Learning Objectives. Understand how to select the correct control chart for an application. Know how to fill out and maintain a control chart.
|
|
- Barry Kelley
- 7 years ago
- Views:
Transcription
1 CONTROL CHARTS
2 Learning Objectives Understand how to select the correct control chart for an application. Know how to fill out and maintain a control chart. Know how to interpret a control chart to determine the occurrence of special causes of variation.
3 How does it help? Control charts are useful to: determine the occurrence of of special cause situations. Utilize the opportunities presented by special cause situations to to identify and correct the occurrence of of the special causes..
4 IMPROVEMENT ROADMAP Uses of Control Charts Characterization Phase 1: Measurement Phase 2: Analysis Common Uses Control charts can be effectively used to determine special cause situations in the Measurement and Analysis phases Breakthrough Strategy Phase 3: Improvement Optimization Phase 4: Control
5 KEYS TO SUCCESS Use control charts on only a few critical output characteristics Ensure that you have the means to investigate any special cause
6 What is a Special Cause? Remember our earlier work with confidence intervals? Any occurrence which falls outside the confidence interval has a low probability of occurring by random chance and therefore is significantly different. If we can identify and correct the cause, we have an opportunity to significantly improve the stability of the process. Due to the amount of data involved, control charts have historically used 99% confidence for determining the occurrence of these special causes Special cause occurrence. Point Value +/- 3s = 99% Confidence Band X
7 What is a Control Chart? A control chart is simply a run chart with confidence intervals calculated and drawn in. These Statistical control limits form the trip wires which enable us to determine when a process characteristic is operating under the influence of a Special cause. +/- 3s = 99% Confidence Interval
8 So how do I construct a control chart? First things first: Select the metric to to be evaluated Select the right control chart for the metric Gather enough data to to calculate the control limits Plot the data on the chart Draw the control limits (UCL & LCL) onto the chart. Continue the run, investigating and correcting the cause of of any out of of control occurrence.
9 How do I select the correct chart? Variable What type of data do I have? Attribute What subgroup size is available? Counting defects or defectives? n > 10 1 < n < 10 n = 1 X-s Chart X-R Chart Note: A defective unit can have more than one defect. IMR Chart Defectives Constant Sample Size? yes no Defects Constant Opportunity? yes no np Chart p Chart c Chart u Chart
10 How do I calculate the control limits? X R Chart For the averages chart: CL = X UCL = X + A R 2 LCL = X A R 2 n D4 D3 A For the range chart: CL = R UCL LCL = D4 = D3 R R X = average of the subgroup averages R = average of the subgroup range values = a constant function of subgroup size (n) A 2 UCL = upper control limit LCL = lower control limit
11 How do I calculate the control limits? p and np Charts For varied sample size: For constant sample size: UCLp = P + 3 P ( 1 P) n ( ) UCL = np + 3 np np 1 P LCL p = P 3 P ( 1 P) n ( ) LCL = np 3 np np 1 P Note: P charts have an individually calculated control limit for each point plotted P = number of rejects in the subgroup/number inspected in subgroup P = total number of rejects/total number inspected n = number inspected in subgroup
12 How do I calculate the control limits? c and u Charts For varied opportunity (u): For constant opportunity (c): UCLu = U +3 U n UCL = C C +3 C LCL u = U 3 U n LCLC = C 3 C Note: U charts have an individually calculated control limit for each point plotted C = total number of nonconformities/total number of subgroups U = total number of nonconformities/total units evaluated n = number evaluated in subgroup
13 How do I interpret the charts? The process is said to be out of control if: One or more points fall outside of the control limits When you divide the chart into zones as shown and: 2 out of 3 points on the same side of the centerline in Zone A 4 out of 5 points on the same side of the centerline in Zone A or B 9 successive points on one side of the centerline 6 successive points successively increasing or decreasing 14 points successively alternating up and down 15 points in a row within Zone C (above and/or below centerline) Zone A Zone B Zone C Zone C Zone B Zone A Upper Control Limit (UCL) Centerline/Average Lower Control Limit (LCL)
14 What do I do when it s out of control? Time to Find and Fix the cause Look for patterns in in the data Analyze the out of of control occurrence Fishbone diagrams and Hypothesis tests are valuable discovery tools.
15 Learning Objectives Understand how to select the correct control chart for an application. Know how to fill out and maintain a control chart. Know how to interpret a control chart to determine out of control situations.
Understand the role that hypothesis testing plays in an improvement project. Know how to perform a two sample hypothesis test.
HYPOTHESIS TESTING Learning Objectives Understand the role that hypothesis testing plays in an improvement project. Know how to perform a two sample hypothesis test. Know how to perform a hypothesis test
More informationSTATISTICAL QUALITY CONTROL (SQC)
Statistical Quality Control 1 SQC consists of two major areas: STATISTICAL QUALITY CONTOL (SQC) - Acceptance Sampling - Process Control or Control Charts Both of these statistical techniques may be applied
More informationSoftware Quality. Unit 2. Advanced techniques
Software Quality Unit 2. Advanced techniques Index 1. Statistical techniques: Statistical process control, variable control charts and control chart for attributes. 2. Advanced techniques: Quality function
More informationIndividual Moving Range (I-MR) Charts. The Swiss Army Knife of Process Charts
Individual Moving Range (I-MR) Charts The Swiss Army Knife of Process Charts SPC Selection Process Choose Appropriate Control Chart ATTRIBUTE type of data CONTINUOUS DEFECTS type of attribute data DEFECTIVES
More informationConfidence Intervals for Cp
Chapter 296 Confidence Intervals for Cp Introduction This routine calculates the sample size needed to obtain a specified width of a Cp confidence interval at a stated confidence level. Cp is a process
More informationSPC Response Variable
SPC Response Variable This procedure creates control charts for data in the form of continuous variables. Such charts are widely used to monitor manufacturing processes, where the data often represent
More informationUse and interpretation of statistical quality control charts
International Journal for Quality in Health Care 1998; Volume 10, Number I: pp. 69-73 Methodology matters VIII 'Methodology Matters' is a series of intermittently appearing articles on methodology. Suggestions
More informationCORRELATION ANALYSIS
CORRELATION ANALYSIS Learning Objectives Understand how correlation can be used to demonstrate a relationship between two factors. Know how to perform a correlation analysis and calculate the coefficient
More information10 CONTROL CHART CONTROL CHART
Module 10 CONTOL CHT CONTOL CHT 1 What is a Control Chart? control chart is a statistical tool used to distinguish between variation in a process resulting from common causes and variation resulting from
More informationIntroduction to STATISTICAL PROCESS CONTROL TECHNIQUES
Introduction to STATISTICAL PROCESS CONTROL TECHNIQUES Preface 1 Quality Control Today 1 New Demands On Systems Require Action 1 Socratic SPC -- Overview Q&A 2 Steps Involved In Using Statistical Process
More informationCommon Tools for Displaying and Communicating Data for Process Improvement
Common Tools for Displaying and Communicating Data for Process Improvement Packet includes: Tool Use Page # Box and Whisker Plot Check Sheet Control Chart Histogram Pareto Diagram Run Chart Scatter Plot
More informationControl Charts for Variables. Control Chart for X and R
Control Charts for Variables X-R, X-S charts, non-random patterns, process capability estimation. 1 Control Chart for X and R Often, there are two things that might go wrong in a process; its mean or its
More informationTHE PROCESS CAPABILITY ANALYSIS - A TOOL FOR PROCESS PERFORMANCE MEASURES AND METRICS - A CASE STUDY
International Journal for Quality Research 8(3) 399-416 ISSN 1800-6450 Yerriswamy Wooluru 1 Swamy D.R. P. Nagesh THE PROCESS CAPABILITY ANALYSIS - A TOOL FOR PROCESS PERFORMANCE MEASURES AND METRICS -
More informationApplied Reliability ------------------------------------------------------------------------------------------------------------ Applied Reliability
Applied Reliability Techniques for Reliability Analysis with Applied Reliability Tools (ART) (an EXCEL Add-In) and JMP Software AM216 Class 6 Notes Santa Clara University Copyright David C. Trindade, Ph.
More informationUniversally Accepted Lean Six Sigma Body of Knowledge for Green Belts
Universally Accepted Lean Six Sigma Body of Knowledge for Green Belts The IASSC Certified Green Belt Exam was developed and constructed based on the topics within the body of knowledge listed here. Questions
More informationTHE SIX SIGMA BLACK BELT PRIMER
INTRO-1 (1) THE SIX SIGMA BLACK BELT PRIMER by Quality Council of Indiana - All rights reserved Fourth Edition - September, 2014 Quality Council of Indiana 602 West Paris Avenue West Terre Haute, IN 47885
More informationConfidence Intervals for Cpk
Chapter 297 Confidence Intervals for Cpk Introduction This routine calculates the sample size needed to obtain a specified width of a Cpk confidence interval at a stated confidence level. Cpk is a process
More informationGetting Started with Statistics. Out of Control! ID: 10137
Out of Control! ID: 10137 By Michele Patrick Time required 35 minutes Activity Overview In this activity, students make XY Line Plots and scatter plots to create run charts and control charts (types of
More informationINTERPRETING THE ONE-WAY ANALYSIS OF VARIANCE (ANOVA)
INTERPRETING THE ONE-WAY ANALYSIS OF VARIANCE (ANOVA) As with other parametric statistics, we begin the one-way ANOVA with a test of the underlying assumptions. Our first assumption is the assumption of
More informationUSE OF SHEWART CONTROL CHART TECHNIQUE IN MONITORING STUDENT PERFORMANCE
Bulgarian Journal of Science and Education Policy (BJSEP), Volume 8, Number 2, 2014 USE OF SHEWART CONTROL CHART TECHNIQUE IN MONITORING STUDENT PERFORMANCE A. A. AKINREFON, O. S. BALOGUN Modibbo Adama
More informationControl Charts and Data Integration
Control Charts and Data Integration The acceptance chart and other control alternatives. Examples on SPC applications 1 Modified Charts If C pk >> 1 we set control limits so that the fraction non-conf.
More informationControl CHAPTER OUTLINE LEARNING OBJECTIVES
Quality Control 16Statistical CHAPTER OUTLINE 16-1 QUALITY IMPROVEMENT AND STATISTICS 16-2 STATISTICAL QUALITY CONTROL 16-3 STATISTICAL PROCESS CONTROL 16-4 INTRODUCTION TO CONTROL CHARTS 16-4.1 Basic
More informationAdverse Impact Ratio for Females (0/ 1) = 0 (5/ 17) = 0.2941 Adverse impact as defined by the 4/5ths rule was not found in the above data.
1 of 9 12/8/2014 12:57 PM (an On-Line Internet based application) Instructions: Please fill out the information into the form below. Once you have entered your data below, you may select the types of analysis
More informationSAMPLE SIZE CONSIDERATIONS
SAMPLE SIZE CONSIDERATIONS Learning Objectives Understand the critical role having the right sample size has on an analysis or study. Know how to determine the correct sample size for a specific study.
More informationControl Charts - SigmaXL Version 6.1
Control Charts - SigmaXL Version 6.1 Control Charts: Overview Summary Report on Test for Special Causes Individuals & Moving Range Charts Use Historical Groups to Display Before VS After Improvement X-Bar
More informationSix Sigma Project Charter
rev 2 Six Sigma Project Charter Name of project: Decreasing percent of transferred out calls by 50% Green belt: Submitted by: Joy May e-mail: joy@purdue.edu Date submitted: May 2, 202 I. Project Selection
More information1. What is the critical value for this 95% confidence interval? CV = z.025 = invnorm(0.025) = 1.96
1 Final Review 2 Review 2.1 CI 1-propZint Scenario 1 A TV manufacturer claims in its warranty brochure that in the past not more than 10 percent of its TV sets needed any repair during the first two years
More informationIntroduction to CONTINUOUS QUALITY IMPROVEMENT TECHNIQUES. for Healthcare Process Improvement
Introduction to CONTINUOUS QUALITY IMPROVEMENT TECHNIQUES for Healthcare Process Improvement Preface 1 Quality Control and Healthcare Today 1 New Demands On Healthcare Systems Require Action 1 Continous
More informationTools & Techniques for Process Improvement
Tools & Techniques for Process Improvement Understanding processes so that they can be improved by means of a systematic approach requires the knowledge of a simple kit of ols or techniques. The effective
More informationStatistical Process Control OPRE 6364 1
Statistical Process Control OPRE 6364 1 Statistical QA Approaches Statistical process control (SPC) Monitors production process to prevent poor quality Acceptance sampling Inspects random sample of product
More informationBA 275 Review Problems - Week 5 (10/23/06-10/27/06) CD Lessons: 48, 49, 50, 51, 52 Textbook: pp. 380-394
BA 275 Review Problems - Week 5 (10/23/06-10/27/06) CD Lessons: 48, 49, 50, 51, 52 Textbook: pp. 380-394 1. Does vigorous exercise affect concentration? In general, the time needed for people to complete
More informationLeading Indicators for Project Management
Leading Indicators for Project Management Project Headlights Dave Card David.card@dnv.com Agenda Motivation Headlights Strategies for Leading Indicators Common Leading Indicators Back-up Lights Summary
More informationAGILE Burndown Chart deviation - Predictive Analysis to Improve Iteration Planning
AGILE Burndown Chart deviation - Predictive Analysis to Improve Iteration Planning A. Mr. Dhruba Jyoti Chaudhuri 1, B. Ms. Aditi Chaudhuri 2 1 Process Excellence Group, Tata Consultancy Services (TCS)
More informationFAILURE INVESTIGATION AND ROOT CAUSE ANALYSIS
FAILURE INVESTIGATION AND ROOT CAUSE ANALYSIS Presented By Clay Anselmo, R.A.C. President and C.O.O. Reglera L.L.C. Denver, CO Learning Objectives Understand the Definitions of Failure Investigation and
More informationI/A Series Information Suite AIM*SPC Statistical Process Control
I/A Series Information Suite AIM*SPC Statistical Process Control PSS 21S-6C3 B3 QUALITY PRODUCTIVITY SQC SPC TQC y y y y y y y y yy y y y yy s y yy s sss s ss s s ssss ss sssss $ QIP JIT INTRODUCTION AIM*SPC
More informationStatistical Process Control Basics. 70 GLEN ROAD, CRANSTON, RI 02920 T: 401-461-1118 F: 401-461-1119 www.tedco-inc.com
Statistical Process Control Basics 70 GLEN ROAD, CRANSTON, RI 02920 T: 401-461-1118 F: 401-461-1119 www.tedco-inc.com What is Statistical Process Control? Statistical Process Control (SPC) is an industrystandard
More informationProjects Involving Statistics (& SPSS)
Projects Involving Statistics (& SPSS) Academic Skills Advice Starting a project which involves using statistics can feel confusing as there seems to be many different things you can do (charts, graphs,
More informationLesson 1: Comparison of Population Means Part c: Comparison of Two- Means
Lesson : Comparison of Population Means Part c: Comparison of Two- Means Welcome to lesson c. This third lesson of lesson will discuss hypothesis testing for two independent means. Steps in Hypothesis
More informationInstruction Manual for SPC for MS Excel V3.0
Frequency Business Process Improvement 281-304-9504 20314 Lakeland Falls www.spcforexcel.com Cypress, TX 77433 Instruction Manual for SPC for MS Excel V3.0 35 30 25 LSL=60 Nominal=70 Capability Analysis
More informationThe G Chart in Minitab Statistical Software
The G Chart in Minitab Statistical Software Background Developed by James Benneyan in 1991, the g chart (or G chart in Minitab) is a control chart that is based on the geometric distribution. Benneyan
More informationPart 3. Comparing Groups. Chapter 7 Comparing Paired Groups 189. Chapter 8 Comparing Two Independent Groups 217
Part 3 Comparing Groups Chapter 7 Comparing Paired Groups 189 Chapter 8 Comparing Two Independent Groups 217 Chapter 9 Comparing More Than Two Groups 257 188 Elementary Statistics Using SAS Chapter 7 Comparing
More informationPROJECT QUALITY MANAGEMENT
8 PROJECT QUALITY MANAGEMENT Project Quality Management includes the processes required to ensure that the project will satisfy the needs for which it was undertaken. It includes all activities of the
More informationHypothesis testing. c 2014, Jeffrey S. Simonoff 1
Hypothesis testing So far, we ve talked about inference from the point of estimation. We ve tried to answer questions like What is a good estimate for a typical value? or How much variability is there
More informationTHE BASICS OF STATISTICAL PROCESS CONTROL & PROCESS BEHAVIOUR CHARTING
THE BASICS OF STATISTICAL PROCESS CONTROL & PROCESS BEHAVIOUR CHARTING A User s Guide to SPC By David Howard Management-NewStyle "...Shewhart perceived that control limits must serve industry in action.
More informationChapter 5 Analysis of variance SPSS Analysis of variance
Chapter 5 Analysis of variance SPSS Analysis of variance Data file used: gss.sav How to get there: Analyze Compare Means One-way ANOVA To test the null hypothesis that several population means are equal,
More informationChapter 8 Hypothesis Testing Chapter 8 Hypothesis Testing 8-1 Overview 8-2 Basics of Hypothesis Testing
Chapter 8 Hypothesis Testing 1 Chapter 8 Hypothesis Testing 8-1 Overview 8-2 Basics of Hypothesis Testing 8-3 Testing a Claim About a Proportion 8-5 Testing a Claim About a Mean: s Not Known 8-6 Testing
More informationThe normal approximation to the binomial
The normal approximation to the binomial In order for a continuous distribution (like the normal) to be used to approximate a discrete one (like the binomial), a continuity correction should be used. There
More informationConfidence Intervals for Exponential Reliability
Chapter 408 Confidence Intervals for Exponential Reliability Introduction This routine calculates the number of events needed to obtain a specified width of a confidence interval for the reliability (proportion
More informationChapter 7 Notes - Inference for Single Samples. You know already for a large sample, you can invoke the CLT so:
Chapter 7 Notes - Inference for Single Samples You know already for a large sample, you can invoke the CLT so: X N(µ, ). Also for a large sample, you can replace an unknown σ by s. You know how to do a
More information46.2. Quality Control. Introduction. Prerequisites. Learning Outcomes
Quality Control 46.2 Introduction Quality control via the use of statistical methods is a very large area of study in its own right and is central to success in modern industry with its emphasis on reducing
More informationSix Sigma. Breakthrough Strategy or Your Worse Nightmare? Jeffrey T. Gotro, Ph.D. Director of Research & Development Ablestik Laboratories
Six Sigma Breakthrough Strategy or Your Worse Nightmare? Jeffrey T. Gotro, Ph.D. Director of Research & Development Ablestik Laboratories Agenda What is Six Sigma? What are the challenges? What are the
More informationFoundation of Quantitative Data Analysis
Foundation of Quantitative Data Analysis Part 1: Data manipulation and descriptive statistics with SPSS/Excel HSRS #10 - October 17, 2013 Reference : A. Aczel, Complete Business Statistics. Chapters 1
More informationElaboration of Scrum Burndown Charts.
. Combining Control and Burndown Charts and Related Elements Discussion Document By Mark Crowther, Empirical Pragmatic Tester Introduction When following the Scrum approach a tool frequently used is the
More informationSTATISTICAL METHODS FOR QUALITY CONTROL
statistics STATISTICAL METHODS FOR QUALITY CONTROL CONTENTS STATISTICS IN PRACTICE: DOW CHEMICAL U.S.A. 1 STATISTICAL PROCESS CONTROL Control Charts x Chart: Process Mean and Standard Deviation Known x
More informationDATA MONITORING AND ANALYSIS PROGRAM MANUAL
DATA MONITORING AND ANALYSIS PROGRAM MANUAL LBNL/PUB-5519 (3), Rev. 0 Effective Date: Orlando Lawrence Berkeley National Laboratory LBNL/PUB-5519 (3), Rev. 0 Page 2 of 22 REVISION HISTORY Revision Date
More informationAssessing Measurement System Variation
Assessing Measurement System Variation Example 1: Fuel Injector Nozzle Diameters Problem A manufacturer of fuel injector nozzles installs a new digital measuring system. Investigators want to determine
More informationSTATISTICAL REASON FOR THE 1.5σ SHIFT Davis R. Bothe
STATISTICAL REASON FOR THE 1.5σ SHIFT Davis R. Bothe INTRODUCTION Motorola Inc. introduced its 6σ quality initiative to the world in the 1980s. Almost since that time quality practitioners have questioned
More informationComparing Multiple Proportions, Test of Independence and Goodness of Fit
Comparing Multiple Proportions, Test of Independence and Goodness of Fit Content Testing the Equality of Population Proportions for Three or More Populations Test of Independence Goodness of Fit Test 2
More informationVariables Control Charts
MINITAB ASSISTANT WHITE PAPER This paper explains the research conducted by Minitab statisticians to develop the methods and data checks used in the Assistant in Minitab 17 Statistical Software. Variables
More informationProcess Quality. BIZ2121-04 Production & Operations Management. Sung Joo Bae, Assistant Professor. Yonsei University School of Business
BIZ2121-04 Production & Operations Management Process Quality Sung Joo Bae, Assistant Professor Yonsei University School of Business Disclaimer: Many slides in this presentation file are from the copyrighted
More informationSAMPLE EXAMINATION. If you have any questions regarding this sample examination, please email cert@asq.org
SAMPLE EXAMINATION The purpose of the following sample examination is to provide an example of what is provided on exam day by ASQ, complete with the same instructions that are provided on exam day. The
More informationIMPROVING TRADITIONAL EARNED VALUE MANAGEMENT BY INCORPORATING STATISTICAL PROCESS CHARTS
IMPROVING TRADITIONAL EARNED VALUE MANAGEMENT BY INCORPORATING STATISTICAL PROCESS CHARTS Sou-Sen Leu P.O.Box 90-30, Taipei, leuss@mail.ntust.edu.tw You-Che Lin P.O.Box 90-30, Taipei, M9055@mail.ntust.edu.tw
More informationSix Sigma Acronyms. 2-1 Do Not Reprint without permission of
Six Sigma Acronyms $k Thousands of dollars $M Millions of dollars % R & R Gauge % Repeatability and Reproducibility ANOVA Analysis of Variance AOP Annual Operating Plan BB Black Belt C & E Cause and Effects
More informationMeasurement and Metrics Fundamentals. SE 350 Software Process & Product Quality
Measurement and Metrics Fundamentals Lecture Objectives Provide some basic concepts of metrics Quality attribute metrics and measurements Reliability, validity, error Correlation and causation Discuss
More informationTHE OPEN SOURCE SOFTWARE R IN THE STATISTICAL QUALITY CONTROL
1. Miriam ANDREJIOVÁ, 2. Zuzana KIMÁKOVÁ THE OPEN SOURCE SOFTWARE R IN THE STATISTICAL QUALITY CONTROL 1,2 TECHNICAL UNIVERSITY IN KOŠICE, FACULTY OF MECHANICAL ENGINEERING, KOŠICE, DEPARTMENT OF APPLIED
More informationRegression Analysis: A Complete Example
Regression Analysis: A Complete Example This section works out an example that includes all the topics we have discussed so far in this chapter. A complete example of regression analysis. PhotoDisc, Inc./Getty
More informationUnit 1: Introduction to Quality Management
Unit 1: Introduction to Quality Management Definition & Dimensions of Quality Quality Control vs Quality Assurance Small-Q vs Big-Q & Evolution of Quality Movement Total Quality Management (TQM) & its
More informationBenchmarking Student Learning Outcomes using Shewhart Control Charts
Benchmarking Student Learning Outcomes using Shewhart Control Charts Steven J. Peterson, MBA, PE Weber State University Ogden, Utah This paper looks at how Shewhart control charts a statistical tool used
More informationMULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question.
Sample Practice problems - chapter 12-1 and 2 proportions for inference - Z Distributions Name MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Provide
More informationPremaster Statistics Tutorial 4 Full solutions
Premaster Statistics Tutorial 4 Full solutions Regression analysis Q1 (based on Doane & Seward, 4/E, 12.7) a. Interpret the slope of the fitted regression = 125,000 + 150. b. What is the prediction for
More informationNCSS Statistical Software
Chapter 06 Introduction This procedure provides several reports for the comparison of two distributions, including confidence intervals for the difference in means, two-sample t-tests, the z-test, the
More informationScatter Plots with Error Bars
Chapter 165 Scatter Plots with Error Bars Introduction The procedure extends the capability of the basic scatter plot by allowing you to plot the variability in Y and X corresponding to each point. Each
More informationIndependent t- Test (Comparing Two Means)
Independent t- Test (Comparing Two Means) The objectives of this lesson are to learn: the definition/purpose of independent t-test when to use the independent t-test the use of SPSS to complete an independent
More informationGage Studies for Continuous Data
1 Gage Studies for Continuous Data Objectives Determine the adequacy of measurement systems. Calculate statistics to assess the linearity and bias of a measurement system. 1-1 Contents Contents Examples
More informationStatistical Process Control (SPC) Training Guide
Statistical Process Control (SPC) Training Guide Rev X05, 09/2013 What is data? Data is factual information (as measurements or statistics) used as a basic for reasoning, discussion or calculation. (Merriam-Webster
More informationA) 0.1554 B) 0.0557 C) 0.0750 D) 0.0777
Math 210 - Exam 4 - Sample Exam 1) What is the p-value for testing H1: µ < 90 if the test statistic is t=-1.592 and n=8? A) 0.1554 B) 0.0557 C) 0.0750 D) 0.0777 2) The owner of a football team claims that
More informationThe normal approximation to the binomial
The normal approximation to the binomial The binomial probability function is not useful for calculating probabilities when the number of trials n is large, as it involves multiplying a potentially very
More informationPearson's Correlation Tests
Chapter 800 Pearson's Correlation Tests Introduction The correlation coefficient, ρ (rho), is a popular statistic for describing the strength of the relationship between two variables. The correlation
More informationBA 275 Review Problems - Week 6 (10/30/06-11/3/06) CD Lessons: 53, 54, 55, 56 Textbook: pp. 394-398, 404-408, 410-420
BA 275 Review Problems - Week 6 (10/30/06-11/3/06) CD Lessons: 53, 54, 55, 56 Textbook: pp. 394-398, 404-408, 410-420 1. Which of the following will increase the value of the power in a statistical test
More informationPoint Biserial Correlation Tests
Chapter 807 Point Biserial Correlation Tests Introduction The point biserial correlation coefficient (ρ in this chapter) is the product-moment correlation calculated between a continuous random variable
More information2. What is the general linear model to be used to model linear trend? (Write out the model) = + + + or
Simple and Multiple Regression Analysis Example: Explore the relationships among Month, Adv.$ and Sales $: 1. Prepare a scatter plot of these data. The scatter plots for Adv.$ versus Sales, and Month versus
More informationA Study of Process Variability of the Injection Molding of Plastics Parts Using Statistical Process Control (SPC)
Paper ID #7829 A Study of Process Variability of the Injection Molding of Plastics Parts Using Statistical Process Control (SPC) Dr. Rex C Kanu, Ball State University Dr. Rex Kanu is the coordinator of
More informationConfidence Intervals for Spearman s Rank Correlation
Chapter 808 Confidence Intervals for Spearman s Rank Correlation Introduction This routine calculates the sample size needed to obtain a specified width of Spearman s rank correlation coefficient confidence
More informationThe Importance of Project Quality Management. What Is Project Quality? The International Organization for Standardization (ISO)
Chapter 8 Project Quality Management November 17, 2008 2 The Importance of Project Quality Management Many people joke about the poor quality of IT products People seem to accept systems being down occasionally
More informationComparison of EngineRoom (6.0) with Minitab (16) and Quality Companion (3)
Comparison of EngineRoom (6.0) with Minitab (16) and Quality Companion (3) What is EngineRoom? A Microsoft Excel add in A suite of powerful, simple to use Lean and Six Sigma data analysis tools Built for
More informationSTART Selected Topics in Assurance
START Selected Topics in Assurance Related Technologies Table of Contents Introduction Some Essential Concepts Some QC Charts Summary For Further Study About the Author Other START Sheets Available Introduction
More informationA NEW APPROACH FOR MEASUREMENT OF THE EFFICIENCY OF C pm AND C pmk CONTROL CHARTS
International Journal for Quality Research 7(4) 605 6 ISSN 1800-6450 María Teresa Carot Aysun Sagbas 1 José María Sanz A NEW APPROACH FOR MEASUREMENT OF THE EFFICIENCY OF C pm AND C pmk CONTROL CHARTS
More informationCHAPTER 13. Control Charts
13.1 Introduction 1 CHAPTER 13 Control Charts This chapter discusses a set of methods for monitoring process characteristics over time called control charts and places these tools in the wider perspective
More informationABSORBENCY OF PAPER TOWELS
ABSORBENCY OF PAPER TOWELS 15. Brief Version of the Case Study 15.1 Problem Formulation 15.2 Selection of Factors 15.3 Obtaining Random Samples of Paper Towels 15.4 How will the Absorbency be measured?
More informationFinal Exam Practice Problem Answers
Final Exam Practice Problem Answers The following data set consists of data gathered from 77 popular breakfast cereals. The variables in the data set are as follows: Brand: The brand name of the cereal
More informationAC 2010-884: TEACHING CONTROL CHARTS FOR VARIABLES USING THE MOUSE FACTORY
AC 2010-884: TEACHING CONTROL CHARTS FOR VARIABLES USING THE MOUSE FACTORY Douglas Timmer, University of Texas, Pan American Miguel Gonzalez, University of Texas, Pan American Connie Borror, Arizona State
More informationStudy Guide for the Final Exam
Study Guide for the Final Exam When studying, remember that the computational portion of the exam will only involve new material (covered after the second midterm), that material from Exam 1 will make
More informationInter-Hospital Variation in Growth Outcomes and Improvement Using a Quantitative Metric
Inter-Hospital Variation in Growth Outcomes and Improvement Using a Quantitative Metric Kristyn eam MD, Sergei Roumiantsev MD PhD, Terri Gorman MD, Munish Gupta MD, Nneka Nzegwu DO, Sunita Pereira MD,
More informationBody of Knowledge for Six Sigma Green Belt
Body of Knowledge for Six Sigma Green Belt What to Prepare For: The following is the Six Sigma Green Belt Certification Body of Knowledge that the exam will cover. We strongly encourage you to study and
More informationUnit 23: Control Charts
Unit 23: Control Charts Summary of Video Statistical inference is a powerful tool. Using relatively small amounts of sample data we can figure out something about the larger population as a whole. Many
More informationControl chart, run chart, upper control limit, lower control limit, Statistical process control
CONTROL CHARTS ABSTRACT Category: Monitoring - Control ΚEYWORDS Control charts (G) are line graphs in which data are plotted over time, with the addition of two horizontal lines, called control limits,
More informationChapter 2 Probability Topics SPSS T tests
Chapter 2 Probability Topics SPSS T tests Data file used: gss.sav In the lecture about chapter 2, only the One-Sample T test has been explained. In this handout, we also give the SPSS methods to perform
More informationQUALITY MANAGEMENT AND CLIENT RELATIONSHIP MANAGEMENT IN SOFTWARE TESTING Shubhra Banerji Address for Correspondence
ABSTRACT: Research Article QUALITY MANAGEMENT AND CLIENT RELATIONSHIP MANAGEMENT IN SOFTWARE TESTING Shubhra Banerji Address for Correspondence IBM India Private Limited, SA-2 Subramanya Arcade-II, Banerghata
More informationDISCRETE MODEL DATA IN STATISTICAL PROCESS CONTROL. Ester Gutiérrez Moya 1. Keywords: Quality control, Statistical process control, Geometric chart.
VI Congreso de Ingeniería de Organización Gijón, 8 y 9 de septiembre 005 DISCRETE MODEL DATA IN STATISTICAL PROCESS CONTROL Ester Gutiérrez Moya Dpto. Organización Industrial y Gestión de Empresas. Escuela
More informationConfidence Intervals for the Difference Between Two Means
Chapter 47 Confidence Intervals for the Difference Between Two Means Introduction This procedure calculates the sample size necessary to achieve a specified distance from the difference in sample means
More information