A simple linear regression model is a type of regression analysis used to model the relationship between a single independent variable (predictor) and a dependent variable (response) by fitting a linear equation to the observed data.
This code segment to carry out a straightforward linear regression analysis. To prepare the data, a constant term representing the intercept is first added. Understanding the relationship between the two variables “% INACTIVE” (the independent variable) and “% DIABETIC” (the dependent variable) is the main goal of the analysis. The algorithm finds the best-fitting linear equation by fitting an Ordinary Least Squares (OLS) regression model to the data. Detailed statistics about the model, including coefficients and p-values, are provided via the’summary()’ function. The code then extracts and shows the intercept value, demonstrating how the dataset’s “% INACTIVE” and “% DIABETIC” variables interact.
The code then proceeds to create a visual representation of the relationship between “% INACTIVE” (X3) and “% DIABETIC” (y3) using a scatter plot. The blue dots on the plot represent individual data points, allowing you to see the distribution of your data. Additionally, you overlay a red line on the scatter plot, which represents the OLS regression line. This line summarizes the linear relationship between the two variables as determined by your model.
plt.show() to display the plot, enabling you to visually assess how changes in “% INACTIVE” relate to “% DIABETIC” according to your simple linear regression analysis.