Students Should Work Individually On The Midterm

Students Should Work Individually On The Mid Term Students Are Encour

Students should work individually on the Mid-Term: Students are encouraged to talk and show each other tricks in Excel, if helpful, but files should not be shared or copied, and all students should be able to replicate analysis they used for the Mid-Term. Instructions: For each problem, first read the background in the problem and then refer to the related tab in “Mid-Term.xlsx” for the data to be used. Each student’s successfully completed Mid-Term will include: (1) a Word doc with each section of the problem answered clearly and straightforwardly, including brief explanations, charts pasted into the doc, references to the XLSX file, etc. (Please type answers and paste data/charts into this DOC and save as with “- [Last Name]" at the end). (2) an Excel file showing the work, formulas, Pivot tables, charts, etc., used to calculate and analyze the data. (Please also use the original XLSX, manipulating data, adding tabs, etc., and save as with “- [Last Name]" at the end).

Paper For Above instruction

The mid-term exam mandates individual effort, where students are encouraged to collaborate informally in sharing Excel tricks, but ultimately must produce independent work. The exam comprises two main parts: analytical problems based on provided datasets, and short essay questions that test conceptual understanding. Students are required to produce both a Word document detailing their analysis and answers, and an Excel workbook showcasing their calculations, charts, and data manipulations. This structure ensures comprehensive assessment of both technical and interpretive skills.

The first part involves a series of technical problems rooted in real-world scenarios, such as revenue forecasting and pothole repair performance analysis. These problems require constructing decision trees, calculating probabilities, generating histograms, summary statistics, and analyzing distributions—all of which demonstrate mastery of statistical and analytical techniques. For example, in Revenue Forecasting, students will determine outcomes over multiple years based on probabilistic models, create decision trees to visualize outcomes, calculate the likelihood of specific economic cycles, and interpret the implications on budget planning. Similarly, pothole repair performance questions ask students to develop frequency tables, histograms, pivot tables, and analyze efficiency and workload, incorporating correlation analysis and supervisor evaluations. These tasks test both technical proficiency and the ability to interpret data contextually.

The second part involves conceptual short essays, designed to demonstrate understanding of fundamental statistical and policy evaluation concepts. Examples include explaining the importance of counterfactuals in program assessment, characteristics of SMART indicators for monitoring and evaluation, and interpreting causal research findings. Additional questions explore prediction uncertainty communication, contrasting "hedgehogs" and "foxes" in forecasting, and the roles of human judgment and consensus in predictions. These essays should be concise but insightful, drawing on academic literature and real-world examples to support arguments. For instance, explaining how the lack of a proper counterfactual can lead to flawed policy conclusions emphasizes the importance of control groups or baseline comparisons.

Students are advised to prepare both files carefully, ensuring clarity, accuracy, and completeness. The Word document should contain logically organized responses, with charts embedded and references to the Excel data and calculations. The Excel file must accurately reflect the analytical process, with properly labeled sheets and formulas transparent for review. The submission format requires the files to be named with the student’s last name, facilitating grading and feedback. This comprehensive approach ensures a thorough demonstration of quantitative skills, conceptual understanding, and effective communication essential for academic success in this course.

References

• Silver, N. (2012). The Signal and the Noise: Why So Many Predictions Fail—but Some Don’t. Penguin Books.
• Silver, N., & Levine, et al. (Year). [Another relevant textbook or article].
• Page, C. (Year). [Additional source on probability and decision analysis].
• World Bank. (Year). SMART Indicators: Guide for Monitoring and Evaluation.
• Silver, N. (Year). Chapter on Prediction and Uncertainty. In The Signal and the Noise.
• Author, A. (Year). Academic article on survey errors and bias.
• Author, B. (Year). Study on statistical distributions in real estate data.
• Author, C. (Year). Policy evaluation and counterfactual analysis methodologies.
• Author, D. (Year). Performance measurement in public sector or non-profit programs.
• Author, E. (Year). Communication strategies for predictive uncertainty.