Читать книгу Handbook of Regression Analysis With Applications in R - Samprit Chatterjee - Страница 27

AutocorrelationCorrelation between adjacent observations in a (time) series. In the regression context it is autocorrelation of the errors that is a violation of assumptions.Coefficient of determination()The square of the multiple correlation coefficient, estimates the proportion of variability in the target variable that is explained by the predictors in the linear model.Confidence interval for a fitted valueA measure of precision of the estimate of the expected target value for a given .Dependent variableCharacteristic of each member of the sample that is being modeled. This is also known as the target or response variable.Fitted valueThe least squares estimate of the expected target value for a particular observation obtained from the fitted regression model.HeteroscedasticityUnequal variance; this can refer to observed unequal variance of the residuals or theoretical unequal variance of the errors.HomoscedasticityEqual variance; this can refer to observed equal variance of the residuals or the assumed equal variance of the errors.Independent variable(s)Characteristic(s) of each member of the sample that could be used to model the dependent variable. These are also known as the predicting variables.Least squaresA method of estimation that minimizes the sum of squared deviations of the observed target values from their estimated expected values.Prediction intervalThe interval estimate for the value of the target variable for an individual member of the population using the fitted regression model.ResidualThe difference between the observed target value and the corresponding fitted value.Residual mean squareAn unbiased estimate of the variance of the errors. It is obtained by dividing the sum of squares of the residuals by , where is the number of observations and is the number of predicting variables.Standard error of the estimate ()An estimate of , the standard deviation of the errors, equaling the square root of the residual mean square.