The **Goldfeld-Quandt test** is used to determine if heteroscedasticity is present in a regression model.

Heteroscedasticity refers to the unequal scatter of residuals at different levels of a response variable in a regression model.

If heteroscedasticity is present, this violates one of the key assumptions of linear regression that the residuals are equally scattered at each level of the response variable.

This tutorial provides a step-by-step example of how to perform the Goldfeld-Quandt test in Python.

**Step 1: Create the Dataset**

For this example, let’s create the following pandas DataFrame that contains information about hours studied, prep exams taken, and final exam score received by 13 students in some class:

import pandas as pd #create DataFrame df = pd.DataFrame({'hours': [1, 2, 2, 4, 2, 1, 5, 4, 2, 4, 4, 3, 6], 'exams': [1, 3, 3, 5, 2, 2, 1, 1, 0, 3, 4, 3, 2], 'score': [76, 78, 85, 88, 72, 69, 94, 94, 88, 92, 90, 75, 96]}) #view DataFrame print(df) hours exams score 0 1 1 76 1 2 3 78 2 2 3 85 3 4 5 88 4 2 2 72 5 1 2 69 6 5 1 94 7 4 1 94 8 2 0 88 9 4 3 92 10 4 4 90 11 3 3 75 12 6 2 96

**Step 2: Fit Linear Regression Model**

Next, we’ll fit a multiple linear regression model using **hours** and **exams** as the predictor variables and **score** as the response variable:

**import statsmodels.api as sm
#define predictor and response variables
y = df['score']
x = df[['hours', 'exams']]
#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: 0.718
Model: OLS Adj. R-squared: 0.661
Method: Least Squares F-statistic: 12.70
Date: Mon, 31 Oct 2022 Prob (F-statistic): 0.00180
Time: 09:22:56 Log-Likelihood: -38.618
No. Observations: 13 AIC: 83.24
Df Residuals: 10 BIC: 84.93
Df Model: 2
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 71.4048 4.001 17.847 0.000 62.490 80.319
hours 5.1275 1.018 5.038 0.001 2.860 7.395
exams -1.2121 1.147 -1.057 0.315 -3.768 1.344
==============================================================================
Omnibus: 1.103 Durbin-Watson: 1.248
Prob(Omnibus): 0.576 Jarque-Bera (JB): 0.803
Skew: -0.289 Prob(JB): 0.669
Kurtosis: 1.928 Cond. No. 11.7
==============================================================================
**

**Step 3: Perform the Goldfeld-Quandt test**

Next, we will use the **het_goldfeldquandt()** function from **statsmodels** to perform the Goldfeld-Quandt test.

**Note**: The Goldfeld-Quandt test works by removing some number of observations located in the center of the dataset, then testing to see if the spread of residuals is different from the resulting two datasets that are on either side of the central observations.

Typically we choose to remove around 20% of the total observations. In this case, we can use the **drop** argument to specify that we’d like to remove 20% of observations:

#perform Goldfeld-Quandt test sm.stats.diagnostic.het_goldfeldquandt(y, x, drop=0.2) (1.7574505407790355, 0.38270288684680076, 'increasing')

Here is how to interpret the output:

- The test statistic is
**1.757**. - The corresponding p-value is
**0.383**.

The Goldfeld-Quandt test uses the following null and alternative hypotheses:

**Null (H**: Homoscedasticity is present._{0})**Alternative (H**Heteroscedasticity is present._{A}):

Since the p-value is not less than 0.05, we fail to reject the null hypothesis.

We do not have sufficient evidence to say that heteroscedasticity is a problem in the regression model.

**What To Do Next**

If you fail to reject the null hypothesis of the Goldfeld-Quandt test then heteroscedasticity is not present and you can proceed to interpret the output of the original regression.

However, if you reject the null hypothesis, this means heteroscedasticity is present in the data. In this case, the standard errors that are shown in the output table of the regression may be unreliable.

There are a couple common ways that you can fix this issue, including:

**1. Transform the response variable.**

You can try performing a transformation on the response variable, such as taking the log, square root, or cube root of the response variable. Typically this can cause heteroscedasticity to go away.

**2. Use weighted regression.**

Weighted regression assigns a weight to each data point based on the variance of its fitted value. Essentially, this gives small weights to data points that have higher variances, which shrinks their squared residuals.

When the proper weights are used, weighted regression can eliminate the problem of heteroscedasticity.

**Additional Resources**

The following tutorials explain how to perform other common operations in Python:

How to Perform OLS Regression in Python

How to Create a Residual Plot in Python

How to Perform White’s Test in Python

How to Perform a Breusch-Pagan Test in Python