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# goodness of fit

## Appendix C

The Bayesian information criterion (BIC^{i}) or Schwarz criterion (SIC^{i}) is a measure of the goodness of fit of a statistical model, and is often used as a criterion for model selection among a finite set of models. It is based on log-likelihood function (LLF^{i}) and closely related to Akaike's information criterion.

## For goodness of fit's sake

In this paper, we will use NumXL to explain several different goodness-of-fit functions. For sample data, we will use the time series of the monthly average of the hourly ozone level for Los Angeles downtown area, from January 1955 to December 1972. We will start with the log-likelihood function, then expand our focus to cover other derivative measures - namely Akaike's Information Criterion (AIC) and Bayesian/Schwarz Information Criterion (BIC/SIC/SBC). The objective of this exercise is to provide a tutorial for using different goodness-of-fit functions to find a model to ideally fit your data.