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Basics of Multiple Regression in Python
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Multiple regression is a statistical method that is used for modeling the relationship between a dependent variable and multiple independent variables. It is used to predict the value of a dependent variable (what is to be predicted) based on the values of one or more independent variables (predictors for what is to be predicted) . Multiple regression is a technique that is widely used in various fields, including finance, economics, and marketing, to understand the relationship between different variables and make informed decisions.
One library in Python where multiple regression can be performed is the statsmodels library. The Ordinary Least Squares (OLS) class in this library can be used to fit a multiple regression model. The basic syntax for fitting a multiple regression model using statsmodels can be seen below:
# Import necessary library
import statsmodels.api as sm
# Define model variables
x = "[list of independent variables]"
y = "[dependent variable]"
# Add constants to avoid bias in data
x = sm.add_constant(x) # Adds a constant to the independent variables
# Fit the model
model = sm.OLS(y, x).fit()Once the model has been fitted, various statistics can be obtained from the model object for evaluation. For example, the…
