Exercise 11.1 Dietary study: Predict body-mass index (kg/m2) from age, gender, length (cm), weight (kg), and the food intake variables measuring the daily consumption of: alcohol (g/day), calcium (mg/day), cholesterol (mg/day), protein (g/day), iron (mg/day), potassium (mg/day), carbohydrates (g/day) containing mono and disachharides (g/day) and polysachharide (g/day), several parameters measuring fat intake (total fat intake (g/day), mono-unsaturated fat (g/day), poly-unsaturated fat (g/day), saturated fate intake (g/day), ratio poly-unsat/sat fat (PS), ratio mono+poly-unsat/sat fat (US)), sodium (mg/day), phosphorus (mg/day), fibres (mg/day) and energy intake (kcal/day); see excel file ‘IBBENS.xls’ for the data. In addition to a full linear regression analysis by making use of all variables, use as classical variable selection approaches: Forward, backward and stepwise selection techniques, ridge and LASSO shrinkage techniques. Determine the best fitting model(s) to the data. Search for appropriate R functions to do the job.