Stat 498 B
Industrial Statistics



Grades
Instructor:
  • Fritz Scholz
  • Office: Padelford C-310
  • Office Hours: Tu 1:00pm-2:00pm, Th 3:00pm-4:00pm or by appointment.
  • Office Phone: 206-543-3866
  • fscholz at u dot washington dot edu (best for messages)


    Initial Course Announcement:

    Syllabus

    Homework 1, due 4-10-2008.
    Homework 1 Solution

    Homework 2, due 4-17-2008.
    Homework 2 Solution

    Homework 3, due 4-24-2008.
    Homework 3 Solution

    Homework 4, due 5-1-2008.
    Homework 4 Solution

    Homework 5, due 5-8-2008.
    Homework 5 Solution

    Homework 6, due 5-15-2008.
    Homework 6 Solution

    Homework 7, due 5-22-2008.
    Homework 7 Solution

    Homework 8, due 5-29-2008.
    Homework 8 Solution

    Homework 9, due 6-5-2008.
    Homework 9 Solution




    Lectures: Tu/Th 9:00-10:20 Padelford C-301
    Text (optional): Peter Dalgaard, Introductory Statistics with R.
    See also the many free introductory guides at the bottom of this page.

    Class Notes and Other Background Material:

    Confidence Bounds & Intervals for Parameters Relating
    to the Binomial, Negative Binomial, Poisson and Hypergeometric Distributions.
    Last modified 11/17/2019

    Applications of the Noncentral t-Distribution.
    Last modified 5/7/2008

    Maximum Likelihood Estimation for Type I Censored Weibull Data Including Covariates.
    Revised January 5, 2001

    Weibull Probability Paper.
    Last modified 4/8/2008

    Inference for the Weibull Distribution.
    Last modified 5/22/2008

    More detailed description of the actuator example.
    Last modified 4/17/2007

    Technical Report: Tolerance Stack Analysis Methods, A Critical Review.
    Fritz Scholz, November 1995.

    Technical Report: Tolerance Stack Analysis Methods.
    Fritz Scholz, December 1995.

    Technical Report: The Bootstrap Small Sample Properties.
    Fritz Scholz, March 1993.



    Lecture Slides:

    Introduction/Review of R.
    Last modified 3/5/2008

    Binomial/Poisson/Hypergeometric Confidence Bounds & Intervals.
    Last modified 4/18/2008

    Uses of the Noncentral t-Distribution.
    Last modified 5/7/2008

    Weibull Analysis.
    Last modified 6/4/2008

    Statistical Tolerancing.
    Last modified 5/22/2007

    The Bootstrap Method.
    Last modified 5/30/2008


    Reprints:

    Tolerance Bounds and Cpk Confidence Bounds Under Batch Effects
    R work spaces:

    R work space for Binomial/Poisson/Hypergeometric Confidence Bounds & Intervals,
    place this in its own directory, so that you don't step on other R work spaces.
    Make sure you save this work space under the name name.RData (name is your choice).

    R work space for Noncentral t-Distribution Applications
    place this in its own directory, so that you don't step on other R work spaces.
    Make sure you save this work space under the name name.RData (name is your choice).

    R work space for Weibull Distribution Applications
    place this in its own directory, so that you don't step on other R work spaces.
    Make sure you save this work space under the name name.RData (name is your choice).

    R work space for Random Curves
    place this in its own directory, so that you don't step on other R work spaces.
    Make sure you save this work space under the name name.RData (name is your choice).

    R work space for Statistical Tolerancing
    place this in its own directory, so that you don't step on other R work spaces.
    Make sure you save this work space under the name name.RData (name is your choice).

    R work space for Bootstrap Method
    place this in its own directory, so that you don't step on other R work spaces.
    Make sure you save this work space under the name name.RData (name is your choice).

    Grades: Homework 100%.
    Assignments follow below.

    Submit them electronically or on paper, written legibly!
    Approximate Schedule:

    Week   1 : Binomial confidence bounds and intervals (Clopper-Pearson)
                        Coverage properties and comparison with other methods. Examples.
    Week   2 : Negative binomial confidence bounds and intervals.
                        Poisson confidence bounds and intervals. Examples.
    Week   3 : Hypergeometric confidence bounds and intervals.
                        Bounds and Intervals for ratio of Poisson means. Examples.
    Week   4 : Noncentral t-distribution, properties, power calculation
                        sample size planning. Variables Acceptance Sampling Plans.
    Week   5 : Tolerance bounds, confidence bounds for tail probabilities,
                        quality indices and coefficients of variation.
    Week   6 : Tolerance bounds under batch effects.
    Week   7 : Tolerance bounds in the regression model.
    Week   8 : Statistical Tolerancing, Introduction
                        Central Limit Theorem and linear tolerance stackup.
                        Tolerancing with part variation different from normal.
                        Simulation approach. Linearization.
    Week   9 : Tolerancing under mean shifts.
                        Examples: Actuator and Voltage Amplifier.
    Week 10 : The Bootstrap Method
    R Packages:
  • Package nortest nortest_1.0.zip .
    R Installation Info and Introductory Guides:
    R Primer by Chris Green
    An Introduction to R by W.N. Venables and D.M. Smith and the R Development Core Team
    SimpleR by John Verzani
    Mathematical Annotations in R by Paul Murrell and Ross Ihaka

    For many more guides see R Contributed Documentation


    The R Project for Statistical Computing
    The Comprehensive R Archive Network

    For more documentation on R see:
    Manuals, FAQs, Newsletter, Wiki, Books,

    Home page of Fritz Scholz