After a model selection procedure is performed, the resulting вЂњbestвЂќ model may include predictors that have a significant effect on the response albeit without necessarily improving predictive accuracy. Explanatory analysis requires a less flexible (high bias) but more interpretable model, which uses probabilistic reasoning to approximate a hypothesized data generation process.

ItвЂ™s crucial to understand the distinction between the two as it determines our rationale behind our choice of modeling approach. In other words, weвЂ™re interested in a model that will be used for inference rather than prediction or - to state it more accurately - one that favors causal inference over predictive inference. quantify the uncertainty of our estimates.estimate the magnitude of impact of the significant predictors.identify predictor variables with statistically significant impact to the event of death.The purpose of an explanatory model is to explain rather than predict the outcome of death, where the objective of explanation is the application of statistical inference in order to: We will consequently try to express this response as a function of predictor variables, which are directly or indirectly related to the effect of COVID-19 on manвЂ™s health. Computational statistics concepts such as bootstrap resamplingДЄ mortality risk analysis requires a response variable with records on the event of death.Machine learning concepts such as logistic regression and bagging.This guide to explanatory modeling requires an intermediate understanding of the following topics: Having records at patient-level means that there is enough granularity to derive medical insights about how a personвЂ™s individual attributes relate to the risk of dying from exposure to COVID-19. This TDS article provides a nice overview of the data and its fields. This Kaggle dataset consists of 1085 COVID-19 cases sampled in the city of Wuhan between December 2019 and March 2020, and it was one of the first published datasets to contain records at patient-level.

For a while IвЂ™ve wanted to write an article about statistical model interpretation and all I needed was time, motivation, and data. Perhaps a positive side-effect of the recent pandemic and the associated lockdown is that we get to materialize projects that had been set aside.

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