![]() a fixed annual percent change - APC), while for a linear model the rates change at a constant fixed amount per year. Under a log-linear model the rates change at a constant percent per year (i.e. One motivation for using the log-linear model for cancer rates regardless if they are rare or not is the ease of interpretation. Rates for common cancers or which come from a large population can be approximated as arising from a normal distribution without a transformation. The log transformation is a standard way to make this skewed distribution approximately a normal distribution. ![]() One reason for using a log transformation for cancer rates is that they arise from a Poisson distribution which is skewed especially when the cancer is rare or the rates come from a small population. ![]() Select a model whose residual analysis indicates a better fit, regarding the model assumptions of normality, linearity, equal variance, and independence. In order to check the goodness of fit of the chosen model, a user can test for normality of the residuals obtained under the linear or the log-linear fit. The linear or log-linear model can be chosen depending on how linear the observed rates or the logarithm of the observed rates are over time. ![]()
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