Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Multipel regressionsanalys av variabler som paverkar BNP
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.).
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.).
2014 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

In this report a model was constructed in order to determine how a number of covariates influence the gross domestic product, GDP. The covariates were chosen depending on their expected influence on GDP, for example education and life expectancy. The data used in this report are collected from the World Bank. The model to describe GDP has been calculated using multiple line arregression. In order to reach a reliable final model the number of covariates has been gradually decreased to eliminate insignificant covariates. In order to minimize the error term and find a reliable model the Baysian Information Criterion has been used together with hypothesis testing. At a 95% confidence interval the final model could predict 111 of 139 countries GDP. The influences of the covariates in the final model is well in line with the expectations. For instance a positive relationship between GDP and education is observed.

Abstract [sv]

I denna rapport har en modell tagits fram som beskriver hur bruttonationalprodukten, BNP, påverkas av ett antal kovariater, t.ex. befolkningens utbildning och livslängd. Data för BNP samt för samtliga kovariater har inhämtats från Världsbanken. Modellen för att beskriva BNP har tagits frammed hjälp av multipel linjär regression. För att nå en slutgiltig modell med god tillförlitlighet har antalet kovariater successivt minskats för att utesluta insignifikanta kovariater. För att minimera feltermen och hitta en tillförlitlig modell har Baysian Information Criterion använts i kombination med hypotestest. Den slutgiltiga modellenkunde med ett konfidensintervall på 95% prediktera 111 av 139 länders BNP. Den slutgiltiga modellen stämmer väl med den förväntade bilden av kovariaternas inverkan på BNP. Exempelvis syns en positiv relation mellan utbildning och ett lands BNP.

Place, publisher, year, edition, pages
2014. , 22 p.
National Category
Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-155200OAI: oai:DiVA.org:kth-155200DiVA: diva2:760125
Educational program
Master of Science in Engineering - Vehicle Engineering
Supervisors
Available from: 2014-11-03 Created: 2014-11-03 Last updated: 2014-11-03Bibliographically approved

Open Access in DiVA

Carl Cronsioe & Marcus Ribbenstedt kandidatexam(504 kB)429 downloads
File information
File name FULLTEXT01.pdfFile size 504 kBChecksum SHA-512
259c5d1d91aea4bebbe9c0eca25f1f4e9512182e4b2f7a8ea87a6736d1b0c23f444af8431f8ea4b674fafb3073ecdd823da16256e096b47ae66b6a064cef7d41
Type fulltextMimetype application/pdf

By organisation
Mathematics (Dept.)
Mathematics

Search outside of DiVA

GoogleGoogle Scholar
Total: 429 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 297 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf