DOCSLIB.ORG

Run and Control Charts Are the Best Tools to Determine: 1

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Run and Control Charts Are the Best Tools to Determine: 1 1 You have performance data! Now, what do you do with it? 2 “If I had to reduce my message for management to just a few words, I’d say it all had to do with reducing variation.” W. Edwards Deming The Problem! 3 Aggregated data presented in tabular formats or with summary statistics, will not help you measure the impact of process improvement efforts. Aggregated data can only lead to judgment, not to improvement. Percent of A&E patients Seen by a Physician within 10 min Did we improve? What will happen next? Should we do something? Source: R. Lloyd Percent of A&E patients Seen by a Physician within 10 min 100% 95% 90% 85% 80% 75% 70% 65% 60% Change 55% made here 50% 1/9/2008 2/6/2008 3/5/2008 10/3/2007 1/23/2008 2/20/2008 3/19/2008 10/17/2007 10/31/2007 11/14/2007 11/28/2007 12/12/2007 12/26/2007 Did we improve? What will happen next? Should we do something? Source: R. Lloyd Change made between week 7 and week 8 Was the change an improvement? 10 9 8 8 7 6 5 Delay Time (hrs) Time Delay 4 3 3 2 1 0 1 2 Before Change After Change (measure on Week 4) (measure on week 11) Was the change an improvement? Case1 12 10 8 6 Delay Time (hrs) Time Delay 4 Make Change 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 12 Case 2 10 Delay Delay Time (hrs) 8 Make 6 Change 4 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 12 Case 3 10 8 Delay Delay Time (hrs) 6 4 Make Change 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Case 4 12 10 8 Delay Delay Time (hrs) 6 Make 4 Change 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 12 Case 5 10 8 Make Delay Delay Time (hrs) 6 Change 4 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 12 Case 6 10 8 Make Delay Delay Time (hrs) 6 Change 4 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 12 Case 2 10 Delay Delay Time (hrs) 8 Random Variation Make 6 Change 4 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 12 Case 3 10 Headed down before change. 8 Delay Delay Time (hrs) 6 Where begin? 4 Make Change 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Case 4 12 10 Change did not hold 8 Delay Delay Time (hrs) 6 Make 4 Change 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 12 Case 5 10 Improvement: before change 8 Make Delay Delay Time (hrs) 6 Change (between week 4 & 5) 4 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 12 Case 6 10 Week 4 not typical of process 8 Make Delay Delay Time (hrs) 6 Change 4 2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 People unclear of the concept! Percent Hand Hygiene “…and then another decrease in the percent compliance with hand hygiene this month. But, I have a really good feeling about next month! The average of a set of numbers can be 10 created by many different distributions Average Measure Time If you don’t understand the variation that lives in11 your data, you will be tempted to ... • Deny the data (It doesn’t fit my view of reality!) • See trends where there are no trends • Try to explain natural variation as special events • Blame and give credit to people for things over which they have no control • Distort the process that produced the data • Kill the messenger! Distorting the Data! “You'll be happy to see that I’ve finally managed to turn things around!” The 13 to understanding quality performance, therefore, lies in understanding variation over time not in preparing aggregated data and calculating summary statistics! “A phenomenon will be said to be controlled when, through the use of past experience, we can predict, at least within limits, how the Dr. Walter A Shewhart phenomenon may be W. Shewhart. Economic Control of expected to vary in Quality of Manufactured Product, 1931 the future” 15 “What is the variation in one system over time?” Walter A. Shewhart - early 1920’s, Bell Laboratories Dynamic View UCL Static View time Every process displays variation: LCL • Controlled variation stable, consistent pattern of variation “chance”, constant causes • Special cause variation Static View “assignable” pattern changes over time Types of Variation Common Cause Variation Special Cause Variation • Is inherent in the design of the process • Is due to irregular or unnatural causes that are not inherent in the design of the process • Is due to regular, natural or ordinary causes • Affect some, but not necessarily all aspects of the process • Affects all the outcomes of a process • Results in an “unstable” process • Results in a “stable” process that that is not predictable is predictable • Also known as non-random or • Also known as random or assignable variation unassignable variation Common Cause Variation 100 90 80 70 60 50 40 30 20 10 0 3/1/2008 3/8/2008 4/5/2008 5/3/2008 6/7/2008 3/15/2008 3/22/2008 3/29/2008 4/12/2008 4/19/2008 4/26/2008 5/10/2008 5/17/2008 5/24/2008 5/31/2008 • Points equally likely above or below center line • There will be a high data point and a low, but this is expected • No trends or shifts or other patterns Courtesy of Richard Scoville, PhD, IHI Improvement Advisor A Stable Process is Predictable ! Thus you can confidently: • Counsel patients about what to expect • Plan for the future • Inform management • Use PDSA testing to improve it! Courtesy of Richard Scoville, PhD, IHI Improvement Advisor Two Types of Special Causes Unintentional When the system is out of control and unstable Holding the Gain: Isolated Femur Fractures Intentional 1200 1000 800 When we’re trying 600 Patient to change the 400 200 Minutes ED to per OR system 0 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 Sequential Patients Courtesy of Richard Scoville, PhD, IHI Improvement Advisor Point …Variation exists! Common Cause does not mean “Good Variation.” It only means that the process is stable and predictable. For example, if a patient’s systolic blood pressure averaged around 165 and was usually between 160 and 170 mmHg, this might be stable and predictable but completely unacceptable. Similarly Special Cause variation should not be viewed as “Bad Variation.” You could have a special cause that represents a very good result (e.g., a low turnaround time), which you would want to emulate. Special Cause merely means that the process is unstable and unpredictable. 21 Common Cause Variation Special Cause Variation Normal Sinus Rhythm (a.k.a. Atrial Flutter Rhythm (a.k.a. Common Cause Variation) SpecialHolding the Gain:Cause Isolated Femur Variation) Fractures 1200 1000 800 600 Patient 400 200 Minutes ED to per OR 0 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 Sequential Patients Appropriate Management Response to 22 Common & Special Causes of Variation Is the process stable? YES NO Type of variation Only Common Special + Common Change the Investigate the origin of the Right Choice process special cause Treat normal variation as a Change the process Wrong Choice special cause (tampering) Wasted Consequences of Increased making the wrong resources! choice variation! (time, effort, morale, money) Source: Carey, R. and Lloyd, R. Measuring Quality Improvement in Healthcare: A Guide to Statistical Process Control Applications. ASQ Press, Milwaukee, WI, 2001, page 153. 2 Questions … 1. Is the process stable? If so, it is predictable. 2. Is the process capable? The chart will tell you if the process is stable and predictable. You have to decide if the output of the process is capable of meeting the target or goal you have set! 23 Understanding Variation Statistically Unplanned Returns to Ed w/in 72 Hours Month M A M J J A S O N D J F M A M J J A S ED/100 41.78 43.89 39.86 40.03 38.01 43.43 39.21 41.90 41.78 43.00 39.66 40.03 48.21 43.89 39.86 36.21 41.78 43.89 31.45 Returns 17 26 13 16 24 27 19 14 33 20 17 22 29 17 36 19 22 24 22 u chart 1.2 1.0 UCL = 0.88 0.8 0.6 Mean = 0.54 Rate per 100 ED100Patients per Rate 0.4 0.2 LCL = 0.19 0.0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 STATIC VIEW DYNAMIC VIEW Run Chart Descriptive Statistics Mean, Median & Mode Control Chart Minimum/Maximum/Range (plot data over time) Standard Deviation Bar graphs/Pie charts Statistical Process Control (SPC) 24 The SPC Pioneers Walter W.
Load more

Recommended publications
  • X-Bar Charts
    NCSS Statistical Software NCSS.com Chapter 244 X-bar Charts Introduction This procedure generates X-bar control charts for variables. The format of the control charts is fully customizable. The data for the subgroups can be in a single column or in multiple columns. This procedure permits the defining of stages. The center line can be entered directly or estimated from the data, or a sub-set of the data. Sigma may be estimated from the data or a standard sigma value may be entered. A list of out-of-control points can be produced in the output, if desired, and means may be stored to the spreadsheet. X-bar Control Charts X-bar charts are used to monitor the mean of a process based on samples taken from the process at given times (hours, shifts, days, weeks, months, etc.). The measurements of the samples at a given time constitute a subgroup. Typically, an initial series of subgroups is used to estimate the mean and standard deviation of a process. The mean and standard deviation are then used to produce control limits for the mean of each subgroup. During this initial phase, the process should be in control. If points are out-of-control during the initial (estimation) phase, the assignable cause should be determined and the subgroup should be removed from estimation. Determining the process capability (see R & R Study and Capability Analysis procedures) may also be useful at this phase. Once the control limits have been established of the X-bar charts, these limits may be used to monitor the mean of the process going forward.
    [Show full text]
  • Methods and Philosophy of Statistical Process Control
    5Methods and Philosophy of Statistical Process Control CHAPTER OUTLINE 5.1 INTRODUCTION 5.4 THE REST OF THE MAGNIFICENT 5.2 CHANCE AND ASSIGNABLE CAUSES SEVEN OF QUALITY VARIATION 5.5 IMPLEMENTING SPC IN A 5.3 STATISTICAL BASIS OF THE CONTROL QUALITY IMPROVEMENT CHART PROGRAM 5.3.1 Basic Principles 5.6 AN APPLICATION OF SPC 5.3.2 Choice of Control Limits 5.7 APPLICATIONS OF STATISTICAL PROCESS CONTROL AND QUALITY 5.3.3 Sample Size and Sampling IMPROVEMENT TOOLS IN Frequency TRANSACTIONAL AND SERVICE 5.3.4 Rational Subgroups BUSINESSES 5.3.5 Analysis of Patterns on Control Charts Supplemental Material for Chapter 5 5.3.6 Discussion of Sensitizing S5.1 A SIMPLE ALTERNATIVE TO RUNS Rules for Control Charts RULES ON THEx CHART 5.3.7 Phase I and Phase II Control Chart Application The supplemental material is on the textbook Website www.wiley.com/college/montgomery. CHAPTER OVERVIEW AND LEARNING OBJECTIVES This chapter has three objectives. The first is to present the basic statistical control process (SPC) problem-solving tools, called the magnificent seven, and to illustrate how these tools form a cohesive, practical framework for quality improvement. These tools form an impor- tant basic approach to both reducing variability and monitoring the performance of a process, and are widely used in both the analyze and control steps of DMAIC. The second objective is to describe the statistical basis of the Shewhart control chart. The reader will see how decisions 179 180 Chapter 5 ■ Methods and Philosophy of Statistical Process Control about sample size, sampling interval, and placement of control limits affect the performance of a control chart.
    [Show full text]
  • Finland—Selected Issues and Statistical Appendix
    O1996 International Monetary Fund September 1996 IMF Staff Country Report No. 96/95 Finland—Selected Issues and Statistical Appendix This report on selected issues and statistical appendix on Finland was prepared by a staff team of the International Monetary Fund as background documentation for the periodic consultation with this member country. As such, the views expressed in this document are those of the staff team and do not necessarily reflect the views of the Government of Finland or the Executive Board of the IMF. Copies of this report are available to the public from International Monetary Fund • Publication Services 700 19th Street, N.W. • Washington, D.C. 20431 Telephone: (202) 623-7430 • Telefax: (202) 623-7201 Telex (RCA): 248331 IMF UR Internet: [email protected] Price: $15.00 a copy International Monetary Fund Washington, D.C. ©International Monetary Fund. Not for Redistribution This page intentionally left blank ©International Monetary Fund. Not for Redistribution INTERNATIONAL MONETARY FUND FINLAND Selected Issues and Statistical Appendix Prepared by T. Feyzioglu, D. Tambakis (both EU1) and C. Pazarbasioglu (MAE) Approved by the European I Department July 10, 1996 Contents Page I. Inflation and Wage Dynamics in Finland: A Cointegration Approach 1 1. Introduction and summary 1 2 . Data sources and statistical properties 4 a. Data sources and definitions 4 b. Order of integration 4 3. Empirical estimates 6 a. Modeling strategy 6 b. Cointegration and error correction 8 c. Model multipliers 10 4. Outlook for CPI and nominal wage inflation: 1996-2001 14 a. Baseline scenario 14 b. Alternative scenario: further depreciation in 1996 17 References 20 II.
    [Show full text]
  • Using Likelihood Ratios to Compare Run Chart Rules on Simulated Data Series
    RESEARCH ARTICLE Diagnostic Value of Run Chart Analysis: Using Likelihood Ratios to Compare Run Chart Rules on Simulated Data Series Jacob Anhøj* Centre of Diagnostic Evaluation, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark * [email protected] Abstract Run charts are widely used in healthcare improvement, but there is little consensus on how to interpret them. The primary aim of this study was to evaluate and compare the diagnostic a11111 properties of different sets of run chart rules. A run chart is a line graph of a quality measure over time. The main purpose of the run chart is to detect process improvement or process degradation, which will turn up as non-random patterns in the distribution of data points around the median. Non-random variation may be identified by simple statistical tests in- cluding the presence of unusually long runs of data points on one side of the median or if the graph crosses the median unusually few times. However, there is no general agreement OPEN ACCESS on what defines “unusually long” or “unusually few”. Other tests of questionable value are Citation: Anhøj J (2015) Diagnostic Value of Run frequently used as well. Three sets of run chart rules (Anhoej, Perla, and Carey rules) have Chart Analysis: Using Likelihood Ratios to Compare been published in peer reviewed healthcare journals, but these sets differ significantly in Run Chart Rules on Simulated Data Series. PLoS their sensitivity and specificity to non-random variation. In this study I investigate the diag- ONE 10(3): e0121349. doi:10.1371/journal. pone.0121349 nostic values expressed by likelihood ratios of three sets of run chart rules for detection of shifts in process performance using random data series.
    [Show full text]
  • Approaches for Detection of Unstable Processes: a Comparative Study Yerriswamy Wooluru J S S Academy of Technical Education, Bangalore, India, [email protected]
    Journal of Modern Applied Statistical Methods Volume 14 | Issue 2 Article 17 11-1-2015 Approaches for Detection of Unstable Processes: A Comparative Study Yerriswamy Wooluru J S S Academy of Technical Education, Bangalore, India, [email protected] D. R. Swamy J S S Academy of Technical Education, Bangalore, India P. Nagesh JSS Centre for Management Studies, Mysore, Indi Follow this and additional works at: http://digitalcommons.wayne.edu/jmasm Part of the Applied Statistics Commons, Social and Behavioral Sciences Commons, and the Statistical Theory Commons Recommended Citation Wooluru, Yerriswamy; Swamy, D. R.; and Nagesh, P. (2015) "Approaches for Detection of Unstable Processes: A Comparative Study," Journal of Modern Applied Statistical Methods: Vol. 14 : Iss. 2 , Article 17. DOI: 10.22237/jmasm/1446351360 Available at: http://digitalcommons.wayne.edu/jmasm/vol14/iss2/17 This Regular Article is brought to you for free and open access by the Open Access Journals at DigitalCommons@WayneState. It has been accepted for inclusion in Journal of Modern Applied Statistical Methods by an authorized editor of DigitalCommons@WayneState. Approaches for Detection of Unstable Processes: A Comparative Study Cover Page Footnote This work is supported by JSSMVP Mysore. I, sincerely thank to my Guide Dr.Swamy D.R, Professor and Head of the Department, Industrial Engineering &Management, JSSATE Bangalore and Co-Guide Dr P.Nagesh, Professor, Department of Management studies, SJCE, Mysore. This regular article is available in Journal of Modern Applied Statistical Methods: http://digitalcommons.wayne.edu/jmasm/vol14/ iss2/17 Journal of Modern Applied Statistical Methods Copyright © 2015 JMASM, Inc. November 2015, Vol. 14 No.
    [Show full text]
  • Run Charts Revisited: a Simulation Study of Run Chart Rules for Detection of Non- Random Variation in Health Care Processes
    RESEARCH ARTICLE Run Charts Revisited: A Simulation Study of Run Chart Rules for Detection of Non- Random Variation in Health Care Processes Jacob Anhøj1*, Anne Vingaard Olesen2 1. Rigshospitalet, University of Copenhagen, Copenhagen, Denmark, 2. University of Aalborg, Danish Center for Healthcare Improvements, Department of Business and Management, Aalborg, Denmark *[email protected] Abstract Background: A run chart is a line graph of a measure plotted over time with the median as a horizontal line. The main purpose of the run chart is to identify process improvement or degradation, which may be detected by statistical tests for non- random patterns in the data sequence. Methods: We studied the sensitivity to shifts and linear drifts in simulated processes using the shift, crossings and trend rules for detecting non-random variation in run charts. Results: The shift and crossings rules are effective in detecting shifts and drifts in OPEN ACCESS process centre over time while keeping the false signal rate constant around 5% Citation: Anhøj J, Olesen AV (2014) Run Charts Revisited: A Simulation Study of Run Chart Rules and independent of the number of data points in the chart. The trend rule is virtually for Detection of Non-Random Variation in Health useless for detection of linear drift over time, the purpose it was intended for. Care Processes. PLoS ONE 9(11): e113825. doi:10.1371/journal.pone.0113825 Editor: Robert K. Hills, Cardiff University, United Kingdom Received: February 24, 2014 Accepted: November 1, 2014 Introduction Published: November 25, 2014 Plotting measurements over time turns out, in my view, to be one of the most Copyright: ß 2014 Anhøj, Olesen.
    [Show full text]
  • A Guide to Creating and Interpreting Run and Control Charts Turning Data Into Information for Improvement Using This Guide
    Institute for Innovation and Improvement A guide to creating and interpreting run and control charts Turning Data into Information for Improvement Using this guide The NHS Institute has developed this guide as a reminder to commissioners how to create and analyse time-series data as part of their improvement work. It should help you ask the right questions and to better assess whether a change has led to an improvement. Contents The importance of time based measurements .........................................4 Run Charts ...............................................6 Control Charts .......................................12 Process Changes ....................................26 Recommended Reading .........................29 The Improving immunisation rates importance Before and after the implementation of a new recall system This example shows yearly figures for immunisation rates of time-based before and after a new system was introduced. The aggregated measurements data seems to indicate the change was a success. 90 Wow! A “significant improvement” from 86% 79% to 86% -up % take 79% 75 Time 1 Time 2 Conclusion - The change was a success! 4 Improving immunisation rates Before and after the implementation of a new recall system However, viewing how the rates have changed within the two periods tells a 100 very different story. Here New system implemented here we see that immunisation rates were actually improving until the new 86% – system was introduced. X They then became worse. 79% – Seeing this more detailed X 75 time based picture prompts a different response. % take-up Now what do you conclude about the impact of the new system? 50 24 Months 5 Run Charts Elements of a run chart A run chart shows a measurement on the y-axis plotted over time (on the x-axis).
    [Show full text]
  • Recent Debates on Monetary Condition and Inflation Pressures on the Mainland Call for an Analysis on the Inflation Dynamics and Their Main Determinants
    Research Memorandum 04/2005 March 2005 MONEY AND INFLATION IN CHINA Key points: • Recent debates on monetary condition and inflation pressures on the Mainland call for an analysis on the inflation dynamics and their main determinants. A natural starting point for the econometric analysis of monetary and inflation developments is the notion of monetary equilibrium. • This paper presents an estimate of the long-run demand for money in China. The estimated long-run income elasticity is rather stable over time and consistent with that estimated in earlier studies. • The difference between the estimated demand for money and the actual money stock provides an estimate of monetary disequilibria. These seem to provide leading information about CPI inflation in the sample period. At the current conjuncture, the measure suggests that monetary conditions have tightened since the macroeconomic adjustment started in early 2004. • Additionally, an error-correction model based on the long-run money demand function offers insights into the short-run and long-run inflation dynamics. Specifically: ¾ Over the longer term, inflation is mostly caused by monetary expansion. ¾ The price level responds to monetary shocks with lags and on average it takes about 20 quarters for the impact of a permanent monetary shock to peak. This suggests the need for early policy actions to curb inflation pressure given the long policy operation lag. Prepared by : Stefan Gerlach and Janet Kong Research Department Hong Kong Monetary Authority I. INTRODUCTION Concerns have been raised recently about overheating pressures in the economy of Mainland China (see, for example, Kalish 2004, Bradsher 2004, and Ignatius 2004).
    [Show full text]
  • Medians and the Individuals Control Chart the Individuals Control Chart
    Medians and the Individuals Control Chart The individuals control chart is used quite often to monitor processes. It can be used in almost all cases, so it makes the art of deciding which control chart to use extremely easy at times. Not sure? Use the individuals control chart. The individuals control chart is composed of two charts: the X chart where the individual values are plotted and the moving range (mR) chart where the range between consecutive values are plotted. The averages and control limits are also part of the charts. The average moving range (R̅) is used to calculate the control limits on the X chart. If the mR chart has a lot of out of control points, the average moving range will be inflated leading to wider control limits on the X chart. This can lead to missed signals (out of control points) on the X chart. Is there anything you can do when the mR chart has many out of control points to help miss fewer signals on the X chart? Yes, there is. One method is to use the median moving range. The median moving range is impacted much less by large moving range values than the average. Another option is to use the median of the X values in place of the overall average on the X chart. This publication explores using median values for the centerlines on the X and mR charts. We will start with a review of the individuals control chart. Then, the individuals control charts using median values is introduced. The charts look the same – the only difference is the location of the centerlines and control limits.
    [Show full text]
  • Phase I and Phase II - Control Charts for the Variance and Generalized Variance
    Phase I and Phase II - Control Charts for the Variance and Generalized Variance R. van Zyl1, A.J. van der Merwe2 1Quintiles International, [email protected] 2University of the Free State 1 Abstract By extending the results of Human, Chakraborti, and Smit(2010), Phase I control charts are derived for the generalized variance when the mean vector and covariance matrix of multivariate normally distributed data are unknown and estimated from m independent samples, each of size n. In Phase II predictive distributions based on a Bayesian approach are used to construct Shewart-type control limits for the variance and generalized variance. The posterior distribution is obtained by combining the likelihood (the observed data in Phase I) and the uncertainty of the unknown parameters via the prior distribution. By using the posterior distribution the unconditional predictive density functions are derived. Keywords: Shewart-type Control Charts, Variance, Generalized Variance, Phase I, Phase II, Predictive Density 1 Introduction Quality control is a process which is used to maintain the standards of products produced or services delivered. It is nowadays commonly accepted by most statisticians that statistical processes should be implemented in two phases: 1. Phase I where the primary interest is to assess process stability; and 2. Phase II where online monitoring of the process is done. Bayarri and Garcia-Donato(2005) gave the following reasons for recommending Bayesian analysis for the determining of control chart limits: • Control charts are based on future observations and Bayesian methods are very natural for prediction. • Uncertainty in the estimation of the unknown parameters are adequately handled.
    [Show full text]
  • Introduction to Using Control Charts Brought to You by NICHQ
    Introduction to Using Control Charts Brought to you by NICHQ Control Chart Purpose A control chart is a statistical tool that can help users identify variation and use that knowledge to inform the development of changes for improvement. Control charts provide a method to distinguish between the two types of causes of variation in a measure: Common Causes - those causes that are inherent in the system over time, affect everyone working in the system, and affect all outcomes of the system. Using your morning commute as an example, it may take between 35-45 minutes to commute to work each morning. It does not take exactly 40 minutes each morning because there is variation in common causes, such as the number of red lights or traffic volume. Special Causes - those causes that are not part of the system all the time or do not affect everyone, but arise because of specific circumstances. For example, it may take you 2 hours to get to work one morning because of a special cause, such as a major accident. Variation in data is expected and the type of variation that affects your system will inform your course of action for making improvements. A stable system, or one that is in a state of statistical control, is a system that has only common causes affecting the outcomes (common cause variation only). A stable system implies that the variation is predictable within statistically established limits, but does not imply that the system is performing well. An unstable system is a system with both common and special causes affecting the outcomes.
    [Show full text]
  • Stat 3411 Fall 2012 Exam 1
    Stat 3411 Fall 2012 Exam 1 The questions will cover material from assignments, classroom examples, posted lecture notes, and the text reading material corresponding to the points covered in class. If there is a concept in the book that is not covered in the posted lecture notes, in class, or on assignments, I won't ask you anything about that topic. The questions will be chosen so that the required arithmetic and other work is minimal in order to fit questions into 55 minutes. Plotting, if any, you are asked to do will be very quick and simple. o See problem 6 on the posted chapter 3 sample exam questions. Make use of information posted on the class web site, http://www.d.umn.edu/~rregal/stat3411.html Stat 3411 Syllabus Notes NIST Engineering Statistics Handbook National Institute of Standards and Technology Assignments Data Sets, Statistics Primer, and Examples Project Exam Information Sample Exam Questions From the Notes link: Chapters 1 and 2 1.1 Engineering Statistics: What and Why 1.2 Basic Terminology 1.3 Measurement: Its Importance and Difficulty 1.4 Mathematical Models, Reality, and Data Analysis 2.1 General Principles in the Collection of Engineering Data 2.2 Sampling in Enunerative Studies 2.3 Principles for Effective Experimentation 2.4 Some Common Experimental Designs 2.5 Collecting Engineering Data Outline Notes Guitar Strings: Use of Experimental Design Sticker Adhesion: Use of Models Testing Golf Clubs Observational and Experimental Studies: Ford Explorer Rollovers Excel: Factorial Randomization Table B1: Random Digits The first two chapters concerned basic ideas that go into designing studies and collecting data.
    [Show full text]