Estimating meat consumption based on economic indicators using linear regression analysis approach: A case study of Türkiye

Abstract

The main idea of this study is to investigate Türkiye’s meat consumption, projection and supplies by using the structure of the Turkish meat industry and Turkish economic indicators. This present study develops several models for the analysis of meat consumption and makes future estimations based on the Regression Analysis Meat Consumption Model (RAMCM). Four forms of Regression Analysis models are used to estimate meat consumption. These models are named Multiple Linear Regression Analysis (MULIRA), Linear Regression Analysis (LIRA), Polynomial Linear Regression Analysis (POLIRA), and Logarithmic Linear Regression Analysis. The models developed in the linear and non-linear forms are applied to estimate meat consumption in Türkiye based on social and economic indicators; Population, Gross National Product (GNP) per capita, Imports of goods and services (% of GDP), Exports of goods and services (% of GDP), electricity consumption per capita, unemployment, Gross capital formation (% of GDP) figures. It may be concluded that the Multiple Linear Regression Analysis models can be used as alternative solutions and estimation techniques for any country's future meat consumption values.

Keywords

• Multiple Linear Regression
• Meat Consumption
• Estimation
• Polynomial Linear Regression Analysis
• Logarithmic Linear Regression Analysis
• Linear Regression Analysis
• Türkiye

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