Primjena linearne regresije na predviđanje COVID-19 pandemije

master's thesis

• Title (english): Application of linear regression to COVID-19 pandemic prediction
• Author: Nikolina Matić
• Mentor: Siniša Slijepčević (mentor)
• Committee member: Siniša Slijepčević (predsjednik povjerenstva)
• Committee member: Dražen Adamović (član povjerenstva)
• Committee member: Rudi Mrazović (član povjerenstva)
• Committee member: Ivica Nakić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Science; (Department of Mathematics); Zagreb
• Defense date and country: 2022-05-05, Croatia
• Scientific / art field, discipline and subdiscipline: NATURAL SCIENCES; Mathematics

Abstract

U ovom radu, objašnjavamo teorijsku pozadinu linearne regresije te primjenjujemo statistički model regresije na stvarne podatke. U prvom poglavlju iskazana je matematička teorija potrebna za razumijevanje sadržaja rada. Drugo poglavlje opisuje model jednostavne linearne regresije te model višestruke linearne regresije. Opisani su uvjeti koje proučavani podaci moraju zadovoljavati kako bi konstrukcija regresijskog modela bila smislena. Konačno, u trećem poglavlju, primijenjujemo linearnu regresiju na aktualnu tematiku današnjice, COVID-19 pandemiju. Sama analiza uključivala je 22 različita faktora (nezavisne varijable) promatrana u 99 različitih zemalja, a promatrani podaci opisuju tjedni ukupan broj oboljelih i smrtnih slučajeva. Same varijable opisuju uređenost pojedine zemlje te one nisu posljedica pojavljivanja COVID-19 pandemije, već one definiraju demografsko-geografsku, političkozakonsku, socijalno-ekonomsku i zdrastvenu sliku pojedine zemlje.

Abstract (english)

This work discusses the theoretical background of linear regression and application of statistical regression model with real data. In the first chapter mathematical theory is stated, required for understanding the work itself. The second chapter describes the simple linear regression model as well as the multiple linear regression model. Various conditions are described which the examined data must satisfy in order for the construction to be meaningful. Finally, in the third chapter, linear regression is being applied on a trending topic of today, the COVID-19 pandemic. The very analysis includes 22 different factors (independent variables) observed in 99 different states, whereas recorded data describes the total number of infected and death cases per week. The variables themselves describe the structure of the individual state and they are not the consequence of occurrences from COVID-19 pandemic, but they define demographic-geographical, political-legal, socioeconomic and health aspects of the individual state.

• Keywords: jednostavna linearna regresija; višestruka linearna regresija; COVID-19 pandemija
• Keywords (english): simple linear regression model; multiple linear regression model; COVID-19 pandemic
• Language: croatian
• URN:NBN: urn:nbn:hr:217:073818
• Study programme: Title: Mathematics Education; specializations in: Mathematics Education; Course: Mathematics Education; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra edukacije matematike (magistar/magistra edukacije matematike)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access
• Created on: 2022-06-07 11:20:42