Using Simulation to Test the Reliability of Regression Models


  •  Fred J. Rispoli    
  •  Vishal Shah    

Abstract

In many sciences, it is standard laboratory practice to use a statistical design of experiment and a regressionmodel to study the influence of multiple parameters under a wide range of conditions. The current study aims atinvestigating the reliability of regression models by examining recently published models. Of particular interestare the assumptions that are not robust to violation such as the reliability of measurements, constant variation ofresiduals, and sample size. To test regression models simulation is used to model potential measurement errorand the importance of sample sizes on parameter estimation. The randomly perturbed designs are then usedtogether with associated mathematical models obtained from the original designs to simulate experiments andobtain new regression models. A comparison of the original model to the new model, and various statistical testsare performed to determine how accurate the original parameters have been predicted when exposed to simulatedmeasurement error.


This work is licensed under a Creative Commons Attribution 4.0 License.
  • Issn(Print): 1927-0569
  • Issn(Onlne): 1927-0577
  • Started: 2011
  • Frequency: semiannual

Journal Metrics

(The data was calculated based on Google Scholar Citations)

Google-based Impact Factor (2018): 13.22

h-index (January 2018): 12

i10-index (January 2018): 18

h5-index (January 2018): 9

h5-median(January 2018):14

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