Analysis of Student Scores in Math, Reading, and Writing: Patterns and Predictive Relationships

EasyChair Preprint 15946

Abstract

This research investigates the relationship between student scores in math, reading, and writing. It aims to identify patterns in performance across these subjects and explore the predictive role of math scores for reading. Descriptive statistics, histograms, and box plots were used to summarize and visualize the data. A correlation matrix examined the relationships between subjects, and linear regression predicted reading scores based on math scores. The Shapiro-Wilk test assessed the normality of math scores. Results showed a strong correlation between math and reading scores, with math scores explaining 66.7% of the variation in reading performance.

The study used data on student scores from 0 to 100 in math, reading, and writing. Descriptive statistics revealed average scores of 66.1 for math, 69.2 for reading, and 68.1 for writing. Histograms illustrated score distributions, showing that math scores were more clustered at lower values, while reading and writing scores were more evenly distributed. Box plots revealed that boys scored higher overall, but their scores were more spread out, while girls showed more even distributions.

The correlation matrix indicated strong relationships between math and reading scores. The linear regression equation was: Reading Score = 20.3523 + 0.7214 × Math Score, showing that math scores are a good predictor of reading scores. The Shapiro-Wilk test confirmed that math scores are normally distributed (p-value = 0.663).

In conclusion, the study demonstrates the interconnectedness of math, reading, and writing scores. It suggests that improving math performance could enhance reading outcomes and that gender differences in performance may warrant further exploration. Future research could explore additional factors influencing student performance.

Keyphrases: Educational Data Analysis, Math Scores, Reading Scores, Writing Scores, correlation analysis, linear regression, student performance

@booklet{EasyChair:15946,
  author    = {Khaled M.M. Alrantisi and Gulnaz Gimaletdinova},
  title     = {Analysis of Student Scores in Math, Reading, and Writing: Patterns and Predictive Relationships},
  howpublished = {EasyChair Preprint 15946},
  year      = {EasyChair, 2025}}