The P-value, short for probability value, is a fundamental concept in mathematical statistics. It quantifies the strength of evidence against a null hypothesis in hypothesis testing. To illustrate, consider a COVID-19 vaccination efficacy experiment with a null hypothesis stating the chemical has no effect. The P-value measures the probability of obtaining the observed results if the drug has no effect. In essence, it assesses the likelihood of our data without any significant impact on the studied group. The Breusch-Pagan Test detects heteroscedasticity, a common issue in regression analysis where error variability changes across independent variable levels. If heteroscedasticity exists, a systematic link between independent variables and squared residuals occurs, as the test examines. Heteroscedasticity refers to variable error or residual variability in a regression model, changing as we move along the x-axis. Detecting and addressing heteroscedasticity is crucial because it can introduce bias and inefficiency in parameter estimates, undermining the model’s reliability for predictions. The Test for Heteroscedasticity is a vital diagnostic tool for uncovering and addressing this issue. R-squared measures how much of the dependent variable’s variance is explained by the independent variables. A housing price prediction model indicates the proportion of variation in prices attributed to the chosen variables. A high R-squared suggests a strong model fit, while a low R-squared implies the model doesn’t capture much of the price variation.