Using logistic regression to determine if health metrics can predict classification of a smoker. The Stat2Data Pulse dataset which records pulse rates before and after exercise of a sample of statistics students using variables of gender, the number of hours of exercise per week a student undertakes, their height, weight and whether or not they are smokers. Ordinarily, one sees this data being used with smoking as a predictor of pulse rate alongside the other variables. We ask the converse question; given this data, can one predict using logistic regression whether a student may be classified as a smoker?

Example of using Plotly; interactive plot of association rules using the R groceries dataset.

IUCN Red List Chameleon Extent of Occupancy and Threat Status. Data extracted via QGIS, geolocated and exported for leaflet presentation.

Logistic regression analysis of Stat2Data package dataset Pulse: Resting pulse is converted to a binary variable where a resting pulse greater than 68 is considered as ‘false’, the kilogramme converted and centred weight offset from the mean is then used as a predictor alongside the binary attribute that records whether the subject is a smoker. These two variables, and their interaction is considered in fitting a logistic regression model with the positive outcome being that of a low resting pulse followed by the testing of these predictions by the fitted model.

Female Antelope Survivalship - as specified as 'optional' in Session 2, Population Dynamics.

Summary and descriptive statics table and plots of impact by event type and the top five storm event types based upon maximum impact.