Select the method or formula of your choice.

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

fitted value | |

x_{k} | k^{th} term. Each term can be a single predictor, a polynomial term, or an interaction term. |

b_{k} | estimate of k^{th} regression coefficient |

The standard error of the fitted value in a regression model with one predictor is:

The standard error of the fitted value in a regression model with more than one predictor is:

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

s^{2} | mean square error |

n | number of observations |

x_{0} | new value of the predictor |

mean of the predictor | |

x_{i} | i^{th} predictor value |

x_{0} | vector of values that produce the fitted values, one for each column in the design matrix, beginning with a 1 for the constant term |

x'_{0} | transpose of the new vector of predictor values |

X | design matrix |

The residual is the difference between an observed value and the corresponding fitted value. This part of the observation is not explained by the model. The residual of an observation is:

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

y_{i} | i^{th} observed response value |

i^{th} fitted value for the response |

Standardized residuals are also called "internally Studentized residuals."

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

e _{i} | i ^{th} residual |

h _{i} | i ^{th} diagonal element of X(X'X)^{–1}X' |

s ^{2} | mean square error |

X | design matrix |

X' | transpose of the design matrix |

Also called the externally Studentized residuals. The formula is:

Another presentation of this formula is :

The model that estimates the *i*^{th} observation omits the *i*^{th} observation from the data set. Therefore, the *i*^{th} observation cannot influence the estimate. Each deleted residual has a student's t-distribution with degrees of freedom.

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

e_{i} | i^{th} residual |

s_{(i)}^{2} | mean square error calculated without the i^{th} observation |

h_{i} | i ^{th} diagonal element of X(X'X)^{–1}X' |

n | number of observations |

p | number of terms, including the constant |

SSE | sum of squares for error |

The range in which the estimated mean response for a given set of predictor values is expected to fall. The interval is defined by lower and upper limits, which Minitab calculates from the confidence level and the standard error of the fits.

where

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

α | chosen alpha value |

n | number of observations |

p | number of parameters |

s^{2} | mean square error |

s^{2}{b} | variance-covariance matrix of the coefficients |

The range in which the predicted response for a new observation is expected to fall. The interval is defined by lower and upper limits, which Minitab calculates from the confidence level and the standard error of the prediction. The prediction interval is always wider than the confidence interval because of the added uncertainty involved in predicting a single response versus the mean response.

The formula is: _{0}+ t(1 -α /2; n - p) s(pred)

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

α | chosen alpha value |

n | number of observations |

p | number of predictors |

s (predi |