Linear regression is one of the most commonly used statistical analysis techniques to understand the impact of independent variables on a dependent variable. In...

Residuals are the differences between the observed values of the dependent variable and the predicted values from the dependent variable. Residuals are an important...

Evaluating the effectiveness of extension programs is crucial to ensure that the interventions implemented provide positive impacts for farmers. One way to measure this...

In linear regression analysis, testing residuals is a very common practice. One crucial assumption in linear regression using the least squares method is that...

Homoscedasticity is a crucial assumption in ordinary least square (OLS) linear regression analysis. This assumption refers to the consistent variability of regression residuals across...

The Ordinary Least Squares (OLS) method in simple linear regression analysis is a statistical technique aimed at understanding the influence of an independent variable...

Testing for normality in linear regression analysis is a crucial part of inferential method assumptions, requiring regression residuals to be normally distributed. Residuals are...

The normality test of residuals is one of the assumptions required in the multiple linear regression analysis using the ordinary least square (OLS) method....

Skills in writing articles, nowadays are increasingly needed. A researcher is not only required to be able to produce novelty in research activities but...

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