22nd Sept 2023

P-value:

• A measure that aids in assessing the significance of a specific finding in a statistical investigation is the p-value (probability value).
• It quantifies the evidence that refutes a null hypothesis. The null hypothesis frequently presupposes that the data are devoid of any influence or connection.
• A low p-value (usually less than 0.05) denotes statistical significance and shows substantial evidence opposing the null hypothesis.
• However, a high p-value indicates that there is little evidence to support the null hypothesis and that the result is not statistically significant.

R-squared:

• In regression analysis, the R-squared statistic is used to assess how well a model fits the data.
• It shows how much of the variance in the dependent variable, which is the variable being predicted, can be attributed to the model’s independent variables, or predictor variables.
• Higher numbers suggest a better fit, and R-squared values range from 0 to 1. An R-squared of 1 indicates that the model perfectly explains the data’s variance, whereas a value of 0 indicates that the model cannot account for any data’s variation.
• R-squared is used to measure how well a model fits observed data, although it is not always a reliable indicator of how well a model predicts the future.