Tasks:

1. Data Exploration:
   • Provide a summary of the dataset, including descriptive statistics, missing values, and outliers.
   • Visualize the relationships between audit fees and each predictor variable using scatter plots.
2. Correlation Analysis:
   • Calculate and interpret the correlation matrix between audit fees and predictor variables.
   • Identify the variables with the highest and lowest correlations with audit fees.
3. Regression Analysis:
   • Perform a multiple linear regression analysis with audit fees as the dependent variable and all predictor variables as independent variables.
   • Evaluate the overall fit of the regression model using appropriate statistics (R-squared, F-statistic).
   • Interpret the regression model coefficients, paying particular attention to the significance levels.
4. Factor Analysis:
   • Conduct a factor analysis to identify latent factors that explain the shared variance among the predictor variables.
   • Examine the factor loadings and determine the interpretation of each factor.
   • Assess whether the identified factors provide insights into the underlying structure of the data and the potential for dimensionality reduction.
   • Compare the predicted and actual audit fees to assess the model's accuracy.
5. Other Analysis:
   • Robust
   • Sensitivity
6. Discussion and Conclusion:
   • Summarize the key findings from the regression, correlation, and factor analyses.
   • Discuss the variables that significantly influence audit fees and their respective magnitudes.
   • Reflect on the limitations of the analysis and potential areas for further research