Description: This session is from the 1976 Washington D.C. Meeting and discusses qualitative and quantitative models for forecasting. The session is a general teaching session and not for experts.

Description: This chapter introduces a few probability distributions that play an important role in understanding and analyzing healthcare data. While theoretical in nature, these distributions model real life if appropriately chosen. Probability distributions are mathematical representations of how statistical data behave.

Description: This paper has two related purposes: first, to introduce a new class of Moving Weighted Average MWA graduation formulas where the coefficients are determined in accordance with the relative emphasis placed on smoothness and fit by the graduator, and second, to consider the question regarding what value of n to use when graduating by MWA formula. From the Actuarial Research Clearing House, 1978, Vol.1.

Description: Traditional risk scoring models include only fixed (non-random) demographic and condition-based variables. By including the provider effect as part of the risk scoring model as a random effect, we can get better estimates of provider performance (incorporating practice patterns conditional on patient morbidity profile). This approach can be generalized to study other factors that are now masked in error variance (i.e. area, Social Determinants) but if modeled could give us better estimates of risk

Description: Introducing Random Effects into Risk Score Models