Assignment Title: Predicting Audit Fees: A Multivariate Analysis

Objective: The objective of this assignment is to utilize various analysis techniques to predict audit fees based on various financial and non-financial factors such as market capitalization, industry, auditor, balance sheet assets, beta, and sales.

Dataset: Use a dataset containing information on audit fees, market capitalization, industry classification, auditor details, balance sheet assets, beta values, and sales for a sample of companies. See details of variables in the appendix.

Starting Point:Using Zhang and Myrteza (1995)as a starting point, conduct an abridged literature review to familiarise yourself with the research objective.

General Requirements:

The following list of deadlines and requirements should be adhered to. Failure to do so will result in a lower grade on the project.

Submission Date: 18 March 2024

Submission and Format Guidelines:

Prepare a comprehensive report that includes data summaries, analysis results, visualizations, and interpretations. Present your findings and conclusions in a well-organized manner. Include any code used for analysis in an appendix.

The paper should

• be typed and double-spaced;
• flow as a well-documented coherent paper;
• cite all sources;
• have correct formats for the bibliography, footnotes, and references;
• have on the first page of the paper, the title of the paper, the author's names, and
• have an executive summary.

Quality is the most appropriate determinant of the grade awarded, but it is suggested that approximately 2,000 words be a suitable length.

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.