Correlation analysis (Pearson, Spearman) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Pearson correlation calculation

What is the output of this Python code calculating Pearson correlation between two lists?

Data Analysis Python

import numpy as np
from scipy.stats import pearsonr
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
correlation, p_value = pearsonr(x, y)
print(round(correlation, 2))

A 0.50

B 0.80

C 1.00

D -1.00

💡 Hint

Pearson correlation measures linear relationship strength.

The lists y and x have a perfect linear relationship (y = 2*x), so Pearson correlation is 1.00.

❓ data_output

intermediate

Spearman correlation output for rank data

Given these two lists, what is the Spearman correlation coefficient output?

Data Analysis Python

from scipy.stats import spearmanr
x = [10, 20, 30, 40, 50]
y = [2, 1, 3, 5, 4]
correlation, _ = spearmanr(x, y)
print(round(correlation, 2))

A 0.70

B 0.60

C 0.90

D 0.80

💡 Hint

Spearman correlation measures monotonic relationship using ranks.

Spearman correlation here is about 0.80, reflecting moderate positive monotonic association.

🧠 Conceptual

advanced

Difference between Pearson and Spearman correlation

Which statement correctly describes the difference between Pearson and Spearman correlation?

A Pearson measures monotonic relationships; Spearman measures linear relationships.

B Pearson measures linear relationships; Spearman measures monotonic relationships using ranks.

C Both measure only linear relationships but use different formulas.

D Spearman correlation is only used for categorical data.

💡 Hint

Think about how each correlation handles data ordering and linearity.

Pearson correlation measures linear association between variables, while Spearman uses ranked data to measure monotonic relationships.

🔧 Debug

advanced

Identify the error in correlation calculation code

What error will this code raise when calculating Pearson correlation?

Data Analysis Python

from scipy.stats import pearsonr
x = [1, 2, 3]
y = [4, 5]
correlation, p = pearsonr(x, y)

A ValueError: x and y must have the same length

B TypeError: unsupported operand type(s)

C IndexError: list index out of range

D No error, code runs successfully

💡 Hint

Check if the input lists have the same number of elements.

Pearson correlation requires x and y to be the same length; here they differ, causing ValueError.

🚀 Application

expert

Choosing correlation method for non-linear but monotonic data

You have two variables with a clear monotonic but non-linear relationship. Which correlation method should you use to best capture their association?

A Spearman correlation

B Pearson correlation

C Both Pearson and Spearman will give the same result

D Neither Pearson nor Spearman is suitable

💡 Hint

Consider which method uses ranks and can detect monotonic relationships.

Spearman correlation captures monotonic relationships even if they are not linear, unlike Pearson.