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Multiple Linear Regression Model Used in Economic Analyses

Author

Listed:
  • Constantin ANGHELACHE

    (Academy of Economic Studies, Bucharest, “Artifex” University of Bucharest)

  • Madalina Gabriela ANGHEL

    (“Artifex” University of Bucharest)

  • Ligia PRODAN

    (Academy of Economic Studies, Bucharest)

  • Cristina SACALA

    (Academy of Economic Studies, Bucharest)

  • Marius POPOVICI

    (Academy of Economic Studies, Bucharest)

Abstract

The multiple regression is a tool that offers the possibility to analyze the correlations between more than two variables, situation which account for most cases in macro-economic studies. The best known method of estimation for multiple regression is the method of least squares. As in the two-variable regression, we choose the regression function of sample and minimize the sum of squared residual values. Another method that allows us to take into account the number of variables factor when determining the validity of harmonization is given by the Akaike information criterion.

Suggested Citation

  • Constantin ANGHELACHE & Madalina Gabriela ANGHEL & Ligia PRODAN & Cristina SACALA & Marius POPOVICI, 2014. "Multiple Linear Regression Model Used in Economic Analyses," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 62(10), pages 120-127, Octomber.
    • Handle: RePEc:rsr:supplm:v:62:y:2014:i:10:p:120-127
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    Citations

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    Cited by:

    1. Constantin ANGHELACHE & Alexandru MANOLE & Mădălina Gabriela ANGHEL, 2015. "Analysis of final consumption and gross investment influence on GDP – multiple linear regression model," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(3(604), A), pages 137-142, Autumn.
    2. Dan CRUCERU & Madalina Gabriela ANGHEL & Aurelian DIACONU, 2016. "The multiple linear regression used to analyse the correlation between variables," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(10), pages 114-117, October.
    3. Florian Blaschke & Biewendt Marcel & Böhnert Arno, 2021. "The Repercussions of the Digital Twin in the Automotive Industry on the New Marketing Logic," European Journal of Marketing and Economics Articles, Revistia Research and Publishing, vol. 4, ejme_v4_i.
    4. Emmanouil Karakostas, 2023. "The Macroeconomic Determinants of the Stock Market Index Performance: The Case of DAX Index," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(3), pages 21-38.
    5. Adriana AnaMaria DAVIDESCU, 2015. "The Relationship between Shadow Economy and Unemployment Rate. A Ardl Causality Analysis for the Case Of Romania," Romanian Statistical Review, Romanian Statistical Review, vol. 63(4), pages 46-62, December.
    6. Mirela PANAIT & Andreea – Ioana Marinescu, 2016. "Statistical-econometric model used for the analysis of the correlation between the Gross Domestic Product and the Labour Productivity," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(11), pages 180-187, November.
    7. repec:agr:journl:v:3(604):y:2015:i:3(604):p:137-142 is not listed on IDEAS
    8. Constantin ANGHELACHE & Madalina Gabriela ANGHEL & Cristina SACALA, 2016. "The Financial Sector Influence On Portfolio Dynamics," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(7), pages 9-13, July.

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