Topics Learnt Today:
Multi Linear Regression
A dependent variable and two or more independent variables, often known as predictors or explanatory variables, are modelled using the statistical technique known as multiple linear regression (MLR). It is a development of straightforward linear regression, which takes into account just one independent variable. MLR tries to analyse and quantify the links between various independent variables and the dependent variable.
1: Coefficient Interpretation: The coefficients (1, 2, 3, etc.) show how strongly and in which direction each independent variable and the dependent variable are related. For instance, if 1 is positive, it implies, supposing all other variables are constant, that an increase in X1 is related with an increase in Y.
2: Intercept: When all independent variables are zero, the intercept (0) reflects the estimated value of the dependent variable. Depending on the context of your data, this value might not always have a relevant interpretation.
3: Assumptions: Multiple linear regression makes the following assumptions: homoscedasticity, normal distribution, and independence of the residuals (the discrepancies between the actual values of Y and the values predicted by the model). The reliability of the regression results may be impacted by violations of these presumptions.
4: Model Evaluation: To evaluate the goodness of fit of the model and ascertain if it sufficiently explains the variability in the dependent variable, a variety of statistical approaches, including hypothesis testing, R-squared, and adjusted R-squared, can be utilised.
5: Multicollinearity: This phenomenon happens when there is a strong correlation between two or more independent variables in a model. As a result, figuring out the unique contributions of each variable might be difficult. Multicollinearity can be found and addressed using techniques like the variance inflation factor (VIF).