Minitab uses the same approach to the design matrix as used in general linear model (GLM), which uses regression to fit the model you specify. First Minitab creates a design matrix from the factors and the model that you specify. The columns of this matrix, called **X**, represent the terms in the model.

The design matrix has n rows, where n = number of observations and several blocks of columns, corresponding to the terms in the model. The first block is for the constant and contains just one column, a column of all ones. The block for a continuous factor also contains just one column. The block of columns for a categorical factor contains r columns, where r = degrees of freedom for the factor.

For example, a general full factorial design can have factors with more than 2 levels. Suppose A is a factor with 4 levels. Then it has 3 degrees of freedom and its block contains 3 columns, call them A1, A2, A3. Each row is coded as one of the following:

Level of A | A1 | A2 | A3 |
---|---|---|---|

1 | 1 | 0 | 0 |

2 | 0 | 1 | 0 |

3 | 0 | 0 | 1 |

4 | -1 | -1 | -1 |

To calculate the columns for an interaction term, multiply the corresponding columns for the factors in the interaction. For example, suppose factor A has 6 levels, C has 3 levels, D has 4 levels. Then the term A * C * D has 5 x 2 x 3 = 30 columns. To obtain them, multiply each column for A by each for C, by each for D.

Estimated effects for each factor. Effects are only calculated for two-level models and are not calculated for general factorial models. The formula for the effect of a factor is:

Effect = Coefficient * 2

The estimates of the population regression coefficients in a regression equation. For each factor, Minitab calculates k - 1 coefficients, where k is the number of levels in the factor. For a 2-factor, 2-level, full factorial model, the formulas for coefficients for the factors and interactions are:

The standard error of the coefficient for this 2-factor, 2-level, full factorial model is:

For information on models with more than two factors or factors with more than two levels, see Montgomery^{1}.

Term | Description |
---|---|

mean of y at the high level of factor A | |

overall mean of all observations | |

mean of y at the high level of factor B | |

mean of y at the high levels of A and B | |

MSE | mean square error |

n | number of - 1's and 1's (in the covariance matrix) for the estimated term |

Box-Cox transformation selects lambda values, as shown below, which minimize the residual sum of squares. The resulting transformation is *Y* ^{λ} when λ ≠ 0 and ln(*Y*) when λ = 0. When λ < 0, Minitab also multiplies the transformed response by −1 to maintain the order from the untransformed response.

Minitab searches for an optimal value between −2 and 2. Values that fall outside of this interval might not result in a better fit.

Here are some common transformations where *Y*′ is the transform of the data *Y*:

Lambda (λ) value | Transformation |
---|---|

λ = 2 | Y′ = Y ^{2} |

λ = 0.5 | Y′ = |

λ = 0 | Y′ = ln(Y ) |

λ = −0.5 | |

λ = −1 | Y′ = −1 / Y |

Weighted least squares regression is a method for dealing with observations that have nonconstant variances. If the variances are not constant, observations with:

- large variances should be given relatively small weights
- small variances should be given relatively large weights

The usual choice of weights is the inverse of pure error variance in the response.

The formula for the estimated coefficients is as follows:

This is equivalent to minimizing the weighted SS Error.

Term | Description |
---|---|

X | design matrix |

X' | transpose of the design matrix |

W | an n x n matrix with the weights on the diagonal |

Y | vector of response values |

n | number of observations |

w_{i} | weight for the i^{th} observation |

y_{i} | response value for the i^{th} observation |

fitted value for the i^{th} observation |