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A Multiple Linear Regression Model To Assess The Effects of Macroeconomic Factors On Small and Medium-Sized Enterprises
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
En multipel linjär regressionsmodell för att bedöma effekterna av makroekonomiska faktorer på små och medelstora företag (Swedish)
Abstract [en]

Small and medium-sized enterprises (SMEs) have long been considered the backbone in any country’s economy for their contribution to growth and prosperity. It is therefore of great importance that the government and legislators adopt policies that optimise the success of SMEs. Recent concerns of an impending recession has made this topic even more relevant since small companies will have greater difficulty withstanding such an event. This thesis will focus on the effects of macroeconomic factors on SMEs in Sweden, with the usage of multiple linear regression. Data was collected for a 10 year period, from 2009 to 2019 at a monthly interval. The end result was a five variable model with an coefficient of determination of 98%.

Abstract [sv]

Små- och medelstora företag (SMEs) har länge varit ansedda som en av de viktigaste komponenterna i ett lands ekonomi, främst för deras bidrag till tillväxt och framgång. Det är därför mycket viktigt att regeringar och lagstiftare för en politik som främjar SMEs optimala tillväxt. Flera år av högkonjunktur och oro över kommande lågkonjunktur har gjort detta ämne ytterst relevant då små företag är de som kommer att drabbas värst av en svårare ekonomisk tillvaro. Denna rapport använder multipel linjär regression för att utvärdera effekterna av olika makroekonomiska faktorer på SMEs i Sverige. Data har insamlats månadsvis för en 10 årsperiod mellan 2009 till 2010. Resultatet blev en modell med fem variabler och en förklaringsgrad på 98%.

Place, publisher, year, edition, pages
2019.
Series
TRITA-SCI-GRU ; 2019:166
Keywords [en]
Statistics, Applied Mathematics, Regression Analysis
Keywords [sv]
Statistik, tillämpad matematik, Regressionsanalys
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:kth:diva-254298OAI: oai:DiVA.org:kth-254298DiVA, id: diva2:1334739
Subject / course
Applied Mathematics and Industrial Economics
Educational program
Master of Science in Engineering - Industrial Engineering and Management
Supervisors
Examiners
Available from: 2019-07-03 Created: 2019-07-03 Last updated: 2019-07-03Bibliographically approved

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