Friday – September 22,2023.

In today’s work, I successfully updated my code and constructed linear regression models for all three categories:

1. Inactivity vs. Obesity predicting Diabetes.
2. Inactivity vs. Diabetes predicting Obesity.
3. Obesity vs. Diabetes predicting Inactivity.

However, I am currently facing an issue with calculating confidence intervals & p values for these linear regression models. I’ve been troubleshooting this problem but have not yet found a solution. My goal is to refine my code and proceed with the analysis.

For the analysis of my linear regression models, I plan to follow these steps:

1. Calculating the p-values: Resolve the issue with calculating p-values to determine the significance of each coefficient in the models.
2. Calculating confidence intervals: Once the p-values are successfully calculated, estimate confidence intervals for the coefficients to understand the range of potential values.
3. Using metrics like R-squared: Evaluate the goodness-of-fit of the models using metrics like R-squared to measure how well the models explain the variation in the dependent variable.
4. Performing cross-validation: Implement cross-validation techniques to assess the models’ generalization performance and identify potential overfitting.
5. Finding collinearity: Detect and handle multicollinearity among independent variables to ensure the models’ stability and interpretability.

I’m actively working on resolving the issue with p-values , confidence intervals and progressing with the analysis of these linear regression models.Project 1 - Progress report - Jupyter Notebook