Predicted y values for x values in range R0 based on the regression coefficients in column range Rc handles case with and w/o an interceptĬolumn array with the standardized regression coefficients for R1 and R2 based on the regression coefficients in the column range Rc (including an intercept coefficient). Two-column array with the regression coefficients for the regression line in the first column and the corresponding standard errors in the second columnħ × 1 column array containing the predicted y value for the data in R0, the standard error for the confidence interval, the lower and upper ends of the 1– alpha confidence interval, the standard error for the prediction interval, the lower and upper ends of the 1– alpha prediction interval ( alpha defaults to. Leverage vector = diagonal of hat matrix for the data in R1Ĭovariance matrix for the regression coefficients of the regression line DESIGN(R1)ĭiagonal of the hat matrix for the data in R1Ĭore of the hat matrix for the data in R1
The following are array functions where R1 contains the X data and R2 contains the Y data.
There is also a second form of the RSquare function in which RSquare(R1, k) = R 2 where the X data consists of all the columns in R1 except the kth column and the Y data consist of the kth column of R1. All these functions can optionally take a third argument con, where con = TRUE (default) means that the regression model takes a constant term and con = FALSE means that the regression model doesn’t have a constant term.Ĭlick here for more details about these functions. Similarly, you can use SSRegTot(R1, R2) and its value will be equivalent to SSRegTot(R2). The following are ordinary, non-array functions where R1 contains the X data and R2 contains the Y data:Ī second R2 parameter can be used with each of the df functions above, although this parameter is not used. The following is a summary of all the various regression and ANOVA functions provided in the Real Statistics Resource Pack.