Linear regression how to use scipy linear regress

Regression is when we want to examine relationship between variables

Linear regression uses the least square method.

The concept is to draw a line through all the plotted data points. The line is positioned in a way that it minimizes the distance to all of the data points.

The distance is called "residuals" or "errors".

Below are the main steps involved 

Import the modules you need: Pandas, matplotlib and Scipy

Isolate Average_Pulse as x. Isolate Calorie_burnage as y

Get important key values with: slope, intercept, r, p, std_err = stats.linregress(x, y)

Create a function that uses the slope and intercept values to return a new value. This new value represents where on the y-axis the corresponding x value will be placed

Run each value of the x array through the function. This will result in a new array with new values for the y-axis: mymodel = list(map(myfunc, x))

Draw the original scatter plot: plt.scatter(x, y)

Draw the line of linear regression: plt.plot(x, mymodel)

Define maximum and minimum values of the axis

Label the axis: "Average_Pulse" and "Calorie_Burnage"

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()

References:

https://www.w3schools.com/datascience/ds_linear_regression.asp