16 Statistical Process Control in Python

packages

# Remember to install these packages using a terminal, if you haven't already!
!pip install pandas plotnine scipy

we will experiment pandas For data manipulation, plotnine For visualization, and scipy For statistical purposes.

import pandas as pd
from plotnine import *

custom work

This workshop uses custom functions functions/ Directory. You may need both:- functions_distributions.py – For reliability and delivery functions – functions_process_control.py – for statistical process control tasks

To use these functions, you need to get them from the repository at github.com/timothyfraser/sigma/tree/main/functions.

Add functions directory to your python path

import sys
import os
# Add the functions directory to Python path
sys.path.append('functions')  # or path to wherever you placed the functions folder

Once you have the functions available, you can import them:

from functions_distributions import density, tidy_density, approxfun
# from functions_process_control import ggprocess, ggsubgroup, ggmoving, ggcapability  # if needed

our case

For today’s workshop, we will consider why quality control matters in a local economy by examining the case of the Japanese hot springs bath economy! hot springs, or onsenHot springs are a major source of tourism and recreation for families in Japan, drawing residents from across the country each year, often to rural communities where the perfect geological conditions produce naturally occurring hot springs. Restaurants, taxi and bus companies, and many service sector companies rely on their local efforts to bring a steady stream of tourists into the local economy. Therefore, it is often in the best interest onsen To ensure quality control, operators must monitor the temperature, minerals, or other aspects of their hot spring baths in order to maintain their company’s (and city’s!) reputation for quality rest and relaxation!

Onsen-Those who visit often seek Specific Types of Hot Springs, Why It’s Important onsen Really delivering what it advertises! Sarbulia and Payyappallimana (2012) describe some of these benchmarks.

• temperature: Onsen are divided into “additional hot springs” (>42°C), “hot Springs” (41~34°C), and “Warm Springs” (33~25°C,

• pH: Onsen is classified as “acidic” (pH < 3), “mildly acidic” (pH 3~6), “neutral” (pH 6~7.5), “mild alkaline” (pH 7.5~8.5), and “alkaline” (pH > 8.5,

• sulfur:sulphur onsen There is usually about 2 mg of sulfur per 1 kg of hot spring water; sulfur levels Sure More than 1 mg counts as sulfur onsen. (It smells like rotten eggs!)

These are good examples of quality control metrics onsen Operators might want to keep an eye out!

Figure 4.1: Monkeys are onsen fans too! Reading more here!

our data

You have been assigned to evaluate quality control at the local level onsen In sunny Kagoshima Prefecture! Every month, for 15 months, you systematically took 20 random samples of hot spring water and recorded it temperature, pHAnd sulfur level. How can you determine that this onsen There is a danger of moving out of one sector of the market (e.g. extra hot!) into another (just normal hot springs?).

Let’s read into our data workshops/onsen.csv,

# Add functions directory to path if not already there
import sys
if 'functions' not in sys.path:
    sys.path.append('functions')

from functions_distributions import density, tidy_density, approxfun

water = pd.read_csv('workshops/onsen.csv')
water.head(3)

##    id  time  temp   ph  sulfur
## 0   1     1  43.2  5.1     0.0
## 1   2     1  45.3  4.8     0.4
## 2   3     1  45.5  6.2     0.9