### Abstract

Original language | English |
---|---|

Pages | 41-45 |

Number of pages | 5 |

Volume | 2010 |

No. | March |

Specialist publication | Optometry Today |

Publication status | Published - Mar 2010 |

### Fingerprint

### Keywords

- regression
- statistical procedures
- clinical studies
- optometry

### Cite this

*Optometry Today*,

*2010*(March), 41-45.

}

*Optometry Today*, vol. 2010, no. March, pp. 41-45.

**Data methods in optometry. Part 10: non-linear regression analysis.** / Armstrong, Richard A.; Eperjesi, Frank.

Research output: Contribution to specialist publication › Article

TY - GEN

T1 - Data methods in optometry. Part 10: non-linear regression analysis

AU - Armstrong, Richard A.

AU - Eperjesi, Frank

PY - 2010/3

Y1 - 2010/3

N2 - 1. The techniques associated with regression, whether linear or non-linear, are some of the most useful statistical procedures that can be applied in clinical studies in optometry. 2. In some cases, there may be no scientific model of the relationship between X and Y that can be specified in advance and the objective may be to provide a ‘curve of best fit’ for predictive purposes. In such cases, the fitting of a general polynomial type curve may be the best approach. 3. An investigator may have a specific model in mind that relates Y to X and the data may provide a test of this hypothesis. Some of these curves can be reduced to a linear regression by transformation, e.g., the exponential and negative exponential decay curves. 4. In some circumstances, e.g., the asymptotic curve or logistic growth law, a more complex process of curve fitting involving non-linear estimation will be required.

AB - 1. The techniques associated with regression, whether linear or non-linear, are some of the most useful statistical procedures that can be applied in clinical studies in optometry. 2. In some cases, there may be no scientific model of the relationship between X and Y that can be specified in advance and the objective may be to provide a ‘curve of best fit’ for predictive purposes. In such cases, the fitting of a general polynomial type curve may be the best approach. 3. An investigator may have a specific model in mind that relates Y to X and the data may provide a test of this hypothesis. Some of these curves can be reduced to a linear regression by transformation, e.g., the exponential and negative exponential decay curves. 4. In some circumstances, e.g., the asymptotic curve or logistic growth law, a more complex process of curve fitting involving non-linear estimation will be required.

KW - regression

KW - statistical procedures

KW - clinical studies

KW - optometry

M3 - Article

VL - 2010

SP - 41

EP - 45

JO - Optometry Today

JF - Optometry Today

SN - 0268-5485

ER -