Evaluation of the Current Situation of Tea Production and Consumption in Bangladesh Through Different Statistical Models
DOI:
https://doi.org/10.3329/jbs.v31i2.74143Keywords:
GPR, Forecasting, Residuals analysis, Trend Analysis, Tea consumption and productionAbstract
Tea is considered a valuable non-alcoholic beverage worldwide and is gaining popularity as a healthy drink due to its multifarious medicinal properties. The tea industry is a pivotal economic driver of Bangladesh with rising production and consumption. Several statistical models were used to anticipate the best-fitted model and pattern of production and consumption until 2025. Data collected from numerous authentic sources and analyzed. Rational Quadratic Gaussian Process Regression (GPR) and Quadratic Support Vector Machines (SVM) models were chosen for tea production and consumption respectively based on RMSE and R-Square value. In 2022, this study predicts tea production to be 93.83 millon kg and consumption to be 98.48 million kg while intersecting each other. Our study suggests an existing gap in the production and consumption trend and this issue needs to be addressed imperatively.Tea is considered a valuable non-alcoholic beverage worldwide and is gaining popularity as a healthy drink due to its multifarious medicinal properties. The tea industry is a pivotal economic driver of Bangladesh with rising production and consumption. Several statistical models were used to anticipate the best-fitted model and pattern of production and consumption until 2025. Data collected from numerous authentic sources and analyzed. Rational Quadratic Gaussian Process Regression (GPR) and Quadratic Support Vector Machines (SVM) models were chosen for tea production and consumption respectively based on RMSE and R-Square value. In 2022, this study predicts tea production to be 93.83 millon kg and consumption to be 98.48 million kg while intersecting each other. Our study suggests an existing gap in the production and consumption trend and this issue needs to be addressed imperatively.
J. Bio-Sci. 31(2): 25-34, 2023
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