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Regression Analysis

In: Applied Statistics and Multivariate Data Analysis for Business and Economics

Author

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  • Thomas Cleff

    (Pforzheim University of Applied Sciences)

Abstract

This chapter provides a comprehensive introduction to regression analysis, a widely used statistical technique for investigating the relationship between dependent and independent variables. It begins with the fundamental concepts of bivariate regression, illustrating how to calculate and interpret regression coefficients. The chapter continues to explain multiple regression analysis, where the influence of several independent variables on a dependent variable is analysed. It describes how to derive regression equations using ordinary least squares (OLS) and examines how to assess the goodness of fit using the coefficient of determination (R2) and the adjusted R2. Practical applications are demonstrated using tools like R, Excel, SPSS, and Stata, showcasing how these software packages facilitate regression calculations and interpretation of results. The chapter concludes with an exploration of advanced topics such as non-linear regression and the use of dummy variables, providing a solid foundation for applying regression analysis in various research and practical scenarios.

Suggested Citation

  • Thomas Cleff, 2025. "Regression Analysis," Springer Texts in Business and Economics, in: Applied Statistics and Multivariate Data Analysis for Business and Economics, edition 0, chapter 0, pages 383-419, Springer.
    • Handle: RePEc:spr:sptchp:978-3-031-78070-7_10
    DOI: 10.1007/978-3-031-78070-7_10
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    More about this item

    Keywords

    Regression analysis; Multiple regression; Coefficient of determination (R2); Adjusted R2; Ordinary least squares (OLS); Multicollinearity; Heteroscedasticity; Autocorrelation; Non-linear Regression;
    All these keywords.

    JEL classification:

    • R2 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis

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