TI-Nspire Python: Linear Regression
Introduction
The following is a simple script to fit bivariate data using linear regression:
y = ax + b
a = slope
b = y-intercept
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TI-Nspire Python Script: linreg.py
# 2020-12-31 EWS
# linear regression
from math import *
# enter and calculate data
xlist=[]
ylist=[]
n=int(input('n? '))
nb=0
sx=0
sx2=0
sy=0
sy2=0
sxy=0
for i in range(n):
nb=nb+1
print("Data Point "+str(nb))
x=float(input('x? '))
y=float(input('y? '))
sx=sx+x
sy=sy+y
sx2=sx2+x**2
sy2=sy2+y**2
sxy=sxy+x*y
xlist.append(x)
ylist.append(y)
# slope
a=(sxy-sx*sy/nb)/(sx2-sx**2/nb)
# y-intercept
b=sy/nb-a*sx/nb
# correlation
r=(sxy-sx*sy/nb)/(sqrt(sx2-sx**2/nb)*sqrt(sy2-sy**2/nb))
# results
print("y = ax + b")
print("a= "+str(a))
print("b= "+str(b))
print("r= "+str(r))
The following script adds a plot of both the real and predicted data.
TI-Nspire Python Script: linregplus.py
# 2020-12-31 EWS
# linear regression with plot
from math import *
import ti_plotlib as plt
# enter and calculate data
xlist=[]
ylist=[]
n=int(input('n? '))
nb=0
sx=0
sx2=0
sy=0
sy2=0
sxy=0
for i in range(n):
nb=nb+1
print("Data Point "+str(nb))
x=float(input('x? '))
y=float(input('y? '))
sx=sx+x
sy=sy+y
sx2=sx2+x**2
sy2=sy2+y**2
sxy=sxy+x*y
xlist.append(x)
ylist.append(y)
# slope
a=(sxy-sx*sy/nb)/(sx2-sx**2/nb)
# y-intercept
b=sy/nb-a*sx/nb
# correlation
r=(sxy-sx*sy/nb)/(sqrt(sx2-sx**2/nb)*sqrt(sy2-sy**2/nb))
# results
print("y = ax + b")
print("a= "+str(a))
print("b= "+str(b))
print("r= "+str(r))
# predictive list
plist=[]
for i in range(n):
p=a*xlist[i]+b
plist.append(p)
# plot routine
# clear the screen
plt.cls()
# automatically fits the screen to fit the data
plt.auto_window(xlist,ylist)
# display the axes
# "on" plots axes and endpoints
# "axes" just plots the axes
plt.axes("on")
# select color - use RGB style
# denim blue, real data
plt.color(21,96,189)
plt.plot(xlist,ylist,".")
# orange, predictive data
plt.color(255,165,0)
plt.plot(xlist,plist,".")
# plot the graph
plt.show_plot()
Eddie
All original content copyright, © 2011-2021. Edward Shore. Unauthorized use and/or unauthorized distribution for commercial purposes without express and written permission from the author is strictly prohibited. This blog entry may be distributed for noncommercial purposes, provided that full credit is given to the author.
