To determine which class has greater variation in scores, we calculate the standard deviation for the scores of each class. Standard deviation measures how spread out the numbers are from the mean.

Here are the scores:

• Class A: 85, 90, 78, 92, 88
• Class B: 70, 75, 80, 85, 60

Steps to Calculate Standard Deviation:

1. Calculate the mean (average) of the scores.
2. Subtract the mean from each score to find deviations.
3. Square each deviation.
4. Find the mean of the squared deviations.
5. Take the square root of that mean.

Let me calculate the standard deviation for both classes.import numpy as np

Scores for each class

class_a_scores = [85, 90, 78, 92, 88] class_b_scores = [70, 75, 80, 85, 60]

Calculate standard deviation for each class

std_dev_class_a = np.std(class_a_scores, ddof=0) std_dev_class_b = np.std(class_b_scores, ddof=0)

std_dev_class_a, std_dev_class_b(4.882622246293481, 8.602325267042627)(4.882622246293481, 8.602325267042627)successThe standard deviations are as follows:

• Class A: 4.88
• Class B: 8.60

Conclusion:

Class B has a greater variation in scores compared to Class A, as its standard deviation is significantly higher.

Would you like a detailed step-by-step explanation of these calculations or help with another query?

Here are 5 related questions for further exploration:

1. What does a higher standard deviation indicate about a dataset?
2. How can we calculate the variance, and how is it related to standard deviation?
3. What is the impact of outliers on the standard deviation?
4. Can we compare variation using other metrics like range or interquartile range?
5. How does normalizing the scores affect the variation?

Tip: Standard deviation is sensitive to extreme values; always check for outliers before drawing conclusions about data spread.