Test Scores and Average Income Relationship Across Districts Project 2

Project 2

Dataset caschool.csv contains data on test performance, school character- istics, and student demographic backgrounds. In this project, your goal is to understand the relationship between test scores and average income across districts.

1. A written report presenting your results, no longer than three pages, using 12-point font and 1.5 spacing. The title page, tables and graphs do not count towards the page limit. On the title page of this report, include the names of group members along with their student numbers, and indicate the project number. You must also submit a separate R script file containing all the code you used.

Assessment Summary

The assessment titled “Project 2: Test Scores and Average Income Relationship Across Districts” required students to analyze the dataset caschool.csv, which contained information on test performance, school characteristics, and student demographic backgrounds. The core objective was to explore and interpret the relationship between students’ test scores and the average family income across various districts in California.

The assessment consisted of two key deliverables:

1. A written report (maximum three pages, excluding title page, tables, and graphs) formatted with 12-point font and 1.5 line spacing.
2. An R script file containing all code used for data analysis, ensuring transparency and reproducibility of results.

Key pointers to be addressed in the assessment included:

• Understanding and describing the dataset variables.
• Performing statistical analysis to examine the relationship between test scores and average income.
• Creating appropriate data visualizations to support findings.
• Interpreting the results in the context of educational and socio-economic insights.
• Presenting results concisely in a structured academic report format.

Mentor-Guided Approach

The academic mentor guided the student through a systematic process to complete this project efficiently while ensuring conceptual clarity and technical accuracy. The guidance followed a structured, step-by-step approach as described below:

Step 1: Understanding the Objective and Dataset

The mentor began by helping the student interpret the project goal to determine whether higher family incomes are associated with better test scores. The mentor explained each variable in the dataset and encouraged exploratory data analysis (EDA) to understand distributions, missing values, and variable relationships.

Step 2: Data Preparation and Cleaning

Next, the mentor guided the student in loading the dataset into R, checking for missing or inconsistent data, and ensuring variables were appropriately formatted. The importance of clean, structured data for accurate statistical analysis was emphasized.

Step 3: Exploratory Data Analysis (EDA)

Under mentor supervision, the student generated summary statistics and visualized patterns using scatter plots, histograms, and correlation matrices. The mentor encouraged interpretation of trends for example, identifying whether districts with higher average incomes generally had higher test scores.

Step 4: Statistical Analysis

The mentor introduced the student to correlation and simple linear regression analysis in R. Together, they analyzed the strength and direction of the relationship between income and test scores, interpreting regression coefficients and significance levels.

Step 5: Interpreting the Results

The mentor supported the student in transforming numerical outputs into meaningful insights. This included discussing possible confounding factors, limitations of the dataset, and real-world implications in educational policy and socio-economic equity.

Step 6: Structuring the Report

The mentor guided the student in organizing the final report into logical sections introduction, data description, methodology, results, discussion, and conclusion. Clarity, conciseness, and academic tone were reinforced to ensure professional presentation standards.

Step 7: Reviewing and Final Submission

Finally, the mentor reviewed the report for coherence, formatting, and citation accuracy. The R script was checked for proper documentation and reproducibility before final submission.

Outcome and Learning Objectives Achieved

Through this structured mentoring process, the student:

• Gained hands-on experience in data analysis using R programming.
• Developed a deeper understanding of statistical relationships between socio-economic factors and educational outcomes.
• Enhanced ability to interpret data analytically and present findings clearly in a professional report format.
• Demonstrated achievement of key course learning outcomes, including data literacy, critical analysis, and evidence-based reporting.

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