Linear regression for biostatistics in Python

We will continue evaluating the diabetes dataset from previous chapters, but this time, we will be setting the research questions related to predictive biostatistics and creating biostatical models. To proceed, make sure you previously downloaded the diabetes dataset as in previous chapters at https://data.mendeley.com/datasets/wj9rwkp9c2/1.

First, let’s load the required libraries. Notice that we are loading the stats library from the scipy package. This library will be used to create the predictive models:

import pandas as pd
from scipy import stats
import statsmodels.formula.api as smf
import seaborn as sns
import matplotlib.pyplot as plt
# Load the data
data = pd.read_csv(r'C:\Users\MEDIN\Downloads\Dataset of Diabetes .csv')

Sometimes, in the datasets, there may be inconsistencies with lower- and uppercase letters. In this dataset, this is the case with one f versus F letter in the gender column. Here is how...