This project is an analysis of the students performance. The goal is to use Linear regression to predict what factors influence the math scores
The data used in this analysis is the "Student Performance" from kaggle.The data contains information about student performance such as gender, race/ethnicity, parental level of education, test preparation courses completion, lunch, reading scores, writing scores and writing scores.
To run the analysis you will need:
- R programming language
- The dataset
I adjusted the math, writing and reading scores to be on a scale of 0 to 100
The following visualization was created to better explore the data:
- A Histogram of the Math, Reading and Writing scores
- A scatter plot of the Math vs Reading scores
- A scatter plot of the Math scores vs. Reading scores
- A correlation between math and reading scores were calculated to be x indicating a strong relationship between the two variables
- A t-test comparing the mean math scores between male and female students showed that male got a mean of 68.72822 while females got 63.633320. Indicating that males had a higher scores that female
- Another t - test mean math scores between the different lunches showed that the group with standard lunch had a significant higher scores in Math compared to the group with free or reduced lunch
In conclusion, this analysis provided insights into the factors influencing math scores among students. The linear regression model revealed that reading scores, writing scores, gender, and lunch type are significant predictors of students math scores.