We now test x 1 and x 3 for elimination and find that x 1 should not be eliminated (since p-value 1.58E-06 x 3 should be eliminated (since p-value.265655.15). In the following step, we add variable x 4 and so the model contains the variables x 1, x 3, x 4 ). This we test in cell J7 using the formula IF(R6J5,J5,IF(J6,J6)).Ī blank value in any of these rows just means that the corresponding variable was not already in the model and so cant be eliminated.įor example, the test as to whether to eliminate cell x 4 from the model at the second step (when we have just added variable x 1 ) is done in cell G10 using the formula IF(L9R3,IF(G9,G9)). Thus we see that at variable x 4 is the first variable that can be added to the model (provided its p-value is less than the alpha value of.15 (shown in cell R3). The value in cell L8 is the p-value of the x 1 coefficient for the model containing x 1 and x 3 as independent variables (since x 3 was already in the model at that stage).
The even-numbered rows show the p-values for potential variables to include in the model (corresponding to steps 1a and 2a in the above procedure).Īn x in one of these cells indicates that the corresponding variable is already in the model (at least at that stage) and so a p-value doesnt need to be computed. We see that the model starts out with no variables (range G6:J6) and terminates with a model containing x 1 and x 4 (range G12:J12). Metal gear solid 5 the phantom pain crack 3dmĬolumns G through J show the status of the four variables at each step in the process.Īn empty cell corresponds to the corresponding variable not being part of the regression model at that stage, while a non-blank value indicates that the variable is part of the model. In order to make this process clearer, lets look at an example.
Lets call this variable z m 1 and suppose the p-value for the z m 1 coefficient in the regression of y on z 1, z 2, z m, z m 1 is p. Whereas for most statistical tests a value of alpha.05 is chosen, here it is more common to choose a higher value such as alpha.15 or.20. The algorithm we use can be described as follows where x 1, x k are the independent variables and y is the dependent variable.
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