Why scatter plots show relationships in Matplotlib - Challenge Your Understanding

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this scatter plot code?

Look at the code below that creates a scatter plot. What will the plot show about the relationship between x and y?

Matplotlib

import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]
plt.scatter(x, y)
plt.show()

A A scatter plot showing points in a straight line going up, indicating a positive linear relationship.

B A scatter plot showing points randomly scattered with no clear pattern.

C A scatter plot showing points in a straight line going down, indicating a negative linear relationship.

D A scatter plot showing points clustered at the origin with no spread.

💡 Hint

Look at how y changes as x increases.

The y values are exactly twice the x values, so the points form a straight line going up. This shows a positive linear relationship.

❓ data_output

intermediate

How many points are plotted in this scatter plot?

Given the data below, how many points will appear in the scatter plot?

Matplotlib

import matplotlib.pyplot as plt
x = [5, 3, 9, 1, 7]
y = [10, 6, 18, 2, 14]
plt.scatter(x, y)
plt.show()
num_points = len(x)

A 3

B 4

C 5

D 6

💡 Hint

Count the number of elements in the x list.

There are 5 elements in the x list, so 5 points will be plotted.

❓ visualization

advanced

Which scatter plot shows a negative relationship?

Below are four scatter plots created from different data sets. Which plot shows a negative relationship between x and y?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np

fig, axs = plt.subplots(2, 2, figsize=(8, 6))

# Plot A: Positive linear
x = np.arange(10)
y = 2 * x + 1
axs[0, 0].scatter(x, y)
axs[0, 0].set_title('A')

# Plot B: Negative linear
x = np.arange(10)
y = 10 - x
axs[0, 1].scatter(x, y)
axs[0, 1].set_title('B')

# Plot C: No relationship
x = np.random.rand(10)
y = np.random.rand(10)
axs[1, 0].scatter(x, y)
axs[1, 0].set_title('C')

# Plot D: Clustered points
x = np.ones(10)
y = np.linspace(0, 5, 10)
axs[1, 1].scatter(x, y)
axs[1, 1].set_title('D')

plt.tight_layout()
plt.show()

A Plot A

B Plot C

C Plot D

D Plot B

💡 Hint

Look for points that go down as x increases.

Plot B shows y decreasing as x increases, which is a negative linear relationship.

🧠 Conceptual

advanced

Why do scatter plots help show relationships between variables?

Which explanation best describes why scatter plots are useful to see relationships between two variables?

A Because scatter plots show individual data points, making it easy to see patterns like trends or clusters.

B Because scatter plots only show averages, hiding individual differences.

C Because scatter plots connect points with lines to show exact changes over time.

D Because scatter plots use bars to compare categories clearly.

💡 Hint

Think about what you see when you look at a scatter plot.

Scatter plots display each data point, so you can see how variables relate by spotting patterns or groupings.

🔧 Debug

expert

What error does this scatter plot code produce?

Look at the code below. What error will occur when running it?

Matplotlib

import matplotlib.pyplot as plt
x = [1, 2, 3]
y = [4, 5]
plt.scatter(x, y)
plt.show()

A TypeError: 'list' object is not callable

B ValueError: x and y must be the same size

C SyntaxError: invalid syntax

D No error, plot will display correctly

💡 Hint

Check if x and y have the same number of elements.

The x list has 3 elements but y has 2, so matplotlib raises a ValueError about size mismatch.