Plotting scatter plot and line of best fit using python (turtle and math modules). Run the code online: https://trinket.io/python/d43cc2dddf

labdata.txt

44 71
79 37
78 24
41 76
19 12
19 32
28 36
22 58
89 92
91 6
53 7
27 80
14 34
8 81
80 19
46 72
83 96
88 18
96 48
77 67

scatterplot.py

import turtle
import math

def main(datafile):
    dataset = retrieveData(datafile)

    wn = turtle.Screen()
    wn.setworldcoordinates(0,0,1,1)
    
    kurt = turtle.Turtle()
    
    plotRegression(kurt, dataset)
            
def retrieveData(filename):
    data = []
    with open(filename, 'r') as datafile:
        for line in datafile:
            datapoint = tuple([int(i) for i in line.split()])
            data.append(datapoint)
    return data
    
def plotRegression(t, dataset):
    plotPoints(t, dataset)
    m, c = bestfit(dataset)
    plotLine(t, m, c)

def plotPoints(t, dataset):
    t.up()
    for datapoint in dataset:
        point = tuple([cord/100 for cord in datapoint])
        t.goto(*point); t.dot()
    t.goto(0,0); t.down()

def plotLine(t, m, c):
    t.up(); t.goto(0, c); t.down()
    angle = math.degrees(math.atan(m))
    t.seth(angle)
    while t.xcor() < 1 and t.ycor() < 1:
        t.forward(1)

def bestfit(dataset):
    average = lambda a : sum(a)/len(a)
    x_vals, y_vals = zip(*dataset)
    
    sum_of_products = sum([x*y for x, y in dataset])
    sum_of_x_squares = sum([x*x for x in x_vals])
    
    x_mean = average(x_vals)
    y_mean = average(y_vals)
    
    n = len(dataset)
    
    m = ((sum_of_products - (n * x_mean * y_mean))/
        (sum_of_x_squares - (n * x_mean ** 2)))
    
    c = (y_mean - (m * x_mean))/100
  
    return m, c
    
if __name__ == '__main__':
    main('labdata.txt')