The term **bivariate analysis **refers to the analysis of two variables. You can remember this because the prefix “bi” means “two.”

The purpose of bivariate analysis is to understand the relationship between two variables

There are three common ways to perform bivariate analysis:

**1.** Scatterplots

**2.** Correlation Coefficients

**3.** Simple Linear Regression

The following example shows how to perform each of these types of bivariate analysis in Python using the following pandas DataFrame that contains information about two variables: **(1)** Hours spent studying and **(2)** Exam score received by 20 different students:

import pandas as pd #create DataFrame df = pd.DataFrame({'hours': [1, 1, 1, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 5, 5, 6, 6, 6, 7, 8], 'score': [75, 66, 68, 74, 78, 72, 85, 82, 90, 82, 80, 88, 85, 90, 92, 94, 94, 88, 91, 96]}) #view first five rows of DataFrame df.head() hours score 0 1 75 1 1 66 2 1 68 3 2 74 4 2 78

**1. Scatterplots**

We can use the following syntax to create a scatterplot of hours studied vs. exam score:

import matplotlib.pyplot as plt #create scatterplot of hours vs. score plt.scatter(df.hours, df.score) plt.title('Hours Studied vs. Exam Score') plt.xlabel('Hours Studied') plt.ylabel('Exam Score')

The x-axis shows the hours studied and the y-axis shows the exam score received.

From the plot we can see that there is a positive relationship between the two variables: As hours studied increases, exam score tends to increase as well.

**2. Correlation Coefficients**

A Pearson Correlation Coefficient is a way to quantify the linear relationship between two variables.

We can use the **corr()** function in pandas to create a correlation matrix:

#create correlation matrix df.corr() hours score hours 1.000000 0.891306 score 0.891306 1.000000

The correlation coefficient turns out to be **0.891**. This indicates a strong positive correlation between hours studied and exam score received.

**3. Simple Linear Regression**

Simple linear regression is a statistical method we can use to quantify the relationship between two variables.

We can use the **OLS()** function from the statsmodels package to quickly fit a simple linear regression model for hours studied and exam score received:

import statsmodels.api as sm #define response variable y = df['score'] #define explanatory variable x = df[['hours']] #add constant to predictor variables x = sm.add_constant(x) #fit linear regression model model = sm.OLS(y, x).fit() #view model summary print(model.summary()) OLS Regression Results ============================================================================== Dep. Variable: score R-squared: 0.794 Model: OLS Adj. R-squared: 0.783 Method: Least Squares F-statistic: 69.56 Date: Mon, 22 Nov 2021 Prob (F-statistic): 1.35e-07 Time: 16:15:52 Log-Likelihood: -55.886 No. Observations: 20 AIC: 115.8 Df Residuals: 18 BIC: 117.8 Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ const 69.0734 1.965 35.149 0.000 64.945 73.202 hours 3.8471 0.461 8.340 0.000 2.878 4.816 ============================================================================== Omnibus: 0.171 Durbin-Watson: 1.404 Prob(Omnibus): 0.918 Jarque-Bera (JB): 0.177 Skew: 0.165 Prob(JB): 0.915 Kurtosis: 2.679 Cond. No. 9.37 ==============================================================================

The fitted regression equation turns out to be:

Exam Score = 69.0734 + 3.8471*(hours studied)

This tells us that each additional hour studied is associated with an average increase of **3.8471** in exam score.

We can also use the fitted regression equation to predict the score that a student will receive based on their total hours studied.

For example, a student who studies for 3 hours is predicted to receive a score of **81.6147**:

- Exam Score = 69.0734 + 3.8471*(hours studied)
- Exam Score = 69.0734 + 3.8471*(3)
- Exam Score = 81.6147

**Additional Resources**

The following tutorials provide additional information about bivariate analysis:

An Introduction to Bivariate Analysis

5 Examples of Bivariate Data in Real Life

An Introduction to Simple Linear Regression

An Introduction to the Pearson Correlation Coefficient