Description:
Bivariate analysis refers to the statistical analysis of two variables to understand the
relationship between them. This type of analysis is essential for identifying correlations, trends,
and patterns that might not be immediately obvious when considering each variable independently.
It can be performed using different types of data visualizations and statistical methods to explore
the interaction between two variables.
In this guide, we will focus on methods to perform bivariate analysis on continuous and categorical
data, using Python libraries like Pandas, Matplotlib, Seaborn, and SciPy. This documentation includes
step-by-step guidance, code examples, and the visualization techniques typically used in bivariate analysis.
Step by Step Process
Step 1: Data Collection
Gather the dataset that contains the variables of interest.
Step 2: Data Preprocessing
Clean the data by handling missing values, outliers, and ensuring the data types are appropriate.
Step 3: Exploratory Data Analysis (EDA)
Perform a preliminary analysis to understand the data distribution and key statistics.
Step 4: Correlation Analysis
Compute the correlation coefficient (e.g., Pearson’s correlation) to measure the linear relationship between two continuous variables.
Step 5: Visualization
Use appropriate plots to visualize the relationship between the variables (scatter plot, box plot, heatmap, etc.).
Step 6: Statistical Testing
Perform hypothesis testing or statistical tests (e.g., t-test, chi-squared test) for more in-depth analysis.
Step 7: Interpretation
Analyze the results, draw conclusions, and determine whether any further analysis or modeling is required.
Sample Code
# Importing required libraries
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import pearsonr
# Step 2: Visualizations
# Scatter plot to visualize the relationship
plt.figure(figsize=(8, 6))
sns.scatterplot(data=df, x='Horsepower', y='Fuel_Efficiency')
plt.title('Scatter Plot: Horsepower vs Fuel Efficiency')
plt.xlabel('Horsepower')
plt.ylabel('Fuel Efficiency (MPG)')
plt.show()
# Adding a new column for Horsepower Ranges
df['Horsepower_Range'] = pd.cut(df['Horsepower'], bins=[100, 130, 160, 200], labels=['Low', 'Medium', 'High'])
# Box plot to visualize fuel efficiency distribution based on horsepower ranges
plt.figure(figsize=(8, 6))
sns.boxplot(x='Horsepower_Range', y='Fuel_Efficiency', data=df)
plt.title('Box Plot: Fuel Efficiency by Horsepower Range')
plt.show()
Research Breakthrough Possible @S-Logix
