分析主要成分和双图,根据消费者价格指数降低通货膨胀因子

AL-Muqayyad Pub Date : 2022-12-30 DOI:10.46963/jam.v5i2.766
Anne Mudya Yolanda, A. Adnan, Rustam Efendi, H. Sirait, I. Irfansyah, Okta Bella Syuhada, Rahmad Ramadhan Laska, Riko Febrian
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引用次数: 0

摘要

一个地区的通货膨胀可以通过消费群体的消费者价格指数(CPI)来衡量。目的是研究影响2021年基于CPI的月度通胀的因素。主成分分析用于减少CPI中的支出组变量,其次是双图分析,以二维图形显示PCA的前两个主要成分的可视化。主成分分析结果表明:(1)主要支出成分由住房、水、电和家庭燃料变量构成;设备、工具及家居日常保养;交通运输;信息、通信和金融服务;(2)次要支出成分包括食品、饮料和烟草变量;健康;教育;一般支出部分和(3)互补支出部分,即服装和鞋类变量;个人设备和其他服务。这三个组成部分同时可以代表数据多样性的88.1%。双图分析成功地描述了变量的相似性和位置,总方差为75%
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analisis Komponen Utama dan Biplot untuk Mereduksi Faktor Inflasi Berdasarkan Indeks Harga Konsumen
Inflation of a region can be measured from the Consumer Price Index (CPI) by spending group. The aim is to look at the factors that influence monthly inflation based on the CPI for 2021. Principal Component Analysis is used to reduce the expenditure group variables in the CPI, followed by biplot analysis to display the visualization of the first two main components of the PCA in a two-dimensional graph. The results of the main component analysis, (1) the primary expenditure component consists of housing, water, electricity and household fuel variables; equipment, tools and household routine maintenance; transportation; information, communication and financial services; recreation, sports and culture, (2) secondary expenditure components include food, drink and tobacco variables; health; education; general, and (3) complementary expenditure components, namely clothing and footwear variables; personal equipment and other services. These three components simultaneously can represent 88.1% of the diversity of the data. Biplot analysis succeeded in describing the similarity and position of the variables with a total variance of 75%
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