P-value:

The p-value, short for probability value, is a statistical measure that helps evaluate the significance of a particular finding in a statistical analysis. It quantifies the level of evidence that contradicts a null hypothesis, which often assumes that there is no effect or connection in the data being examined. A low p-value, typically below 0.05, indicates statistical significance and provides strong evidence against the null hypothesis. Conversely, a high p-value suggests limited evidence supporting the null hypothesis, signifying that the result is not statistically significant.

R-squared:

In the context of regression analysis, the R-squared statistic is employed to assess how well a model fits the given data. It quantifies the proportion of variance in the dependent variable, the variable being predicted, that can be attributed to the independent variables or predictor variables within the model. Higher R-squared values indicate a better fit, and they range from 0 to 1. An R-squared value of 1 signifies that the model perfectly explains all the variance in the data, while a value of 0 indicates that the model cannot account for any variation in the data. R-squared is used to measure the goodness of fit of a model to observed data, although it may not always accurately indicate the model’s predictive ability for future data.