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MLR_ANOVA
Calculates the regression model analysis of variance (ANOVA) values.
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 output (1 = SSR (default), 2 = SSE^{i}, 3 = SST, 4 = MSR, 5 = MSE, 6 = FStat, 7 = PValue).
Method  Description 

1  SSR (sum of squares of the regression) 
2  SSE (sum of squares of the residuals) 
3  SST (sum of squares of the dependent variable) 
4  MSR (mean squares of the regression) 
5  MSE (mean squares error or residuals) 
6  FStat (test score) 
7  Significance F (Pvalue of the test) 
Remarks
 The underlying model is described here.


The regression ANOVA table which examines the following hypothesis:
 In other words, the regression ANOVA examines the probability that regression does NOT explain the variation in , i.e. that any fit is due purely to chance.

The MLR_ANOVA calculates the different values in the ANOVA tables as shown below:
Where:
 is the number of nonmissing observations in the sample data.
 is the empirical sample average for the dependent variable.
 is the regression model estimate value for the ith observation.
 is the total sum of squares for the dependent variable.
 is the total sum of squares for the regression (i.e. ) estimate.
 is the total sum of error (aka residuals ) terms for the regression (i.e. ) estimate.
AND
Where:
 is the number of explanatory (aka predictor) variables in the regression.
 is the mean squares of the regression.
 is the mean squares of the residuals.
 is the test score of the hypothesis.

.
 The sample data may include missing values.
 Each column in the inputm atrix 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 the number of rows of the explanatory variables (X).
 The MLR_ANOVA 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