### Abstract

Situations in which the observations are not normally distributed arise frequently in the quality engineering field. The standard approach to the analysis of such responses is to transform the response into a new quantity that behaves more like a normal random variable. An alternative approach is to use an analysis procedure based on the generalized linear model (GLM), where a nonnormal error distribution and a function that links the predictor to the response may be specified. We present an introduction to the GLM, and show how such models may be fit. We present the GLM as an analog to the normal theory linear model. The usefulness of this approach is illustrated with examples.

Original language | English (US) |
---|---|

Pages (from-to) | 274-291 |

Number of pages | 18 |

Journal | Journal of Quality Technology |

Volume | 29 |

Issue number | 3 |

State | Published - Jul 1997 |

### Fingerprint

### Keywords

- Data Analysis
- Design of Experiments
- Generalized Linear Models
- Least Squares
- Maximum Likelihood
- Regression

### ASJC Scopus subject areas

- Management Science and Operations Research
- Industrial and Manufacturing Engineering
- Statistics and Probability

### Cite this

*Journal of Quality Technology*,

*29*(3), 274-291.

**A tutorial on generalized linear models.** / Myers, Raymond H.; Montgomery, Douglas.

Research output: Contribution to journal › Article

*Journal of Quality Technology*, vol. 29, no. 3, pp. 274-291.

}

TY - JOUR

T1 - A tutorial on generalized linear models

AU - Myers, Raymond H.

AU - Montgomery, Douglas

PY - 1997/7

Y1 - 1997/7

N2 - Situations in which the observations are not normally distributed arise frequently in the quality engineering field. The standard approach to the analysis of such responses is to transform the response into a new quantity that behaves more like a normal random variable. An alternative approach is to use an analysis procedure based on the generalized linear model (GLM), where a nonnormal error distribution and a function that links the predictor to the response may be specified. We present an introduction to the GLM, and show how such models may be fit. We present the GLM as an analog to the normal theory linear model. The usefulness of this approach is illustrated with examples.

AB - Situations in which the observations are not normally distributed arise frequently in the quality engineering field. The standard approach to the analysis of such responses is to transform the response into a new quantity that behaves more like a normal random variable. An alternative approach is to use an analysis procedure based on the generalized linear model (GLM), where a nonnormal error distribution and a function that links the predictor to the response may be specified. We present an introduction to the GLM, and show how such models may be fit. We present the GLM as an analog to the normal theory linear model. The usefulness of this approach is illustrated with examples.

KW - Data Analysis

KW - Design of Experiments

KW - Generalized Linear Models

KW - Least Squares

KW - Maximum Likelihood

KW - Regression

UR - http://www.scopus.com/inward/record.url?scp=0031190088&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0031190088&partnerID=8YFLogxK

M3 - Article

VL - 29

SP - 274

EP - 291

JO - Journal of Quality Technology

JF - Journal of Quality Technology

SN - 0022-4065

IS - 3

ER -