Materi ini membahas penerapan konsep statistika inferensial, khususnya dalam mengestimasi parameter populasi menggunakan data sampel. Pendekatan yang digunakan adalah interval kepercayaan untuk rata-rata, dengan mempertimbangkan kondisi apakah standar deviasi populasi diketahui atau tidak diketahui. Melalui analisis ini, dapat dipahami bagaimana ukuran sampel, tingkat kepercayaan, serta variabilitas data memengaruhi hasil estimasi. Selain itu, materi ini juga menunjukkan bagaimana hasil analisis statistik dapat digunakan sebagai dasar dalam pengambilan keputusan berdasarkan kriteria tertentu.

This tutorial demonstrates how to build and evaluate a decision tree model in R using the rdecision package, based on a classic pharmacoeconomic study of migraine treatment. The example compares two treatment strategies—oral sumatriptan and a combined oral dose of caffeine/ergotamine—within a clinically realistic framework that captures key patient pathways, including pain relief, recurrence, emergency care, and hospitalization. The tutorial illustrates how to: Construct a decision tree structure programmatically in R Assign probabilities, costs, and outcomes to model transitions Perform cost-effectiveness analysis (CEA) using attacks averted as the outcome Perform cost-utility analysis (CUA) using quality-adjusted life years (QALYs) Calculate incremental cost-effectiveness ratios (ICERs) for decision making Conduct subgroup analysis based on migraine severity Using a one-day time horizon, the model estimates expected costs and outcomes for each treatment strategy and evaluates their economic value. The tutorial provides a reproducible workflow that reflects real-world approaches used in pharmacoeconomics and health technology assessment (HTA). This example is particularly relevant for students and researchers interested in health economic modelling, pharmaco-economics, and applied decision analysis in R. Keywords: decision tree modelling, migraine, cost-effectiveness analysis, cost-utility analysis, QALY, ICER, pharmacoeconomics, R, rdecision

mind map of papers

Human Physiology data

Prolbem statement: Develop a R script to produce a bar graph displaying the frequency distribution of categorical data, grouped by a specific variable using ggplot2

Iris Data

In this project, we are going to look at Airbnb listings in New York City and try to understand what makes some listings more expensive than others. We were given a dataset that contains information about thousands of Airbnb listings in NYC, including things like what type of room it is, which borough it's in, how many reviews it has, and how available it is throughout the year. We thought this was an interesting topic because a lot of us have used Airbnb before and it can be quite confusing why two listings that seem similar can have very different prices. The goal is to use regression analysis to figure out which variables have the biggest influence on the price. The variable we are trying to explain (the dependent variable) is **price**, which is the nightly rental price in US dollars.