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MLR_FITTED
Returns an array of cells for the fitted values of the conditional mean, residuals or leverage measures.
Syntax
X
is the independent (explanatory) variables data matrix, such that each column represents one variable.
Mask
is the boolean array to choose the explanatory variables in the model. If missing, all variables in X are included.
Y
is the response or the dependent variable data array (one dimensional array of cells (e.g. rows or columns)).
Intercept
is the constant or the intercept value to fix (e.g. zero). If missing, an intercept will not be fixed and is computed normally.
Return_type
is a switch to select the return output (1 = fitted values (default), 2 = residuals, 3 = standardized residuals, 4 = leverage, 5 = Cook's distance).
Method  Description 

1  Fitted/conditional mean 
2  Residuals 
3  Standardized (aka. Studentized) residuals 
4  Leverage (H) 
5  Cook's distance (D) 
Remarks
 The underlying model is described here.

The regression fitted (aka estimated) conditional mean is calculated as follows:
Residuals are defined as follows:
The standardized (aka studentized) residuals are calculated as follow:
Where:
 is the estimated regression value.
 is the error term in the regression.
 is the standardized error term.
 is the standard error for the ith observation.
 For the influential data analysis, SLR^{i}_FITTED computes two values: leverage statistics and Cook's distance for observations in our sample data.

Leverage statistics describe the influence that each observed value has on the fitted value for that same observation. By definition, the diagonal elements of the hat matrix are the leverages.
Where:
 is the Hat matrix for uncorrelated error terms.
 is a (N x p+1) matrix of explanatory variables where the first column is all ones.
 is the leverage statistics for the ith observation.
 is the ith diagonal element in the hat matrix.

Cook's distance measures the effect of deleting a given observation. Data points with large residuals (outliers) and/or high leverage may distort the outcome and accuracy of a regression. Points with a large Cook's distance are considered to merit closer examination in the analysis.
Where
 is the cook's distance for the ith observation.
 is the leverage statistics (or the ith diagonal element in the hat matrix).
 is the mean square error of the regression model.
 is the number of explanatory variables.
 is the error term (residual) for the ith observation.
 The sample data may include missing values.
 Each column in the input matrix corresponds to a separate variable.
 Each row in the input matrix corresponds to an observation.
 Observations (i.e. row) with missing values in X or Y are removed.
 The number of rows of the response variable (Y) must be equal to number of rows of the explanatory variables (X).
 The MLR_FITTED function is available starting with version 1.60 APACHE.
Examples
References
 Hamilton, J .D.; Time Series Analysis , Princeton University Press (1994), ISBN 0691042896
 Kenney, J. F. and Keeping, E. S. (1962) "Linear Regression and Correlation." Ch. 15 in Mathematics of Statistics, Pt. 1, 3rd ed. Princeton, NJ: Van Nostrand, pp. 252285