SPATIAL REGRESSION USING MULTIVARIATE CONDITIONAL AUTOREGRESSIVE (MCAR) CASE STUDY: EMPLOYEE’S HEALTH DATA OF A COMPANY

• 主要作者: Liana Putri Utami, Mei
• 格式: Final Project
• 語言: Indonesia
• 在線閱讀: https://digilib.itb.ac.id/gdl/view/47783
• 機構: Institut Teknologi Bandung

Health is a matter of concern. It is important to care for good health to preserve the body’s mass index as well as its level of triglycerides. Body Mass Index (BMI) is the measure to identify a person’s nutritional status as gaining weight and height. BMI is the simplest way to know if people are at risk of having a chronic illness. Triglycerides is one type of fat found in the blood. High levels of triglycerides are an indication of a person’s ailment, such as diabetes. Using the employee data of the company, it will see the effect of the free variable of height, weight, total cholesterol content in the blood, fat in blood, fast glucose level, tenure, and age band on bonded variables of BMI and triglycerides. To see that influence, it is used spatial regression using Multivariate Conditional Auto-Regressive (MCAR) by considering the spatial factor of the company’s employee district/city. Regression coefficients are estimated using a sample retrieval method, Markov Chain Monte Carlo (MCMC). At this final task, there were 100000 samples taken. From the samples, it was found that the weight and height variables had a significant impact on the BMI variable, with a DIC value of -253,8881. For triglycerides variable, total cholesterol and glucose fasting in the blood are variables that give a significant effect.