Today I learnt about Hypothesis testing and calculating p value or more importantly what is P value. and the best and simplest explanation for that is
“Probability of an event happening if the null hypothesis was true.”
I watched few videos on Youtube regarding Hypothesis Testing and Breusch-Pagan Test and I have implemented Breusch-Pagan Test with null hypothesis(Ho)as the model is homoscedastic, and to check the P values
The Breusch-Pagan test is a diagnostic tool used to determine the presence of heteroscedasticity in a regression model. Heteroscedasticity refers to a situation where the variance of the residuals (or errors) from a regression model is not constant across all levels of the independent variables. The null hypothesis () for the Breusch-Pagan test is that the error variances are homoscedastic (constant across all levels of the independent variables), whereas the alternative hypothesis () posits that the error variances are heteroscedastic (not constant). The -value associated with the test statistic provides the probability of observing the given data (or something more extreme) if the null hypothesis were true. A low -value indicates evidence against the null hypothesis, suggesting heteroscedasticity.
For our regression model with %Inactivity as the predictor and %Diabetic as the response variable, the Breusch-Pagan test was applied. The Lagrange Multiplier (LM) statistic was found to be significant with a P-value close to 0 (approximately 3.607×10−13), indicating strong evidence against the null hypothesis of homoscedasticity. This suggests the presence of heteroscedasticity in the model. The F-test associated with the Breusch-Pagan test yielded unusual results, with a -value of 1, which typically would not suggest heteroscedasticity. However, given the strong evidence from the LM test, it’s recommended to consider the model as having heteroscedastic errors.
This may necessitate adjustments or alternative modelling techniques to ensure valid inference and predictions.
