This week, we focus our attention on your burning questions about goodness-of-fit functions, like What are the different functions for the goodness of fit? How does each function differ from the rest? Which one do I use, and for what purpose?
Put simply, A goodness-of-fit function is a quantitative measure of the discrepancy (or the agreement) between the observed values and the values expected under a model in question. In general, a measure of goodness of fit helps us to find good (or optimal) values for a model’s coefficients and facilitate the comparison of competing models in an effort to select the best one.
In this tutorial, we’ll start with the log-likelihood function (LLF), and then expand to cover other derivative measures (e.g. Akaike’s Information Criterion (AIC) and Bayesian/Schwarz Information Criterion (BIC/SIC/SBC)). For example, we use the time series of the ozone levels in downtown Los Angeles for the period between January 1955 and December 1972.
The exercise will leave you with a thorough understanding of the goodness of fit while demonstrating how NumXL can help you find an ideal model for your data.
For more details and a step-by-step tutorial, along with a downloadable spreadsheet and PDF, click this link: For the goodness of fit’s sake
