What is weighted least squares regression?
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# What is weighted least squares regression?

This recipe explains what is weighted least squares regression

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## Recipe Objective.

What is weighted least squares regression? How to perform it in python?

Weighted least squares regression is accustomed to correct for heteroscedasticity. During a Weighted regression procedure additional weight is given to the observations with smaller variance as a result of these observations give additional reliable info concerning the regression perform than those with massive variances.

## Step 1- Importing Libraries.

``` import pandas as pd import statsmodels.api as sm ```

## Step 2- Reading Dataset.

``` df= pd.read_csv('/content/sample_data/california_housing_train.csv') df.head() ```

## Step 3- Spliting the data.

We have to split the data in X and Y to fit it in the wls model.

``` Y=df['median_house_value'] X=df.drop(['median_house_value'], axis=1) ```

## Step 4- Fitting the model

We will fit the dataset into the model and print the summary.

``` wls_model = sm.WLS(Y,X) results = wls_model.fit() print(results.summary()) ```

If the weights square measure a operate of the info, then the post estimation statistics like fvalue and mse_model may not be correct, because the package doesn't nonetheless support no-constant regression.

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