Regression Analysis Using Statsmodels

Objective:

Teach how to implement regression analysis using statsmodels.

Content Outline:

1. Setting up the Environment:
   • Importing Libraries:
     • Start by demonstrating how to import necessary Python libraries: statsmodels, pandas for data manipulation, and matplotlib or seaborn for data visualization.
     • Example code:

import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt

Loading Data:

• Show how to load a dataset using pandas. Use a simple, well-known dataset like Boston Housing or another dataset that is relevant to the audience.
• Example code:

df = pd.read_csv('path/to/dataset.csv')
print(df.head())

Exploring the Data:

• Basic Data Cleaning:
  • Discuss checking for missing values and demonstrate how to handle them (e.g., using df.dropna() or df.fillna()).
  • Mention the importance of checking for outliers and data type consistency.
• Preprocessing:
  • Explain the necessity of variable selection, feature engineering (if applicable), and the role of dummy variables for categorical data.
  • Show how to prepare data for regression, focusing on selecting independent variables and the dependent variable.
  • Example code:

X = df[['feature1', 'feature2']]
y = df['target']

Creating a Regression Model with Statsmodels:

Specifying the Model:

• Demonstrate how to use statsmodels to fit a simple linear regression model. Explain the syntax and options available.
• Example code:

X = sm.add_constant(X)  # adding a constant
model = sm.OLS(y, X).fit()

Interpreting Results:

• Show how to output the summary of the model and discuss key metrics: coefficients, standard errors, R-squared, adjusted R-squared, and p-values.
• Example code:

print(model.summary())

Code Demonstration: Fit a Simple Linear Regression Model:

• Walk through the complete process using an example dataset:
  • Load data, clean/preprocess it, specify the model, fit the model, and summarize the results.
• Use plots to visualize the relationship between variables and the fit of the regression line:
  • Example code for plotting:

plt.scatter(X['feature1'], y, color='blue')
plt.plot(X['feature1'], model.predict(X), color='red')
plt.show()

1. Discussion on Interpreting Output:
   • Coefficients:
     • Discuss what the coefficients represent and how they influence the dependent variable.
   • R-squared:
     • Explain the concept of R-squared as a measure of how well the variations in the dependent variable are explained by the independent variables.
   • P-values:
     • Discuss the significance of p-values in hypothesis testing to determine the impact of each predictor.