REGRESSION ANALYSIS IN EXAMINING FACTORS AFFECTING TOURISM REVENUES

Bojana Ćendić; Zorica Sredojević; Nataša Milosavljević

doi:10.52370/TISC26450BC

Bojana Ćendić State University of Novi Pazar – Department of Natural and Mathematical Sciences, Novi Pazar, Serbia
Zorica Sredojević University of Belgrade - Faculty of Agriculture, Belgrade - Zemun, Serbia
Nataša Milosavljević University of Belgrade - Faculty of Agriculture, Belgrade - Zemun, Serbia

DOI: https://doi.org/10.52370/TISC26450BC

Keywords: multiple regressions, tourism, revenue, SPSS, statistical analysis, prediction

Abstract

In this paper, the application of multiple linear regression is analysed to assess the factors affecting tourism revenues in European cities. The study uses hypothetical yet realistic data for 15 cities in 2019. Average hotel price, number of tourists, and number of international air connections are included as independent variables, while total tourism revenue is considered as the dependent variable. The regression model was developed and tested using SPSS, estimating coefficients, standard errors, and t-values. Multicollinearity was assessed using the Variance Inflation Factor (VIF), and residual analysis was conducted to confirm model assumptions and prediction reliability. Results show that the number of tourists has the greatest impact on revenue, while other variables represent complementary indicators. The model is statistically significant (p < 0.001) and explains 99.6% of revenue variability, confirming its high predictive accuracy and practical relevance for planning tourism development.

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