Stat 588

Advanced Statistical Quality Control - Fall 2013

Textbook



Introduction to Statistical Quality Control 7th Edition

by Douglas C. Montgomery

ISBN 978-1-118-14681-1


Course Overview

  1. Concepts of quality and quality improvement
  2. Review of fundamental statistics
  3. Control charts for variables and attributes
  4. Process capability analysis
  5. Alternative control charts (CUSUM, EWMA)
  6. Multivariate control charts
  7. Design of experiments
  8. Acceptance sampling


Codes and Supplementary Materials

  1. Summary statistics, stem plot, histogram, and probability plot in R
  2. Generating random variables in R
  3. Performing basic statistical tests in R
  4. Creating control charts using Minitab 16
  5. qcc: An R package for quality control charting and statistical process control
  6. Creating x bar and R control charts in R
  7. Creating I-MR, CUSUM, EWMA Charts in R
  8. Creating p, np, c charts
  9. Creating Multivariate Control Charts in Minitab
  10. Multivariate monitoring using regression adjustment and principal components analysis

Homework 1        

Key                                                  

Chapter 3, Pages 103-107 of ISQC (text)

Exercises

3.9, 3.14, 3.17, 3.20, 3.29, 3.34, 3.38, 3.43, 3.46, 3.62

Due: Wed, Aug 28

Homework 2

Chapter 6 Exercises (ISQC)

5.24, 5.32  pages 232-233

6.4, 6.5, 6.10, 6.8, 6.13, 6.15, 6.20, 6.21  pages 280-284

Due: Wed, Sept 11

Homework 3

Chapter 6

6.22, 6.66, 6.67  pages 284, 285, 292

Chapter 9

9.8, 9.9, 9.16, 9.25, 9.26, 9.34, 9.40  pages 445-446

Bonus: 9.37, 9.38

Due: Wed, Sept 25

Homework 4

Chapter 7

7.5, 7.6, 7.10, 7.20, 7.22, 7.26, 7.64, 7.65, 7.70, 7.81

Due: Oct 16 Wednesday

Homework 5

Chapter 8

8.6, 8.7, 8.8, 8.15, 8.22, 8.24, 8.48

Chapter 11

11.2, 11.11, 11.12

Due: Oct 30 (Wednesday)

Homework 6

Chapter 11

11.7, 11.18 a-b, 11.19, 11.20, 11.21

Chapter 13

13.2, 13.4, 13.6, 13.12, 13.17

Due: Nov 20 Wed



Learning R

R is a powerful open source (free) software for statistical computing and graphics. It can run on both Windows and MacOS platforms and has a very active and friendly support community online.

RStudio is an integrated development environment (IDE) for R. It’s basically a nice front-end for R, giving you a console, a scripting window, a graphics window, and an R workspace, among other options.

Install R and RStudio in Windows

  1. Download R from http://cran.us.r-project.org/ (click on “Download R for Windows” > “base” > “Download R 3.x.x for Windows”)
  2. Install R. Leave all default settings in the installation options.
  3. Download RStudio from http://rstudio.org/download/desktop and install it. Leave all default settings in the installation options.
  4. Open RStudio.

Install R and RStudio in Mac OS X

  1. Download R from http://cran.us.r-project.org/ (click on “Download R for Mac OS X” > “R-3.x.x.pkg (latest version)”)
  2. Install R.
  3. Download RStudio from http://rstudio.org/download/desktop.
  4. Install RStudio by dragging the application icon to your Applications folder.
  5. Open RStudio.

Reading Data in R

R works most easily with datasets stored as csv files. Typically, values in csv files are separated, or delimited, by commas or by tabs/spaces (TXT files).

Base R functions read.csv and  read.table can read in data stored as CSV/TXT files, delimited by almost anything (notice the sep = option for read.table).

The # character at the beginning of a line signifies a comment, which is not executed.

Also, R is a case sensitive software. STAT, Stat and stat are three different objects.

To retrieve files over the internet, just type the URL of the file inside the R function read.csv or  read.table enclose by quotation marks. For example, the URL for the data sets used in Chap 3 is http://www.siue.edu/~jpailde/S588/Chap_03.csv.
  
   data.csv <- read.csv("http://www.siue.edu/~jpailde/S588/Chap_03.csv")

We can also retrieve data set saved on a drive.

Note how we assign the loaded data to object data.csv.

To see the first few rows of the object data.csv, type the function head(data.csv). You can also see the last few rows by  tail(data.csv).  If you just want the variable names, type colnames(data.csv).

   head(data.csv)
##  Ex3.1  Ex3.2 Ex3.3 Ex3.4 Ex3.5 Ex3.7 Ex3.8 Ex3.9 Ex3.10 Ex3.16 Ex3.17
## 1 16.05 50.001    21     6   953    96   127  94.1   13.3   8078   0.78
## 2 16.03 50.002   136    26   955   102   125  93.2   14.5   1891   9.59
## 3 16.02 49.998   185     8   948   104   131  90.6   15.3  13912   2.26

Once read in, datasets in R are usually stored as data frames, which have a matrix structure. Observations are arranged in rows, columns, and cells in a data frame can be accessed through many methods of indexing, such as object$column for a column.