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The %MktEval autocall macro evaluates an experimental design for a linear model and reports on balance and orthogonality. You usually run it immediately after using the %MktEx macro. You do not call it after using the %ChoicEff macro to make a choice design. The descriptive statistics that the %MktEval macro produces are appropriate for linear models, not choice models. However, you can reasonably use the %MktEval macro with a linear arrangement that is to be transformed into a choice design later—for example, after using the %MkRoll macro.

The output from the %MktEval macro contains two default tables. The first table shows the canonical correlations between pairs of coded factors. A canonical correlation is the maximum correlation between linear combinations of the coded factors. A design that is orthogonal for main effects has all zeros on the off-diagonal elements of the canonical correlation matrix. The second table lists off-diagonal canonical correlations greater than 0.316 .

For nonorthogonal designs and designs with interactions, the canonical-correlation matrix is not a substitute for using the %MktEx macro to look at the variance matrix. It just provides a quick and more compact picture of the correlations between the factors. The variance matrix is sensitive to the actual model that is specified and the coding. The canonical-correlation matrix just tells you whether there is some correlation between the main effects. When is a canonical correlation too big? You have to decide that for yourself. In part, the answer depends on the factors and how the design will be used. For example, a high correlation between the client’s and the main competitor’s price factor is a serious problem, indicating that you will need to use a different design. In contrast, a moderate correlation in a choice design between one brand’s minor attribute and another brand’s minor attribute might be perfectly fine.

The %MktEval macro also displays one-way, two-way, and n-way frequencies. Equal one-way frequencies occur when the design is balanced. Equal two-way frequencies occur when the design is orthogonal. Equal n-way frequencies, all equal to 1, occur when there are no duplicate runs or choice sets.

%MktEval( <optional arguments>)

You can specify either of the following to display the option names and simple examples of the macro syntax:

%mkteval(help) %mkteval(?)

This macro specifies `options nonotes`

throughout most of its execution. If you want to see all the notes, submit the following statement before running the macro:

%let mktopts = notes;

To see the macro version, submit the following statement before running the macro:

%let mktopts = version;

The following statement creates a design by using the %MktEx macro:

%mktex(2 2 3 ** 6, n=18, balance=0, mintry=5*18, seed=289)

By default, the %MktEx macro saves a randomized version of the design in a data set named `Randomized`

. The following invocation of the %MktEval macro evaluates the design:

%mkteval(data=randomized)

Figure 1 shows the output of the %MktEval macro.

Figure 1: %MktEval Macro Output

Summary of Frequencies |

There is 1 Canonical Correlation Greater Than 0.316 |

* - Indicates Unequal Frequencies |

Frequencies | ||
---|---|---|

x1 | 9 9 | |

x2 | 9 9 | |

x3 | 6 6 6 | |

x4 | 6 6 6 | |

x5 | 6 6 6 | |

x6 | 6 6 6 | |

x7 | 6 6 6 | |

x8 | 6 6 6 | |

* | x1 x2 | 3 6 6 3 |

x1 x3 | 3 3 3 3 3 3 | |

x1 x4 | 3 3 3 3 3 3 | |

x1 x5 | 3 3 3 3 3 3 | |

x1 x6 | 3 3 3 3 3 3 | |

x1 x7 | 3 3 3 3 3 3 | |

x1 x8 | 3 3 3 3 3 3 | |

x2 x3 | 3 3 3 3 3 3 | |

x2 x4 | 3 3 3 3 3 3 | |

x2 x5 | 3 3 3 3 3 3 | |

x2 x6 | 3 3 3 3 3 3 | |

x2 x7 | 3 3 3 3 3 3 | |

x2 x8 | 3 3 3 3 3 3 | |

x3 x4 | 2 2 2 2 2 2 2 2 2 | |

x3 x5 | 2 2 2 2 2 2 2 2 2 | |

x3 x6 | 2 2 2 2 2 2 2 2 2 | |

x3 x7 | 2 2 2 2 2 2 2 2 2 | |

x3 x8 | 2 2 2 2 2 2 2 2 2 | |

x4 x5 | 2 2 2 2 2 2 2 2 2 | |

x4 x6 | 2 2 2 2 2 2 2 2 2 | |

x4 x7 | 2 2 2 2 2 2 2 2 2 | |

x4 x8 | 2 2 2 2 2 2 2 2 2 | |

x5 x6 | 2 2 2 2 2 2 2 2 2 | |

x5 x7 | 2 2 2 2 2 2 2 2 2 | |

x5 x8 | 2 2 2 2 2 2 2 2 2 | |

x6 x7 | 2 2 2 2 2 2 2 2 2 | |

x6 x8 | 2 2 2 2 2 2 2 2 2 | |

x7 x8 | 2 2 2 2 2 2 2 2 2 | |

N-Way | 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 |

The results show that all the factors in this design are perfectly balanced, and almost all are orthogonal, but `X1`

and `X2`

are correlated with each other.