Comparison of Multiple Groups OneWay Analysis of Variance Examples
Ash Genaidy
Case Study
Plastic containers for motor oil are blow molded in a machine that has two feeders, each feeding into three molding stations (a total of six stations). Plastic is ex

Simple Linear Regression And Correlation Examples
Ash Genaidy
Case Study
A
laboratory tested tires for tread wear. Tires of a certain brand are mounted on a car. The tires were rotated from position to position every 1000 miles, and the groove depth was m

Basic Probability and Statistics Review Six Sigma Black Belt Primer
Pat Hammett, Ph.D.
January 2003
Instructor Comments: This document contains a review of basic probability and statistics. It also includes a practice test at the end of the document. Note

Six Sigma and the Microsoft Case Study
Jennifer Ho IE 524 December 2003
1
Table of Contents Title Page.1 Table of Contents.2 What is Six Sigma?.3 The History of Six Sigma.3 Statistical Definition of Six Sigma.4 The difference between 3 sigma and 6 sigma.5

Quality Engineering, 14(4), 659671 (2002)
The Role of Statistical Design of Experiments in Six Sigma: Perspectives of a Practitioner
T. N. Goh*
Industrial and Systems Engineering Department, National University of Singapore, Singapore 119260
ABSTRACT Six

Six sigma and introductory statistics education
John Maleyeff and Frank C. Kaminsky
The authors John Maleyeff is an Associate Professor with the Lally School of Management and Technology, Rensselaer Polytechnic Institute, Hartford, Connecticut, USA Frank

Variables and Measurement
Team Flying Camel: Ryan Bauer, Andy Brown, Vail Burns, Brian Hill, Justin Bending
Measurements
Collect data for statistical analysis Measurements of variables are made using many different tools
Variables
Quantitative Numerical

The Role of Statistical Design of Experiments in Six Sigma: Perspectives of a Practitioner by T. N. Goh
Summarized by Group 2
DMAIC
Define Measure Analyze Improve Control
DEFINE
Project selection Impact and benefit analysis Project roadmapping
MEAS

Six Sigma and Introductory Statistics Education
Michael Campbell Andrew Arata Joe Horning Matt Fairman Brian Dixon
Statistics in Contemporary Business vs. Statistics in School
Businesses are embracing programs like Six Sigma and TQM Students arent being

Six Sigma
Team 7 Tom Tsiominas Nathan Schmucker Jerold Murray
What is Six Sigma
Measure of quality striving for near perfection 6 standard deviations between the mean and nearest specification limit < 3.4 defects per million Measurement based strategy
Si

Six sigma: A breakthrough strategy for profitability
Mikel J Harry Quality Progress; May 1998; 31, 5; Research Library pg. 60
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced with permission

Simple Linear Regression and Correlation
Ash Genaidy
Introduction
This topic introduce methods for analyzing relationships between pairs of quantitative variables. In this regard, three inter-related issues should be considered
Whether an association

Statistical Inference: Comparison of Multiple Groups - One-way Analysis of Variance Method
Ash Genaidy
Introduction
In
the previous topic, we compared the means of two groups. We next extend those methods to the comparison of means of a quantitative resp

Comparison of Two Groups Examples
Ash Genaidy
Large-Sample Test for the Mean -Case Study
Plastic containers for motor oil are blow molded in a machine that has two feeders, each feeding into two three molding stations( a total of six stations). Plastic is

Introduction to Statistical Methodology
Ash Genaidy
Statistics
In
todays world, an understanding of statistics is essential in many professions across the spectrum from medicine to engineering. Statistics consists of a body of methods for collecting and

Sampling and Measurement
Ash Genaidy
Introduction
The
ultimate goals of any engineering study are to understand, explain and make inferences about engineering phenomena. To do this, we need data.
Descriptive statistical methods provide ways of summariz

Descriptive Statistics - Examples
Ash Genaidy
Case Study
Most
cases of lower back pain are mild and transient, requiring temporary activity modification. About 5% of the cases become chronic and disabling. When conservative measures fail, surgeons may re

Descriptive Statistics
Ash Genaidy
Introduction
Statistical
analysis consists of two
elements:
Descriptive statistics Inferential statistics
Descriptive
statistics are ways of describing a sample.
Topic Outline
Tabular
and graphical methods Measures

Statistical Inference Estimation
Ash Genaidy
Introduction
This
topic deals with how to use sample data to estimate population parameters, focusing on:
The population mean () for quantitative variables. The population proportion () for qualitative varia

Probability Distributions
Ash Genaidy
Topic Outline
Probability distributions likelihoods for possible outcomes of a variable. Normal distributions bell-shaped curve that is the most important probability distribution for statistical analysis. Sampling

Large Sample Estimation Examples
Ash Genaidy
Large Sample Estimation for the Mean
Calculate
a 95% confidence interval for the following age information mean = 43.66 yr sd = 9.2 yr confidence interval = Y Z ( sd n ) = 43.66 Z (9.2 35 ) calculate Z
T table

Statistical Inference Significance Tests
Ash Genaidy
Introduction
A
common goal of many engineering investigations is to check whether the data agree with certain prediction. These predictions are hypotheses about variables measured in the study.
A hypoth

Significance Tests for Single Samples Examples
Ash Genaidy
Case study example 1
Is the average back pain at baseline significantly lower from 10 (worst imaginable pain)? Assume type I error =0.05 Mean =7.83; sd =1.8; N=35 H0: =10 0 Y 10 7.83 2.17 Z= = =

Statistical Inference Comparison of Two Groups
Ash Genaidy
Introduction
Comparing some characteristic of two groups is a fundamental analysis in engineering studies. The outcome about which comparisons are made is called the response variable. The variabl

Non-statistical skills that can help statisticians be more effective
Ronald D Snee Total Quality Management; Dec 1998; 9, 8; Research Library pg. 711
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Re