and true negatives. This allows more detailed analysis than simply observing the proportion of correct classifications (accuracy).
Conditional variance. In probability theory and statistics, a conditional variance is the variance of a random variable given the value (s) of one or more other variables. Particularly in econometrics, the conditional variance is also known as the scedastic function or skedastic function. [1] Conditional variances are important parts of ...
It is remarkable that the sum of squares of the residuals and the sample mean can be shown to be independent of each other, using, e.g. Basu's theorem.That fact, and the normal and chi-squared distributions given above form the basis of calculations involving the t-statistic:
If a vector of predictions is generated from a sample of data points on all variables, and is the vector of observed values of the variable being predicted, with ^ being the predicted values (e.g. as from a least-squares fit), then the within-sample MSE of the predictor is computed as
A variable is considered dependent if it depends on an independent variable. Dependent variables are studied under the supposition or demand that they depend, by some law or rule (e.g., by a mathematical function), on the values of other variables. Independent variables, in turn, are not seen as depending on any other variable in the scope of ...
Plot with random data showing homoscedasticity: at each value of x, the y -value of the dots has about the same variance. Plot with random data showing heteroscedasticity: The variance of the y -values of the dots increases with increasing values of x. In statistics, a sequence of random variables is homoscedastic (/ ˌhoʊmoʊskəˈdæstɪk ...