Final Project Phase Due Sunday Week 11: What Should You Subm

Final Project Phase Due Sunday Week 11 What Should You Submit1

Describe the purpose of your project, include a summary of your research findings, present previous research or data used, explain the basic theory and terminology, and include any similar research and pre-existing results. Provide detailed analysis with graphs, tables, assessment of data quality, data structure evaluation, statistical model performance, and discussion of the model's effectiveness. Include flowcharts, explanations of data structure, variables, and process descriptions. Submit the complete Word document and your .R code file. The report should contain an introduction, methodology, analysis, conclusions, and references, with proper citations and absence of major grammatical errors. Your analysis should explore problem opportunities using at least two data mining methods (such as regression, decision trees, neural networks, clustering, association rules, time series, genetic algorithms), explaining why your chosen methods are appropriate, and comparing their performance. Discuss accuracy measures, error analysis, ROC curves, and justify your recommendations based on data analysis. Organize the project with clear research paper sections, citing relevant literature using APA format, and ensure originality to adhere to academic integrity policies. Include a summary abstract of your research topic and project goals as a preliminary step.

Paper For Above instruction

The final phase of this project entails a comprehensive analytical and methodological investigation into a chosen dataset, with the objective of uncovering insights, testing hypotheses, or building predictive models relevant to labor relations, specifically focusing on collective bargaining and trade union activities. This process involves multiple stages, including data preparation, methodological selection, model development, evaluation, and interpretation, all meticulously documented to meet academic and research standards.

The purpose of this research is to analyze the Labor Relation Data Set, which encompasses detailed information related to collective bargaining agreements and trade union metrics, such as union density and bargaining coverage. By understanding these elements, the project aims to identify patterns, evaluate the impacts of various factors on labor relations, and develop effective models that can predict or explain outcomes such as wage increases, contract duration, and employer contributions. The research findings could provide valuable insights for policymakers, labor organizations, and businesses seeking to optimize labor relations strategies.

The prior research and data employed in this project include implementation of advanced data mining techniques, such as decision trees and neural networks, to assess their applicability and performance on this dataset. These methodologies were selected based on their ability to handle complex, multi-variable data and their interpretability. For example, decision trees offer visual clarity and straightforward decision rules, whereas neural networks can model nonlinear relationships robustly. The theory underlying these approaches is grounded in established machine learning principles, with terminology including model pruning, validation, ROC analysis, and error metrics.

The analysis phase will involve generating and interpreting various graphs and tables to demonstrate preliminary results, including accuracy measures, confusion matrices, and ROC curves for models like logistic regression and discriminant analysis. Data quality will be evaluated by checking for missing values, inconsistencies, and variable distributions. Strategies for data preparation include handling missing values through coding or deletion, deciding on ranges for numeric variables, and hierarchical grouping of categorical data where appropriate.

Furthermore, the research will compare the performance of selected models, assess their predictive accuracy, and analyze their implications for the labor relations field. For instance, the ROC curves will illustrate trade-offs between false positives and negatives in predicting labor contract outcomes. Model results will be summarized through statistical measures such as precision, recall, F1-score, and AUC, supporting evidence-based recommendations.

Finally, conclusions will synthesize findings, highlight potential areas for further study, and suggest policy or organizational adjustments based on the model outcomes. Challenges encountered, such as inconclusive results or data limitations, will be discussed, along with suggestions for future data collection or methodological refinements. Proper citations to relevant literature in labor economics, data mining methods, and statistical modeling will underpin the research framework.

References

• Abowd, J. M., & Kramarz, F. (1999). The analysis of labor markets using matched employer-employee data. Handbook of labor economics, 3, 816-878.
• Visser, J. (2015). Data Base on Institutional Characteristics of Trade Unions, Wage Setting, State Intervention and Social Pacts (ICTWSS). Institute for Advanced Labour Studies AIAS.
• Visser, J., Hayter, S., & Gammarano, R. (2015). Trends in Collective Bargaining Coverage: Stability, Erosion or Decline? Labour Relations and Collective Bargaining. ILO.
• Gunningham, N., & Rees, J. (1998). Industry self-regulation: An institutional alternative to command-and-control regulation. Law & Policy, 20(1), 1-23.
• Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques. Morgan Kaufmann Publishers.
• Witten, I. H., Frank, E., & Hall, M. A. (2017). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer.
• Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer.
• Zhu, D., Premkumar, G., Zhang, X., & Chu, C.-H. (2001). Data mining for network intrusion detection: A comparison of alternative methods. Decision Sciences, 32(4), 567-591.
• Agresti, A. (2018). Statistical methods for the social sciences. Pearson.