Scatter plots with regression (regplot) in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of a simple regplot

What will be the output of this code snippet that uses seaborn's regplot to plot data points and a regression line?

Data Analysis Python

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

data = pd.DataFrame({'x': [1, 2, 3, 4, 5], 'y': [2, 4, 5, 4, 5]})
sns.regplot(x='x', y='y', data=data)
plt.show()

A A scatter plot with points roughly increasing and a regression line fitting the points upward

B A scatter plot with points randomly scattered and a flat horizontal regression line

C A bar chart showing counts of x values

D A line plot connecting the points without any regression line

💡 Hint

Think about what regplot does: it shows points and fits a regression line.

Seaborn's regplot plots scatter points and fits a linear regression line through them. Since y generally increases with x, the line slopes upward.

❓ data_output

intermediate

Number of points plotted by regplot

Given this dataset and regplot code, how many points will be shown on the scatter plot?

Data Analysis Python

import seaborn as sns
import pandas as pd

data = pd.DataFrame({'x': range(10), 'y': [i**2 for i in range(10)]})
sns.regplot(x='x', y='y', data=data)

A 10

B 11

C 9

D 0

💡 Hint

Count the number of rows in the data.

The data has 10 rows, so regplot will plot 10 points.

🔧 Debug

advanced

Error when using regplot with missing data

What error will this code raise when running regplot with missing values in the data?

Data Analysis Python

import seaborn as sns
import pandas as pd
import numpy as np

data = pd.DataFrame({'x': [1, 2, np.nan, 4], 'y': [2, 3, 4, 5]})
sns.regplot(x='x', y='y', data=data)

A TypeError because data is not numeric

B ValueError due to NaN values in x

C No error, plot shows ignoring NaN

D KeyError because 'x' column is missing

💡 Hint

Think about how regression handles missing values.

Seaborn's regplot automatically drops rows with NaN values in the x or y columns before fitting the regression. No error is raised; it plots the remaining 3 points.

❓ visualization

advanced

Effect of changing order parameter in regplot

What is the visual difference when using order=2 in regplot compared to the default order=1?

Data Analysis Python

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

data = pd.DataFrame({'x': range(6), 'y': [1, 4, 9, 16, 25, 36]})
plt.subplot(1,2,1)
sns.regplot(x='x', y='y', data=data, order=1)
plt.title('order=1')
plt.subplot(1,2,2)
sns.regplot(x='x', y='y', data=data, order=2)
plt.title('order=2')
plt.tight_layout()
plt.show()

A order=1 shows a curved line; order=2 shows a straight line

B order=1 shows a straight line; order=2 shows a curved parabola fitting the data better

C Both plots show identical straight lines

D order=2 plot raises an error because order must be 1

💡 Hint

Order controls polynomial degree of regression line.

order=1 fits a linear regression line; order=2 fits a quadratic curve, which better fits the squared y values.

🚀 Application

expert

Interpreting regression line slope from regplot

Given this code plotting a regression line, what is the approximate slope of the regression line?

Data Analysis Python

import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt

data = pd.DataFrame({'x': [0, 1, 2, 3, 4], 'y': [1, 3, 5, 7, 9]})
sns.regplot(x='x', y='y', data=data)
plt.show()

A Approximately -1

B Approximately 1

C Approximately 0.5

D Approximately 2

💡 Hint

Look at how y changes when x increases by 1.

y increases by 2 for every increase of 1 in x, so slope is about 2.