Linear Regression Using One Explanatory Variable

In this example, we will use Linear Regression to predict Calorie Burnage based on Average Pulse.

Example

import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats

full_health_data = pd.read_csv(“data.csv”, header=0, sep=“,”)

x = full_health_data[“Average_Pulse”]
y = full_health_data [“Calorie_Burnage”]

slope, intercept, r, p, std_err = stats.linregress(x, y)

def myfunc(x):
return slope * x + intercept

mymodel = list(map(myfunc, x))

plt.scatter(x, y)
plt.plot(x, slope * x + intercept)
plt.ylim(ymin=0, ymax=2000)
plt.xlim(xmin=0, xmax=200)
plt.xlabel(“Average_Pulse”)
plt.ylabel (“Calorie_Burnage”)
plt.show()

Example Explained:

Import the necessary modules: Pandas, matplotlib, and Scipy.
Isolate Average_Pulse as $x$ and Calorie Burnage as $y$ .
Calculate key values using: slope, intercept, r, p, std_err = stats.linregress(x, y).
Create a function that uses the slope and intercept to return a new value, indicating where the corresponding $x$ value will fall on the $y$ -axis.
Apply the function to each value in the $x$ array, resulting in a new array with updated $y$ -axis values: mymodel = list(map(myfunc, x)).
Plot the original scatter plot: plt.scatter(x, y).
Plot the linear regression line: plt.plot(x, mymodel).
Set the axis’s maximum and minimum values.
Label the axes as “Average_Pulse” and “Calorie_Burnage”.

Curriculum

Data Science

Data Science

DS Math

DS Statistics

DS Advanced

Linear Regression Using One Explanatory Variable

Example

Output: