Multiple Linear Model

Multiple linear regression model using the statsmodels library in Python. Below is a description of the code and its key components:

1. Import Libraries:
   • import pandas as pd: Imports the Pandas library for data manipulation.
   • import statsmodels.api as sm: Imports the statsmodels library, specifically the API for statistical modeling and hypothesis testing.
2. Load Data:
   • Assumes that you have previously loaded your dataset into a Pandas DataFrame named mdf. It’s important to have your data organized in a way where the first column (mdf.iloc[:, 0]) is the dependent variable (target), and the remaining columns (mdf.iloc[:, 1:]) are the independent variables (features) for the multiple linear regression model.
3. Define Dependent and Independent Variables:
   • y4 = mdf.iloc[:, 0]: Defines the dependent variable (y4) as the first column of the mdf DataFrame. This is the variable you want to predict.
   • x4 = mdf.iloc[:, 1:]: Defines the independent variables (x4) as all the columns except the first one in the mdf DataFrame. These are the variables used to predict the dependent variable.
4. Add a Constant Term (Intercept):
   • x4 = sm.add_constant(x4): Adds a constant term (intercept) to the independent variables. This is necessary for estimating the intercept in the multiple linear regression model.
5. Create and Fit the Linear Regression Model:
   • model = sm.OLS(y4, x4).fit(): Creates a linear regression model using the Ordinary Least Squares (OLS) method provided by statsmodels. It fits the model using the dependent variable y4 and the independent variables x4.
6. Print Regression Summary:
   • print(model.summary()): Prints a summary of the regression analysis. This summary includes various statistics and information about the model, such as coefficient estimates, standard errors, t-values, p-values, R-squared, and more.
7. Extract Intercept and Coefficients:
   • intercept = model.params[‘const’]: Extracts the intercept of the linear regression model and assigns it to the variable intercept. This represents the y-intercept of the regression line.
   • print(f”Intercept: {intercept}”): Prints the value of the intercept.

The code allows you to perform a multiple linear regression analysis, evaluate the model’s performance, and extract important statistics, including the intercept and coefficients. This information can be used for interpretation and further analysis of the relationships between the independent and dependent variables.