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It seems the

`mass`

column, which we intended to use as the dependent variable, is not found in the merged dataset. This could be due to various reasons such as naming discrepancies or missing data in the original files.If you have another variable in mind that you'd like to use for the regression, or if there's anything else you'd like to explore, please let me know!

To proceed, I'll need to know which variable you'd like to use as the dependent variable for the linear regression model. Could you specify the variable or provide more details on what you're looking for?

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A linear regression model was created using data merged from two datasets: characters and planets. The dependent variable chosen was

`rotation_period`

. The model included various predictors such as orbital period, diameter, surface water, and population. Here are some key points from the regression analysis:**R-squared**: 0.044, indicating that only 4.4% of the variability in rotation period is explained by the model.**P-values**: High p-values for all coefficients suggest that none of the predictors are statistically significant at typical levels.**Model Fit**: The low R-squared combined with high p-values indicates a poor fit of the model to this data.**Warnings**: There's a note about potential multicollinearity or other numerical problems due to a large condition number (1.05e+12).

Overall, these results suggest that the selected predictors do not have a strong predictive relationship with

`rotation_period`

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The model summary of the OLS regression was displayed. It provides detailed statistics including R-squared, adjusted R-squared, F-statistic, log-likelihood, AIC, BIC, and the coefficients for each variable. The diagnostics suggest potential issues with multicollinearity or other numerical problems due to a large condition number.

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A DataFrame was created and displayed with the following columns and values:

- Column A: [1, 2, 3, 4]
- Column B: [5, 6, 7, 8]
- Column C: [9, 10, 11, 12]

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