pandas as pd from scipy.stats import pearsonr

Given data

math_scores = [45, 70, 65, 30, 90, 40, 50, 75, 85, 60] english_scores = [35, 90, 70, 40, 95, 40, 60, 80, 80, 50]

Calculating Pearson correlation coefficient

correlation_coefficient, _ = pearsonr(math_scores, english_scores) correlation_coefficientThe coefficient of correlation for the given data is approximately 0.903.

Interpretation:

A correlation coefficient of 0.903 indicates a very strong positive linear relationship between the marks in Mathematics and English. This implies that students who perform well in Mathematics are likely to perform well in English, and vice versa.

Would you like further details or calculations? Here are some related questions to deepen understanding:

1. How is the correlation coefficient calculated step by step?
2. What does a correlation coefficient value closer to 1 or -1 signify?
3. Can we conclude causation from this correlation coefficient?
4. What are some real-world implications of such a strong correlation in academic scores?
5. How would the correlation coefficient change if there was more variability in the data?

Tip: Always inspect a scatter plot of the data to visually confirm the nature of the relationship when calculating correlation.