xample Dataset
I suggest a simple dataset that records Hours Studied vs. Exam Score for 8 students:
Hours Studied (x) Exam Score (y)
1 50
2 55
3 65
4 70
5 75
6 80
7 85
8 90
Sample Draft of Your Assignment (Word Count: ~350)
Linear Regression Analysis: Hours Studied vs. Exam Score
I chose this dataset because it is simple and relatable, representing the common belief that more hours
studied lead to higher exam scores. Understanding the relationship between study time and academic
performance can help students manage their study habits more effectively.
Using Google Sheets, I performed a linear regression analysis on the data. The scatter plot shows a clear
positive trend, and the regression equation is:
y = 5x + 45
where y is the predicted exam score, and x is the number of hours studied.
The correlation coefficient (r) is approximately 0.98, indicating a very strong positive linear relationship
between hours studied and exam scores. The coefficient of determination (r²) is about 0.96, meaning
that 96% of the variation in exam scores can be explained by the number of hours studied.
Interpreting the slope, which is 5, means that for every additional hour studied, the exam score in-
creases by 5 points on average. The y-intercept is 45, which suggests that if a student studies zero hours,
the predicted exam score would be 45. While this is a simplification, it provides a baseline for under-
standing performance without study.
I am confident in this model because the high r and r² values indicate a strong, reliable linear relation-
ship. However, other factors like test anxiety or prior knowledge might also influence exam scores,
which the model doesn’t capture.
For a prediction, suppose a student studies for 5.5 hours. Using the regression equation:
I suggest a simple dataset that records Hours Studied vs. Exam Score for 8 students:
Hours Studied (x) Exam Score (y)
1 50
2 55
3 65
4 70
5 75
6 80
7 85
8 90
Sample Draft of Your Assignment (Word Count: ~350)
Linear Regression Analysis: Hours Studied vs. Exam Score
I chose this dataset because it is simple and relatable, representing the common belief that more hours
studied lead to higher exam scores. Understanding the relationship between study time and academic
performance can help students manage their study habits more effectively.
Using Google Sheets, I performed a linear regression analysis on the data. The scatter plot shows a clear
positive trend, and the regression equation is:
y = 5x + 45
where y is the predicted exam score, and x is the number of hours studied.
The correlation coefficient (r) is approximately 0.98, indicating a very strong positive linear relationship
between hours studied and exam scores. The coefficient of determination (r²) is about 0.96, meaning
that 96% of the variation in exam scores can be explained by the number of hours studied.
Interpreting the slope, which is 5, means that for every additional hour studied, the exam score in-
creases by 5 points on average. The y-intercept is 45, which suggests that if a student studies zero hours,
the predicted exam score would be 45. While this is a simplification, it provides a baseline for under-
standing performance without study.
I am confident in this model because the high r and r² values indicate a strong, reliable linear relation-
ship. However, other factors like test anxiety or prior knowledge might also influence exam scores,
which the model doesn’t capture.
For a prediction, suppose a student studies for 5.5 hours. Using the regression equation:
