Tasks: 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. 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. 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. 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. Other Analysis: Robust Sensitivity 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