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Evaluating agronomic factors influencing cotton yield using multivariate regression and model validation
Abstract
Regression analysis is a statistical technique used to estimate connections between variables that exhibit a cause-and-effect relationship. Despite its widespread use for identifying correlations and predicting outcomes, it is crucial to validate the assumptions and reliability of multivariate regression models. This study focuses on multivariate regression analysis, which utilizes multiple independent variables to predict cotton yield in kilogram per hectare (YldPhec). The secondary dataset was sourced from the Cotton Research Station (CRS) in Uthal, Lasbela, Balochistan, Pakistan. The analysis included eight predictors, revealing that the intercept and PlntPl had marginally significant positive effects (Beta = 4425.26, 0.04; p < 0.1), while Grmn and FbrSnt demonstrated highly significant positive effects (Beta = 48.76, 177.97; p < 0.001). Conversely, BolWt and StpLt exhibited significant negative effects (Beta = -741.49, -246.77; p < 0.001), with Lnt also showing a significant negative effect (Beta = -145.57; p < 0.01). Additionally, MikV and BolPp were not significant. Zero-order correlation analyses indicated strong positive relationships for Grmn (0.56) and PlntPl (0.50), while BolWt (-0.41) and Lnt (-0.42) showed strong negative correlations with the dependent variable. Tolerance values exceeding 0.39 and Variance Inflation Factor values less than 3 indicate that multicollinearity is not a significant concern among the predictors. The Shapiro-Wilk, Rainbow, and Studentized Breusch-Pagan test statistics confirmed the normality of residuals, the linearity of the model, and the absence of significant heteroscedasticity, with p-values of 0.74, 0.84, and 0.32, respectively. Confirming these assumptions enhances the model’s validation and underscores the significance of the identified predictors in explaining the variability of YldPhec.
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References
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DOI: 10.33687/ijae.012.003.5418
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