tælledata

Tælledata, literally meaning “count data” in Danish, refer to observations that take non-negative integer values representing the number of occurrences of an event within a defined interval or spatial unit. They are discrete and often right-skewed, with a variance that may differ from the mean. Common examples include the number of daily hospital admissions, pages read per session, or defects per batch. Count data arise across many disciplines and are typically analyzed with methods that respect their discrete nature rather than standard linear regression.

Modeling count data commonly uses Poisson regression, which assumes equidispersion (the mean equals the variance) and a log link to relate covariates to the expected count. When overdispersion is present, negative binomial regression or quasi-Poisson models are often preferred. For datasets with many zeros, zero-inflated or hurdle models can be appropriate. Generalized linear modeling frameworks allow the inclusion of exposure time via offset terms and the incorporation of covariates. Model diagnostics focus on dispersion, goodness-of-fit, and residual patterns, with Bayesian and nonparametric approaches also used in some contexts.

Data for tælledata are typically collected from administrative records, sensor logs, surveys, or observational studies. Quality issues include underreporting, rounding, truncation, and aggregation bias, all of which can affect inference. Proper analysis requires attention to exposure or population at risk, appropriate handling of zeros and overdispersion, and careful model checking to ensure assumptions are reasonable for the data at hand.