Interpreting_OLS_Summary_LR

Important Note - This report is with respect to the Notebook present

If the values of Adjusted Rsqaured and R-sqaured is very different, it is a sign that A feauture/variable, might not be relevant to your model.
Here no such problem occurs

It is used for assessing the overall significance of a model. In a Multiple LR, it compares the model with no predictors.
The Null hypothesis is that these 2 models are equal and Alternate Hypo is that the intercept only model is worse that our model.
We get back a p-value as well as a statistic value, that helps us to select/reject Null hypothesis.
In our case, the p-value is very small (0.00) and high F-statistic value, therefore we reject our Null hypothesis and conclude that there is a Linear Relationship between F1,F2,F3 and the Target Variable.

Unlike f-test, t-test compares each Features with the Target Variable and tells if there is a relationship between them.
Null hypothesis is that the feature variable coefficient is going to be 0 and The Alternate Hypothesis is that the Feature coefficient is not going to be 0.
Higher the t-test value, higher the chances that you reject the Null hypothesis. As per our model, the value is high and hence we reject the Null hypothesis (also p-value < 0.05 to reject the Null hypothesis).