Answer:
Method given below. This question is based on python programming. User has to complete the analysis using python.
Step-by-step explanation:
c) Regression Analysis:
Level of Explained Variation (R-squared):
Check the R-squared value in the regression output. If it's, for example, 0.85, it means that 85% of the overall score's variance is explained by the predictor variables.
Model's Significance (Overall F-test):
Look at the p-value associated with the overall F-test. If it's less than 0.05 (α = 5%), the model is considered statistically significant.
Predictor Variables' Significance (Coefficients and p-values):
Examine the coefficients and associated p-values for each predictor variable. Low p-values (typically < 0.05) indicate that the predictor variable is statistically significant.
d) Address Concerns and Perform Regression Again:
Review Concerns:
Address any issues such as outliers, multicollinearity, or influential data points.
Changes in the Regression Equation:
If you made adjustments, discuss the changes in the regression equation. For example, if 'ShoreExcursions' was removed, the new equation would only include 'Itineraries' and 'FoodDining.'
Level of Explained Variation (R-squared):
Compare the new R-squared value with the initial one. Improved R-squared indicates a better fit.
Model's Significance (Overall F-test):
Check the p-value of the overall F-test in the modified model. It should still be less than 0.05 for significance.
Predictor Variables' Significance (Coefficients and p-values):
Examine the coefficients and p-values for each predictor variable in the modified model. Ensure they remain statistically significant.
Provide the specific values from your analysis to discuss the results more precisely. If you encounter challenges or have further questions, feel free to ask.