Confidence Intervals for Cpk
|
|
|
- Darrell Todd
- 9 years ago
- Views:
Transcription
1 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 capability index used to measure what a process is capable of producing. Unlike Cp, Cpk makes no assumption that the process mean is centered between the specification limits. Cpk requires the assumption that the measurements are normally distributed. The formula for the calculation of Cpk is Cpk = min(usl - μ, μ - LSL) / (3σ) where USL and LSL are the upper and lower specification limits, respectively. A process with a Cpk of 2.0 is considered excellent, while one with a Cpk of 1.33 is considered adequate. Technical Details This procedure is based on the results of Mathews (2010). A 100(1 α) % confidence interval for Cpk is given by P Cpk 1 z 1 α/2 1 n 1 9Cpk + 1 Cp Cpk 1 + z α/2 1 n 1 9Cpk + 1 = 1 α 2 2 where Cpk is the estimated value of Cpk, n is the sample size, and z 1 α/2 is the specific value of the standard normal random variate that has probability 1 - α/2 to the left. One-sided limits may be obtained by replacing α/2 by α. Confidence Interval Width The confidence interval width, confidence level, and sample size are related in the equation Width = Cpk upper Cpk lower This equation can be used to find n, α, or the width
2 Confidence Level The confidence level, 1 α, has the following interpretation. If thousands of samples of n items are drawn from a population using simple random sampling and a confidence interval is calculated for each sample, the proportion of those intervals that will include the true population parameter is 1 α. Procedure Options This section describes the options that are specific to this procedure. These are located on the Design tab. For more information about the options of other tabs, go to the Procedure Window chapter. Design Tab The Design tab contains most of the parameters and options that you will be concerned with. Solve For This option specifies the parameter to be solved for from the other parameters. One-Sided or Two-Sided Interval Interval Type Specify whether the confidence interval for the population correlation is two-sided or one-sided. A one-sided interval is often called a confidence bound rather than a confidence interval because it only has one limit. Two-Sided The two-sided confidence interval is defined by two limits: an upper confidence limit (UCL) and a lower confidence limit (LCL). These limits are constructed so that the designated proportion (confidence level) of such intervals will include the true population value. Upper One-Sided The upper confidence interval (or bound) is defined by a limit above the estimated parameter value. The limit is constructed so that the designated proportion (confidence level) of such limits has the true population value below it. Lower One-Sided The lower confidence interval (or bound) is defined by a limit below the estimated parameter value. The limit is constructed so that the designated proportion (confidence level) of such limits has the true population value above it. Confidence Confidence Level The confidence level, 1 α, has the following interpretation. If thousands of samples of n items are drawn from a population using simple random sampling and a confidence interval is calculated for each sample, the proportion of those intervals that will include the true population Cpk is 1 α. Often, the values 0.95 or 0.99 are used. You can enter single values or a range of values such as 0.90, 0.95 or 0.90 to 0.99 by
3 Sample Size N (Sample Size) Enter one or more values for the sample size. This is the number of individuals selected at random from the population to be in the study. You can enter a single value or a range of values. Precision Width of Confidence Interval (Two-Sided) This is the distance from the lower confidence limit to the upper confidence limit. You can enter a single value or a list of values. Distance from Cpk to Limit (One-Sided) This is the distance from the sample Cpk to the lower or upper limit of the confidence interval, depending on the Interval Type. You can enter a single value or a list of values. Planning Estimate of Cpk Cpk Enter an estimate of the sample Cpk. This value can be obtained from prior studies, expert opinion, or as a reasonable guess. The sample size and width calculations assume that the value entered here is the actual Cpk estimate obtained from the sample. The accuracy of your results will depend on the accuracy of this estimate. Cpk can be any number. Often, Cpk is between one and three in stable processes. You can enter a single value, a list of values, or a series of values. Common values are 1, 1.5, and
4 Example 1 Calculating Sample Size Suppose a study is planned in which the researcher wishes to construct a two-sided 95% confidence interval for Cpk such that the width of the interval is no wider than The researcher would like to examine Cpk values of 1.0, 1.5, 2.0, and 3.0 to determine the effect of the Cpk estimate on necessary sample size. Setup This section presents the values of each of the parameters needed to run this example. First, from the PASS Home window, load the procedure window by clicking on Quality Control and then clicking on. You may then make the appropriate entries as listed below, or open Example 1 by going to the File menu and choosing Open Example Template. Option Value Design Tab Solve For... Sample Size Interval Type... Two-Sided Confidence Level (1 Alpha) Confidence Interval Width (Two-Sided) Cpk Annotated Output Click the Calculate button to perform the calculations and generate the following output. Numeric Results Numeric Results for Two-Sided Sample C.I. C.I. Confidence Size Target Actual Lower Upper Level N Width Width Cpk Limit Limit Report Definitions Confidence Level is the proportion of confidence intervals (constructed with this same confidence level, sample size, etc.) that would contain the true value of Cpk. Sample Size N is the size of the sample drawn from the population. Target Width is the width that was requested. Actual Width is the calculated width. This is slightly different from the Target Width because N is an integer. Cpk is equal to min(usl - μ, μ - LSL) / 3σ, where USL and LSL are the upper and lower specification limits, μ is the process mean, and σ is the process standard deviation. C.I. Lower Limit and C.I. Upper Limit is the confidence interval of Cpk. References Kotz, S. and Johnson, N Process Capability Indices. Chapman & Hall. Mathews, Paul Sample Size Calculations: Practical Methods for Engineers and Scientists. Mathews Malnar and Bailey, Inc. Summary Statements A sample size of 940 produces a two-sided 95% confidence interval with a width equal to when the estimate of Cpk is This report shows the calculated sample size for each of the scenarios
5 Plots Section This plot shows the sample size versus Cpk
6 Example 2 Validation using Mathews Mathews (2010), page 230, gives an example of a sample size calculation. In this example the value of Cpk is 1.0, the confidence level is 90%, and the width is The resulting sample size is 662. Note that Mathews uses a normal approximation to the chi-square distribution which may make his results a little different than ours. Setup This section presents the values of each of the parameters needed to run this example. First, from the PASS Home window, load the procedure window by clicking on Quality Control and then clicking on. You may then make the appropriate entries as listed below, or open Example 2 by going to the File menu and choosing Open Example Template. Option Value Design Tab Solve For... Sample Size Interval Type... Two-Sided Confidence Level (1 Alpha) Confidence Interval Width (Two-Sided) Cpk... 1 Output Click the Calculate button to perform the calculations and generate the following output. Numeric Results Sample C.I. C.I. Confidence Size Target Actual Lower Upper Level N Width Width Cpk Limit Limit PASS also calculates the sample size to be
Confidence 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
Confidence 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
Confidence Intervals for One Standard Deviation Using Standard Deviation
Chapter 640 Confidence Intervals for One Standard Deviation Using Standard Deviation Introduction This routine calculates the sample size necessary to achieve a specified interval width or distance from
Confidence 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
Confidence 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
Two-Sample T-Tests Assuming Equal Variance (Enter Means)
Chapter 4 Two-Sample T-Tests Assuming Equal Variance (Enter Means) Introduction This procedure provides sample size and power calculations for one- or two-sided two-sample t-tests when the variances of
SPC 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
Two-Sample T-Tests Allowing Unequal Variance (Enter Difference)
Chapter 45 Two-Sample T-Tests Allowing Unequal Variance (Enter Difference) Introduction This procedure provides sample size and power calculations for one- or two-sided two-sample t-tests when no assumption
Point 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
Pearson'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
THE 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 -
Capability Analysis Using Statgraphics Centurion
Capability Analysis Using Statgraphics Centurion Neil W. Polhemus, CTO, StatPoint Technologies, Inc. Copyright 2011 by StatPoint Technologies, Inc. Web site: www.statgraphics.com Outline Definition of
Lin s Concordance Correlation Coefficient
NSS Statistical Software NSS.com hapter 30 Lin s oncordance orrelation oefficient Introduction This procedure calculates Lin s concordance correlation coefficient ( ) from a set of bivariate data. The
Tests for Two Survival Curves Using Cox s Proportional Hazards Model
Chapter 730 Tests for Two Survival Curves Using Cox s Proportional Hazards Model Introduction A clinical trial is often employed to test the equality of survival distributions of two treatment groups.
NCSS 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
Non-Inferiority Tests for Two Means using Differences
Chapter 450 on-inferiority Tests for Two Means using Differences Introduction This procedure computes power and sample size for non-inferiority tests in two-sample designs in which the outcome is a continuous
Tests for Two Proportions
Chapter 200 Tests for Two Proportions Introduction This module computes power and sample size for hypothesis tests of the difference, ratio, or odds ratio of two independent proportions. The test statistics
Randomized Block Analysis of Variance
Chapter 565 Randomized Block Analysis of Variance Introduction This module analyzes a randomized block analysis of variance with up to two treatment factors and their interaction. It provides tables of
Data Analysis Tools. Tools for Summarizing Data
Data Analysis Tools This section of the notes is meant to introduce you to many of the tools that are provided by Excel under the Tools/Data Analysis menu item. If your computer does not have that tool
Sampling Strategies for Error Rate Estimation and Quality Control
Project Number: JPA0703 Sampling Strategies for Error Rate Estimation and Quality Control A Major Qualifying Project Report Submitted to the faculty of the Worcester Polytechnic Institute in partial fulfillment
Statistical 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
Practice problems for Homework 12 - confidence intervals and hypothesis testing. Open the Homework Assignment 12 and solve the problems.
Practice problems for Homework 1 - confidence intervals and hypothesis testing. Read sections 10..3 and 10.3 of the text. Solve the practice problems below. Open the Homework Assignment 1 and solve the
Non-Inferiority Tests for Two Proportions
Chapter 0 Non-Inferiority Tests for Two Proportions Introduction This module provides power analysis and sample size calculation for non-inferiority and superiority tests in twosample designs in which
THE 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
Unit 26 Estimation with Confidence Intervals
Unit 26 Estimation with Confidence Intervals Objectives: To see how confidence intervals are used to estimate a population proportion, a population mean, a difference in population proportions, or a difference
Scatter 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
12.5: CHI-SQUARE GOODNESS OF FIT TESTS
125: Chi-Square Goodness of Fit Tests CD12-1 125: CHI-SQUARE GOODNESS OF FIT TESTS In this section, the χ 2 distribution is used for testing the goodness of fit of a set of data to a specific probability
Binary Diagnostic Tests Two Independent Samples
Chapter 537 Binary Diagnostic Tests Two Independent Samples Introduction An important task in diagnostic medicine is to measure the accuracy of two diagnostic tests. This can be done by comparing summary
Two Correlated Proportions (McNemar Test)
Chapter 50 Two Correlated Proportions (Mcemar Test) Introduction This procedure computes confidence intervals and hypothesis tests for the comparison of the marginal frequencies of two factors (each with
Instruction 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
STATISTICAL 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
LAB 4 INSTRUCTIONS CONFIDENCE INTERVALS AND HYPOTHESIS TESTING
LAB 4 INSTRUCTIONS CONFIDENCE INTERVALS AND HYPOTHESIS TESTING In this lab you will explore the concept of a confidence interval and hypothesis testing through a simulation problem in engineering setting.
NCSS 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
Getting 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
How To Check For Differences In The One Way Anova
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. One-Way
Standard Deviation Estimator
CSS.com Chapter 905 Standard Deviation Estimator Introduction Even though it is not of primary interest, an estimate of the standard deviation (SD) is needed when calculating the power or sample size of
Coefficient of Determination
Coefficient of Determination The coefficient of determination R 2 (or sometimes r 2 ) is another measure of how well the least squares equation ŷ = b 0 + b 1 x performs as a predictor of y. R 2 is computed
Non-Inferiority Tests for One Mean
Chapter 45 Non-Inferiority ests for One Mean Introduction his module computes power and sample size for non-inferiority tests in one-sample designs in which the outcome is distributed as a normal random
Odds ratio, Odds ratio test for independence, chi-squared statistic.
Odds ratio, Odds ratio test for independence, chi-squared statistic. Announcements: Assignment 5 is live on webpage. Due Wed Aug 1 at 4:30pm. (9 days, 1 hour, 58.5 minutes ) Final exam is Aug 9. Review
Bowerman, O'Connell, Aitken Schermer, & Adcock, Business Statistics in Practice, Canadian edition
Bowerman, O'Connell, Aitken Schermer, & Adcock, Business Statistics in Practice, Canadian edition Online Learning Centre Technology Step-by-Step - Excel Microsoft Excel is a spreadsheet software application
Lecture Notes Module 1
Lecture Notes Module 1 Study Populations A study population is a clearly defined collection of people, animals, plants, or objects. In psychological research, a study population usually consists of a specific
SAMPLE 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.
Two Related Samples t Test
Two Related Samples t Test In this example 1 students saw five pictures of attractive people and five pictures of unattractive people. For each picture, the students rated the friendliness of the person
Control 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
Sample Size and Power in Clinical Trials
Sample Size and Power in Clinical Trials Version 1.0 May 011 1. Power of a Test. Factors affecting Power 3. Required Sample Size RELATED ISSUES 1. Effect Size. Test Statistics 3. Variation 4. Significance
Tests for One Proportion
Chapter 100 Tests for One Proportion Introduction The One-Sample Proportion Test is used to assess whether a population proportion (P1) is significantly different from a hypothesized value (P0). This is
Chapter 23. Inferences for Regression
Chapter 23. Inferences for Regression Topics covered in this chapter: Simple Linear Regression Simple Linear Regression Example 23.1: Crying and IQ The Problem: Infants who cry easily may be more easily
Hypothesis testing - Steps
Hypothesis testing - Steps Steps to do a two-tailed test of the hypothesis that β 1 0: 1. Set up the hypotheses: H 0 : β 1 = 0 H a : β 1 0. 2. Compute the test statistic: t = b 1 0 Std. error of b 1 =
How To Test For Significance On A Data Set
Non-Parametric Univariate Tests: 1 Sample Sign Test 1 1 SAMPLE SIGN TEST A non-parametric equivalent of the 1 SAMPLE T-TEST. ASSUMPTIONS: Data is non-normally distributed, even after log transforming.
Gamma Distribution Fitting
Chapter 552 Gamma Distribution Fitting Introduction This module fits the gamma probability distributions to a complete or censored set of individual or grouped data values. It outputs various statistics
Probability Distributions
CHAPTER 5 Probability Distributions CHAPTER OUTLINE 5.1 Probability Distribution of a Discrete Random Variable 5.2 Mean and Standard Deviation of a Probability Distribution 5.3 The Binomial Distribution
A 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
Using Stata for One Sample Tests
Using Stata for One Sample Tests All of the one sample problems we have discussed so far can be solved in Stata via either (a) statistical calculator functions, where you provide Stata with the necessary
Gage 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
Paired T-Test. Chapter 208. Introduction. Technical Details. Research Questions
Chapter 208 Introduction This procedure provides several reports for making inference about the difference between two population means based on a paired sample. These reports include confidence intervals
Chapter 2. Hypothesis testing in one population
Chapter 2. Hypothesis testing in one population Contents Introduction, the null and alternative hypotheses Hypothesis testing process Type I and Type II errors, power Test statistic, level of significance
Assessing 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
AP STATISTICS (Warm-Up Exercises)
AP STATISTICS (Warm-Up Exercises) 1. Describe the distribution of ages in a city: 2. Graph a box plot on your calculator for the following test scores: {90, 80, 96, 54, 80, 95, 100, 75, 87, 62, 65, 85,
Good luck! BUSINESS STATISTICS FINAL EXAM INSTRUCTIONS. Name:
Glo bal Leadership M BA BUSINESS STATISTICS FINAL EXAM Name: INSTRUCTIONS 1. Do not open this exam until instructed to do so. 2. Be sure to fill in your name before starting the exam. 3. You have two hours
Chapter 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
SPC Demonstration Tips
Tip Sheet SPC Demonstration Tips Key Points to Cover When Demonstrating Ignition SPC Downtime In general, the SPC Module is designed with a great level of flexibility to support a wide variety of production
Directions for using SPSS
Directions for using SPSS Table of Contents Connecting and Working with Files 1. Accessing SPSS... 2 2. Transferring Files to N:\drive or your computer... 3 3. Importing Data from Another File Format...
Simple Linear Regression Inference
Simple Linear Regression Inference 1 Inference requirements The Normality assumption of the stochastic term e is needed for inference even if it is not a OLS requirement. Therefore we have: Interpretation
3.4 Statistical inference for 2 populations based on two samples
3.4 Statistical inference for 2 populations based on two samples Tests for a difference between two population means The first sample will be denoted as X 1, X 2,..., X m. The second sample will be denoted
A Basic Guide to Analyzing Individual Scores Data with SPSS
A Basic Guide to Analyzing Individual Scores Data with SPSS Step 1. Clean the data file Open the Excel file with your data. You may get the following message: If you get this message, click yes. Delete
2 Precision-based sample size calculations
Statistics: An introduction to sample size calculations Rosie Cornish. 2006. 1 Introduction One crucial aspect of study design is deciding how big your sample should be. If you increase your sample size
Fixed-Effect Versus Random-Effects Models
CHAPTER 13 Fixed-Effect Versus Random-Effects Models Introduction Definition of a summary effect Estimating the summary effect Extreme effect size in a large study or a small study Confidence interval
Getting Started with Minitab 17
2014 by Minitab Inc. All rights reserved. Minitab, Quality. Analysis. Results. and the Minitab logo are registered trademarks of Minitab, Inc., in the United States and other countries. Additional trademarks
MBA 611 STATISTICS AND QUANTITATIVE METHODS
MBA 611 STATISTICS AND QUANTITATIVE METHODS Part I. Review of Basic Statistics (Chapters 1-11) A. Introduction (Chapter 1) Uncertainty: Decisions are often based on incomplete information from uncertain
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
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
Learning Objectives. Understand how to select the correct control chart for an application. Know how to fill out and maintain a control chart.
CONTROL CHARTS 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
INFLUENCE OF MEASUREMENT SYSTEM QUALITY ON THE EVALUATION OF PROCESS CAPABILITY INDICES
METALLURGY AND FOUNDRY ENGINEERING Vol. 38, 2012, No. 1 http://dx.doi.org/10.7494/mafe.2012.38.1.25 Andrzej Czarski*, Piotr Matusiewicz* INFLUENCE OF MEASUREMENT SYSTEM QUALITY ON THE EVALUATION OF PROCESS
Experimental Design. Power and Sample Size Determination. Proportions. Proportions. Confidence Interval for p. The Binomial Test
Experimental Design Power and Sample Size Determination Bret Hanlon and Bret Larget Department of Statistics University of Wisconsin Madison November 3 8, 2011 To this point in the semester, we have largely
Statistical 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
5.1 Identifying the Target Parameter
University of California, Davis Department of Statistics Summer Session II Statistics 13 August 20, 2012 Date of latest update: August 20 Lecture 5: Estimation with Confidence intervals 5.1 Identifying
Process Capability Analysis Using MINITAB (I)
Process Capability Analysis Using MINITAB (I) By Keith M. Bower, M.S. Abstract The use of capability indices such as C p, C pk, and Sigma values is widespread in industry. It is important to emphasize
NCSS Statistical Software Principal Components Regression. In ordinary least squares, the regression coefficients are estimated using the formula ( )
Chapter 340 Principal Components Regression Introduction is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates
t Tests in Excel The Excel Statistical Master By Mark Harmon Copyright 2011 Mark Harmon
t-tests in Excel By Mark Harmon Copyright 2011 Mark Harmon No part of this publication may be reproduced or distributed without the express permission of the author. [email protected] www.excelmasterseries.com
Simulation and Lean Six Sigma
Hilary Emmett, 22 August 2007 Improve the quality of your critical business decisions Agenda Simulation and Lean Six Sigma What is Monte Carlo Simulation? Loan Process Example Inventory Optimization Example
THE USE OF STATISTICAL PROCESS CONTROL IN PHARMACEUTICALS INDUSTRY
THE USE OF STATISTICAL PROCESS CONTROL IN PHARMACEUTICALS INDUSTRY Alexandru-Mihnea SPIRIDONICĂ 1 E-mail: [email protected] Abstract The use of statistical process control has gained a major
NCSS Statistical Software. One-Sample T-Test
Chapter 205 Introduction This procedure provides several reports for making inference about a population mean based on a single sample. These reports include confidence intervals of the mean or median,
Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression
Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression Objectives: To perform a hypothesis test concerning the slope of a least squares line To recognize that testing for a
Lesson 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
STATISTICAL 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
Need for Sampling. Very large populations Destructive testing Continuous production process
Chapter 4 Sampling and Estimation Need for Sampling Very large populations Destructive testing Continuous production process The objective of sampling is to draw a valid inference about a population. 4-
Adverse 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
KSTAT MINI-MANUAL. Decision Sciences 434 Kellogg Graduate School of Management
KSTAT MINI-MANUAL Decision Sciences 434 Kellogg Graduate School of Management Kstat is a set of macros added to Excel and it will enable you to do the statistics required for this course very easily. To
Sample Size Planning, Calculation, and Justification
Sample Size Planning, Calculation, and Justification Theresa A Scott, MS Vanderbilt University Department of Biostatistics [email protected] http://biostat.mc.vanderbilt.edu/theresascott Theresa
Measurement Systems Correlation MSC for Suppliers
Measurement Systems Correlation MSC for Suppliers Copyright 2003-2007 Raytheon Company. All rights reserved. R6σ is a Raytheon trademark registered in the United States and Europe. Raytheon Six Sigma is
Statistical Quality Control
Statistical Quality Control CHAPTER 6 Before studying this chapter you should know or, if necessary, review 1. Quality as a competitive priority, Chapter 2, page 00. 2. Total quality management (TQM) concepts,
Chapter 3 RANDOM VARIATE GENERATION
Chapter 3 RANDOM VARIATE GENERATION In order to do a Monte Carlo simulation either by hand or by computer, techniques must be developed for generating values of random variables having known distributions.
Exact Confidence Intervals
Math 541: Statistical Theory II Instructor: Songfeng Zheng Exact Confidence Intervals Confidence intervals provide an alternative to using an estimator ˆθ when we wish to estimate an unknown parameter
A Multi-Stream Process Capability Assessment Using a Nonconformity Ratio Based Desirability Function
A Multi-Stream Process Capability Assessment Using a Nonconformity Ratio Based Desirability Function Thesis for the Acquisition of the Degree of a Doctor in Natural Sciences of the University of Dortmund
EXCEL Tutorial: How to use EXCEL for Graphs and Calculations.
EXCEL Tutorial: How to use EXCEL for Graphs and Calculations. Excel is powerful tool and can make your life easier if you are proficient in using it. You will need to use Excel to complete most of your
4. Continuous Random Variables, the Pareto and Normal Distributions
4. Continuous Random Variables, the Pareto and Normal Distributions A continuous random variable X can take any value in a given range (e.g. height, weight, age). The distribution of a continuous random
Lean Six Sigma Analyze Phase Introduction. TECH 50800 QUALITY and PRODUCTIVITY in INDUSTRY and TECHNOLOGY
TECH 50800 QUALITY and PRODUCTIVITY in INDUSTRY and TECHNOLOGY Before we begin: Turn on the sound on your computer. There is audio to accompany this presentation. Audio will accompany most of the online
