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Sigma Six Current Quality Mini-Paper September 28, 2001 Tiffany Raisch Josh Anderson Kelli Krogman Becky Krueger Iowa State University Ames, IA IE 361 Dr. Vardeman If they don t get the quality they need from us, they will get it from someone else. -Stephen Schwartz, Senior VP, IBM The Greek letter sigma ( ) is used to represent the standard deviation, a measure of variability. The concept of Six Sigma is to accept variation of plus or minus six sigma from the mean, or 3.4 defects per million parts. To achieve Six Sigma in terms of quality, defects have to virtually be eliminated. According to Six Sigma Qualtec, most US companies are achieving three or four sigma which translates into a 10-15 percent loss of their total revenue just from defects. Six Sigma can lead to success for more than just the manufacturing world; it is relevant in service industries as well (www.sixsigmaqualtec.com). According to Les Shroyer, Motorola s Chief Information Officer, Six Sigma can be broken into a six-step process. First, identify your product. What are you making or providing as a service? Second, identify customer requirements. Find out what your customer is dissatisfied with or the potential reason they may not purchase from you again. Third, diagnose the frequency and source of errors. Find out where the problem is initially starting. Fourth, define a process for doing the task. This is referred to as mapping at Motorola. Ask yourself what steps of the process are absolutely necessary and how the process can be simplified. Fifth, mistake-proof the process. Remove all unnecessary cycle time or steps that are only leaving room for error in the process. Sixth, put permanent control measures in place. Determine how you are going to measure your progress and remain at Six Sigma. According to Shroyer, We re at the mentality that if you can t measure it, you can t control it (Rifkin). There is no one procedure defined to achieve Six Sigma, nor will it happen overnight. To successfully achieve this, it must be a company-wide effort that takes place over a period of up to five years. As companies saw how Motorola went about the process of achieving Six Sigma, many followed in a similar manner. Teams were compiled of people coming from different levels of the company, including managers, engineers, and operators. Everyone was trained in statistical process control, including how to use statistical tools. Every team was assigned to a different problem, with each team member having different responsibilities. The operators were taught how to interpret and respond to different charts. Their role of filling in the chart correctly was the backbone of the entire project (Kumar, Gupta 88). Engineers were trained in advanced statistics and were responsible for using these charts to foresee the future. Management s primary training was in presenting and interpreting the statistical process control data. These teams would meet often, discussing problems they were each facing and creating ways to permanently solve them using the design-of-experiments approach (Kumar, Gupta 90). As an incentive to all employees, Motorola decided to recognize employees for their dedication to quality by rewarding them, either by a certificate and recognition or even extra vacation. The benefits from achieving Six Sigma are well worth the time and investment made. According to Six Sigma Systems: Six Sigma reduces cost by 50 % or more through a self-funded approach to improvement. Six Sigma reduces the waste chain. Six Sigma affords a better understanding of customer requirements. Six Sigma improves delivery and quality performance. Six Sigma provides critical process inputs needed to respond to changing customer requirements. Six Sigma develops robust products and processes. Six Sigma drives improvements rapidly with internal resources (Six Sigma Systems 1). History of Six Sigma Six Sigma has breathed new life into the quality and profitability of many goods and services produced by American companies. For years, the focus of quality programs in America consisted of finding the defects of a finished good and reacting to that information. Six Sigma allows companies to take a more proactive approach to quality (Harry and Schroeder). This new paradigm has greatly increased the financial results of companies and allowed them to produce goods and services of much higher quality. Six Sigma and the implementation procedures that have made it so successful were developed by Motorola in the mid 1980 s. Motorola set out to significantly improve the quality of the products it manufactured. They found that by using statistical methods to improve all aspects of their operations, they could produce higher quality products at a lower cost. This disproved the previous notion that quality costs more money. By fixing problems at the root and designing out problems, Motorola was able to produce products virtually free of defects. They changed the language of quality in America by beginning to measure defects out of opportunities or parts per million (and even billion), instead of parts per hundred (www.qualitydigest.com). One important aspect of Six Sigma developed by Motorola is the implementation procedure. By developing different levels of training in Six Sigma procedures, they were able to ensure that only those with the proper training were given leadership roles. They equipped people with titles such as Champion, Master Black Belts, Black Belts, and Green Belts, based on their training and responsibilities. (http://mu.motorola.com). This organized approach of using people effectively allowed Six Sigma to be successful at Motorola. As the success of Six Sigma at Motorola became known, other large corporations developed Six Sigma programs. Results continued to bring value to the new way of doing business, and soon companies like General Electric and IBM embraced Six Sigma programs to help their companies improve market share and profits. General Electric Capital Commercial Finance and Six Sigma General Electric did not begin focusing on quality until the late '80's. Jack Welch, CEO of GE, was "open to change, hungry to learn, and anxious to move quickly on a good idea." He later announced he would invest more than $200 million in attaining Six Sigma quality levels. In 1996, implementation of Six Sigma began. That year, GE earned a return of $880 million on their initial investment of $580 million (www.sixsigmaqualtec.com). Today, Six Sigma is defining how GE works and has set the standard for helping reap customers the benefits of Six Sigma. In late 1996, GE Capital Commercial Finance considered joining their parent company's initiative. They realized to that point, "We had made almost no progress whatsoever," says Managing Director of Quality Steve Sargent. "We had no customer, process or employee measurements. Our people had not bought into the quality initiative. We had no way of knowing how well or how poorly we were doing." He later compared the Commercial Finance organization to a car that didn't have a steering wheel, a dash-board, an air conditioner or a radio. "All we had was an accelerator and a rear view mirror, and we got from point A to point B by putting our foot on the accelerator and getting to wherever we were going through brute force. We needed the works so we could figure out where to go, how to get there and how fast," Sargent states (www.sixsigmaqualtec.com). The Commercial Finance organization worked quickly to install a robust business performance measurement system, which could monitor their business processes, their customers and employees, and measure the results of their efforts. Within three months, Sargent and his cross-functional quality team collected and analyzed the market, customer and employee data, mapped nearly fifty core business processes, established key indicators and began to implement targeted actions to improve the business. Today GE's Commercial Finance is a different kind of organization. It has built a comprehensive balanced scorecard system that gathers, analyzes and reports information on customers, processes, employees, markets and financial performance. It has clear and defined processes. In addition, each month it conducts management reviews, during which performance data is presented and used to initiate improvement projects. Their system is a success. The Commercial Finance organization closed 166 percent more deals in 1997 than it did in 1996, without an increase in head count (www.sixsigmaqualtec.com). This, plus other Six Sigma successes at GE account for an estimated gross annual benefit of five percent sales, which could increase by between 10 and 15 percent. Jack Welch later praised Six Sigma has galvanized our company with an intensity the likes of which I have never seen in my 40 years at GE (Basu, Ron 27). Six Sigma at IBM Another company that has used Six Sigma to increase the quality of their products is International Business Machines (IBM). In the early 1980 s, IBM was one of the world s leaders in the production of mainframes and personal computers (PCs). In 1986, however, the company began losing market share among its major customers in both mainframes and PCs. To combat this problem, IBM hired a new chairman by the name of John Akers to reestablish the company as a technology and product leader. In his first four years as chairman, Akers restructured the entire company, removing almost 37,000 management and workforce jobs while increasing the number of sales and service personnel by 20%. Despite these changes, there were still quality problems along with low morale of the remaining workers (Rayner). To combat these problems, Akers changed the focus from restructuring to dealing with the larger problem of poor quality products. After a session at Motorola Inc., IBM was determined to use the Six Sigma approach to eliminate defects in their products. The goal of the company was to reduce the number of defects by a factor of 20,000 and cut the product cycle time in half. A program entitled Market-Driven Quality (MDQ) was initiated to achieve Six Sigma. MDQ has three components: a set of quality initiatives, a system of process review, and a system of quality measurement. By 1991, all the managers at IBM had attended a MDQ training session in which they learned quality basics such as defining initiatives, deploying a quality-based process-management system, applying the Baldridge criteria, implementing Six Sigma, benchmarking and measuring quality. These managers then took the training material back to their employees and trained them in the same quality basics. All parts of the company, including non-manufacturing segments, were required to adapt the MDQ program (Rayner). The changes that the company made towards improving quality by using MDQ to achieve Six Sigma were very successful in the early 1990 s. According to PC magazine, IBM received A s for quality and customer service on their PC s from 1992-99. However, in the late 1990 s, IBM changed their focus. The company objective is now to strive to lead in the creation, development and manufacture of the industry's most advanced information technologies, including computer systems, software, networking systems, storage devices and microelectronics. Because of these changes, IBM has recently received ratings as low as C s on quality and customer service (www.ibm.com). Is Six-Sigma always the answer? Many organizations, including the venerable GE, have benefited from the development of Six Sigma. Nevertheless, like other processes, if it is not implemented and carried-out correctly, it will never reach its full potential. As IBM and other companies are learning, altering a current process is not always the right decision. The logical and statistical methods that form the backbone of Six Sigma are reinventing the way companies do business. Works Cited Basu, Ron. Six sigma to Fit Sigma. IIE Solutions, July 2001. General Electric: http://www.ge.com/sixsigma/ Harry, Mikel, Ph.D., and Richard Schroeder. Six Sigma. New York, New York : Doubleday, 2000. International Business Machines: www.ibm.com Kumar, Sanjoy, Gupta, Yash P. Statistical Process Control at Motorola s Austin Assembly Plant. The Institute of Management Sciences. March-April 1993 pp 84-92. Motorola University: http://mu.motorola.com/sigma.shtml PC Magazine, August 1. 2001 Quality Digest: http://www.qualitydigest.com/dec97/html/motsix.html Rayner, Bruce. Market-Driven Quality: IBM s Six Sigma Crusade. Electronic Business, Oct. 15, 1990. Rifkin, Glenn. No More Defects! Computerworld. July 15, 1991. Six Sigma Qualtec: What is Six Sigma? http://www.sixsigmaqualtec.com/Products/sixsigma/what.htm GE Capital Commercial Finance Leverages Process and People for Sustainable Business Results. http://www.sixsigmaqualtec.com/News/ge_finance.html Six Sigma Is More Than A Lofty Notion. http://www.sixsigmaqualtec.com/News/notion.html Six Sigma Systems: What is Six Sigma and Lean Manufacturing? http://www.sixsigmasystems.com/what_is_six_sigma.htm
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Iowa State >> PUBLIC >> 0622 (Fall, 2009)
Iowa City Press Citizen, IA 06-20-07 Sophie Donta had a major reason for wanting to learn French: her family. Rob Daniel Her grandmother, Antoinette Begin, and mother, Helene Donta, came to Iowa from the city of Nancy in northeastern France when Hele...
Iowa State >> ECON >> 581 (Spring, 2008)
ECONOMICS 581: Advanced Environmental Economics Jinhua Zhao jzhao@iastate.edu 377 Heady, 294-5857 Spring 2008 MWF, 1:10-2:00pm 272 Heady This course includes two major parts. The first part introduces the economic theory of externalities. We will em...
Iowa State >> ECON >> 581 (Spring, 2008)
evie eve version received My 1 ence to: ler, ks, es, I 1264 L-D. Ko et al., Controlling stock externalities accumulation of greenhouse gases. Tops contamination represent other stock extern bsidy that either remains uncha tuity, or else ch...
Iowa State >> ECON >> 571 (Fall, 2008)
Economics 571 Problem Set #10 (1) Wooldridge 17.1. This question refers to the \"fraction correctly predicted\" for various groups. By convention, if Pr(yi = 1|x) pi > .5, we would predict yi = 1. So, the fraction ^ correctly predicted for the y = 1 s...
Iowa State >> ECON >> 571 (Fall, 2008)
Prof. Wallace Huffman ECON 571 Reading List Spring 2005 I. Introduction Wooldridge, Introductory Econometrics, South-Western Pub. 2nd Ed., Ch. 1, pp. 1-19. Intriligator, Bodkin, and Hsiao, Econometric Models, Techniques, and Applications, 2nd Ed.,...
Iowa State >> ECON >> 571 (Fall, 2008)
Economics 571 Problem Set #7 Solutions (1) First, note that the null imposes the restriction 1 = -2 . Thus, the restricted model can be written as voteA = 0 + 1 (logexpendA - logexpendB) + beta3 prtystrA + u. 2 We run both the restricted and unrestri...
Iowa State >> ECON >> 571 (Fall, 2008)
Economics 571 Problem Set #3 Solutions (1) Note: y = 0 + 1 x + u = 0 + 1 x + 0 + (u 0 ) = (0 + 0 ) + 1 x + = 0 + 1 x + where 0 = 0 + 0 and = u 0 . Note E( ) = E(u 0 ) = E(u) 0 = 0 0 = 0. Thus, the regression model can be written as one with a ne...
Iowa State >> ECON >> 571 (Fall, 2008)
Economics 571 Problem Set #5 Solutions (1a) In vector and matrix form, the regression equation for all the data can be written as 2.8 3.4 3.0 3.5 3.6 3.0 2.7 3.7 = 1 1 1 1 1 1 1 1 21 24 26 27 29 25 25 30 ...
Iowa State >> ECON >> 571 (Fall, 2008)
Economics 571 Problem Set #6 Solutions Wooldridge, 3.9 (i) I would expect 1 to be negative and 2 to be positive. The more pollution in a given community, the less someone would be willing to pay for a home in that community (all else equal), so that ...
Iowa State >> ECON >> 571 (Fall, 2008)
Economics 571 Problem Set #2 Solutions (1a) Pr(X 7) = Pr([X 3]/4 [7 3]/4) = Pr(Z 1) = .841 In the above Z N (0, 1), and the last entry is read from the Normal tables (815 and 816 of your book). (1b) Pr(X > 5) = Pr([X 3]/4 > [5 3]/4) = Pr(Z > ...
Iowa State >> ECON >> 571 (Fall, 2008)
Economics 571 Problem Set #2 (1) The random variable X is distributed as X N (3, 16), a normal distribution with mean 3 and variance 16. Calculate the following: (a) Pr(X 7). (b) Pr(X > 5). (c) Pr(|X| 3). (2) The random variable X is distributed a...
Iowa State >> ECON >> 571 (Fall, 2008)
Economics 571 Problem Set #4 (1) Go back to problem set 3, exercise 2.3 in Wooldridge. (1a) Using the data provided, and formulas presented in the lecutres, calculate an unbiased estimator of the error variance parameter, 2 . ^ ^ (1b) Use your resul...
Iowa State >> ECON >> 571 (Fall, 2008)
Economics 571 Problem Set #1 Solutions (1a) 0 p(x) dx = 0 exp(-x)dx 0 = - exp(-x) = 0 + 1 = 1. (2a) E(x) = 2 0 1/2(x) dx 2 0 = (1/4)x2 = (1/4)4 = 1 (2b) E(x2 ) = 2 0 (1/2)x2 dx 2 0 = (1/6)x3 = 8/6. Thus, Var(x) = 8/6 - 1 = 1/3. (2c) We...
Iowa State >> ECON >> 571 (Fall, 2008)
SYLLABUS Economics 571: Intermediate Econometrics Spring 2008 Professor: Justin L. Tobias Oce: 367 Heady Oce Hours: Wednesday 2:00-4:00 email: tobiasj@iastate.edu Course Web Site: http:/www.econ.iastate.edu/classes/econ571/tobias/index.htm Teaching A...
Iowa State >> ECON >> 571 (Fall, 2008)
Economics 571 Lab Outline 1. Go to the course website http:/www.econ.iastate.edu/classes/econ571/tobias/ under the lab materials section, and click on the link for the California Test Score data in text format. You might also want to take a look at ...
Iowa State >> ECON >> 571 (Fall, 2008)
Economics 571 Problem Set #3 (1) Wooldridge, 2.2. (2) Wooldridge, 2.3 parts (i) - (iii). (3) Wooldridge, 2.4 parts (i) and (ii). (4) Wooldridge, 2.5 parts (i) and (ii). (5) Consider the simple regression model: yi = 0 + 1 Di + ui , where yi denotes ...
Iowa State >> ECON >> 571 (Fall, 2008)
Economics 571 Problem Set #8 Solutions The regression output is provided as a separate attachment. (1a) Generally the results are consistent with what I would expect. In terms of point estimates, if the spouse earns more, than the female is likely to...
Iowa State >> ECON >> 571 (Fall, 2008)
Economics 571 Problem Set #1 (1) Suppose that a random variable x has an exponential density with parameter > 0: p(x) = exp(-x), x 0. Thus, the distribution places no mass to the left of zero. Show that the exponential density is a proper density ...
Iowa State >> ECON >> 571 (Fall, 2008)
Economics 571 Problem Set #8 (1) Get the data labsupply.txt found on the course web site. This question will require you to perform some simple analysis of this data using STATA. Note: This data set is a text le and is not in STATA format. So, you wi...
Iowa State >> ECON >> 571 (Fall, 2008)
Economics 571 Problem Set #4 Solutions (1) Using the formula 2 = [1/(n 2)] with n = 8, we obtain 2 = .0725. As for the estimated variances, we make use of the formulas Var(1 ) = and Var(0 ) = to obtain Var(1 ) = .0013 and Var(0 ) = .8625. (2...
Iowa State >> ECON >> 571 (Fall, 2008)
Economics 571 Problem Set #7 (1) Go back to problem set #6, question C4.1 in Wooldridge. Carry out the test in (iii) that the increase in As expeditures is oset by an increase in Bs expenditures. In this case, perform the test by running the appropri...
Iowa State >> ECON >> 571 (Fall, 2008)
Economics 571 Problem Set #9 (1) Wooldridge, 15.1, all parts. In addition to the identification strategy suggested in (iii), can you think of any other strategies for estimating the \"causal\" impact of P C on GP A? (2) Wooldridge, 15.2, all parts. You...
Iowa State >> ECON >> 571 (Fall, 2008)
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Iowa State >> ECON >> 571 (Fall, 2008)
Economics 571 Problem Set #6 (1) Wooldridge, Exercise 3.9. (2) Wooldridge, Exercise C3.4. (3) Wooldridge, Exercise 4.5. (4) Wooldridge, C4.5, parts (i) and (ii). (Note that \"lsalary\" in the data set is the log of salary). ...
Iowa State >> ECON >> 571 (Fall, 2008)
Economics 571 Problem Set #5 (1) Go back to Exercise 2.3. (1a) Show how you would stack this regression equation into vector and matrix form, as discussed in class. (Note: I want you to insert the particular observations provided in the table into ve...
Iowa State >> ECON >> 571 (Fall, 2008)
LawofLargeNumbers(LLN) and CentralLimitTheorem(CLT) Tobias Econ472 ConsistencyandLLN Asshowninclass,alawoflargenumbersisa powerfultheoremthatcanbeusedtoestablish theconsistencyofanestimator. Weillustratewhatwemeanbyconsistencyby showingwhathappe...
Iowa State >> ECON >> 571 (Fall, 2008)
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Iowa State >> ECON >> 571 (Fall, 2008)
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Iowa State >> ECON >> 571 (Fall, 2008)
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Iowa State >> ECON >> 571 (Fall, 2008)
Intermediate Econometrics (Econ 571) Iowa State University, Spring 2003 William M. Boal Signature: Printed name: HOMEWORK ASSIGNMENT #12.5 \"Nonlinear least-squares and maximum likelihood estimation\" Due Friday, April 18 INSTRUCTIONS: Write answers ...
Iowa State >> ECON >> 571 (Fall, 2008)
log using l:\\2007_2008\\econ571\\solutions\\6a, replace t use l:\\2007_2008\\econ571\\stata_data\\attend describe regress atndrte priGPA ACT clear use l:\\2007_2008\\econ571\\stata_data\\mlb1 regress lsalary years gamesyr bavg hrunsyr rbisyr regress lsalary ye...
Iowa State >> ECON >> 571 (Fall, 2008)
log using l:\\2007_2008\\econ571\\solutions\\5a, replace t use l:\\2007_2008\\econ571\\stata_data\\bwght regress bwght cigs regress bwght cigs faminc correlate cigs faminc log close clear . . regress bwght cigs Source | SS df MS ...
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Iowa State >> EE >> 527 (Fall, 2009)
Registration and Landmark Shape Analysis Namrata Vaswani, namrata@iastate.edu Registration and Landmark Shape Analysis 1 Registration Given a set of corresponding points in 2 frames, nd a global map (usually a group action) that takes one set of ...
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High-Energy Astrophysics, fall semester 2008 Homework, 3rd set, solutions Problem 5: Cosmic-ray source spectra a) The Boron source spectrum is essentiall identical to the density spectrum of Carbon, if we concentrate on relativistic particles with E...
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Iowa State >> STAT >> 580 (Spring, 2008)
On \"On the accuracy of statistical procedures in Microsoft Excel 2003\" David Faden, Stat 580, Spring 2005 The authors evaluate Excel 2003 in three areas statistical distributions, estimation, and random number generation and find it to be deficient...
Iowa State >> MAY >> 0426 (Fall, 2009)
Chapter 4 - RSA Cipher In this Chapter I am going to show you how highly secure communication is realized. We are leaving behind the breakable ciphers that we studied in the previous chapters. Rather, we are going to use learned concepts such as MOD ...
Bowling Green >> BIOLOGY >> 200 (Fall, 2009)
Blackwell Science, LtdOxford, UKMMIMolecular Microbiology 1365-2958Blackwell Publishing Ltd, 2003503857870Original ArticleE. R. Sumner et al.Sod1p-dependent heterogeneity Molecular Microbiology (2003) 50(3), 857870 doi:10.1046/j.1365-2958.2003.0371...
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ON INTER-REFERENTIAL AWARENESS IN COLLABORATIVE AUGMENTED REALITY by JEFFREY W CHASTINE Under the Direction of Ying Zhu ABSTRACT For successful collaboration to occur, a workspace must support inter-referential awareness or the ability for one part...
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WHEN IT HITS THE FAN: A PUBLIC RELATIONS PRACTITIONERS\' GUIDE TO CRISIS COMMUNICATION by JOSHUA L. SMITH Under the Direction of David M. Cheshier ABSTRACT This project is designed as an aid to those interested in practicing, resear...
Iowa State >> CI >> 280 (Fall, 2009)
Curriculum & Instruction 280A PRE-STUDENT TEACHING EXPERIENCE Jason Follett N107 Lagomarcino Hall PHONE: 515-294-3844 FAX: 515-294-6206 EMAIL: jfollett@iastate.edu GENERAL INFORMATION INTRODUCTION TO FIELD EXPERIENCES From a student\'s first semest...
Iowa State >> MATH >> 165 (Fall, 2008)
F08 Math165 F2 Review Notes Chapter 4 1. Introduction to Area (a) (i) notation: n i=1 n i=1 n ai = a1 + a2 + + an n n (ai bi ) = n i=1 ai i=1 bi kai = k i=1 n i=1 ai k = nk (ii) Some important formulas: n n(n + 1) i= 2 n(n + 1)(2n + 1) 6...
Iowa State >> ECON >> 321 (Fall, 2008)
The Effect of Adolescent Experience on Labor Market Outcomes: The Case of Height Nicola Persico and Andrew Postlewaite University of Pennsylvania Dan Silverman University of Michigan Taller workers receive a wage premium. Net of differences in fam...
Iowa State >> ECON >> 500 (Fall, 2008)
...
Iowa State >> ECON >> 500 (Fall, 2008)
Independence De.nition Two events are said to be independent if P (A \\ B ) = P (A)P (B ) If P (B ) > 0 (or P (A) > 0), this can be written in terms of conditional probability as P (AjB ) = P (A) (4) P (B jA) = P (B ) The events A and B are independe...
Iowa State >> ECON >> 500 (Fall, 2008)
REVIEW OF SIMPLE UNIVARIATE CALCULUS 1. Approximating curves with lines 1.1. The equation for a line. A linear function of a real variable x is given by y = f(x) = ax + b, a and b are constants (1) The graph of linear equation is a straight line. T...
Iowa State >> ECON >> 500 (Fall, 2008)
PIN 114 123 134 137 186 228 231 265 318 330 347 425 525 638 848 999 * 079 Average Max grade Min grade Exam 2 Overall 90.77 90.59 96.15 98.02 90.38 93.49 79.23 84.45 75.00 84.76 81.54 91.20 90.77 90.28 90.00 94.42 86.15 89.26 53.08 59.77 86.92 93.45 ...
Iowa State >> ECON >> 500 (Fall, 2008)
PIN 231 525 114 228 347 186 265 999 137 425 079 318 134 330 249 848 638 123 Average Max Min St. Dev. Grade 90.67 94.67 84.67 98.00 100.00 88.33 100.00 98.67 87.33 96.00 92.00 98.67 96.00 42.67 91.00 93.67 90.67 100.00 91.28 100.00 42.67 16.19 ...
Iowa State >> ECON >> 500 (Fall, 2008)
Sets, Econ 500, Lecture 1 Helle Bunzel Fall 2006 Helle Bunzel () Sets Fall 2006 1 / 14 A Review of Set Notation De.nition of a Set: A set is any collection of objects. The objects are called elements. If x is an element of the set S, we say th...
Iowa State >> ECON >> 500 (Fall, 2008)
Econ 500 Schroeter Fall 2008 Final Exam (Closed book. Open notes. Do all 5 problems. Weights: #1 - 10%, #2 - 20%, #3 - 15%, #4 - 25%, #5 - 30%.) 1. Given that random variable X is normally distributed with mean 4 and variance 5, evaluate the followin...
Iowa State >> ECON >> 500 (Fall, 2008)
Econ 500 Schroeter Fall 2008 Homework #2 1. The joint probability density function of random variables X and Y is given by: f ( x, y ) = 1 (2 x + y ) 0 < x < 1, 0 < y < 2 4 . 0 otherwise Find the mean and variance of Y conditional on X = 1 . 4 2. ...
Iowa State >> ECON >> 500 (Fall, 2008)
Econ 500 Schroeter Fall 2008 Homework #2 1. Let\' consider s f (x; y) = 1 4 (2x + y) 0 0 < x < 1; 0 < y < 2 : otherwise The marginal distribution of X is given by R 1 2 (2x + y) dy = 4 0 g(x) = 0 4x+2 4 0<x<1 . otherwise It implies that the cond...
Iowa State >> ECON >> 500 (Fall, 2008)
Econ 500 Schroeter Fall 2008 Homework #1 1. Stirling formula: s n! p n e n 2 n : The number of 13 card hands in a deck of 52 is 52 C13 = 52! 13!39! p 2 13 p 2 52 = 6:3944 13 13 e 11 p : 2 39 52 52 e 39 39 e 10 2. Lets partition the popul...
Iowa State >> ECON >> 500 (Fall, 2008)
Econ 500 Schroeter Fall 2008 Homework #3 1. Let consider X1 ; X2 ; :Xn i.i.d. N ( ; s is unknown and 2 = 4 P 1 X = n n Xi and X N i=1 P P P Xp 2= n 2 ) ; 2 n Let now nd the smallest value of n such that s 0:5 0:5 X X X p 2= n + 0:5 0:5 0:95 0:95...
Iowa State >> ECON >> 500 (Fall, 2008)
Econ500,Fall2006,Part1 Instructor:HelleBunzel Office:373HeadyHall Phone:46163 email: hbunzel@iastate.edu Office Hours: M: 11-12, T: 10-11, W: 11-12. CourseAdministration CourseObjective:Econ500isafirstcourseinquantativeanalysis.Themainobjective oft...
Iowa State >> ECE >> 00000089 (Fall, 2009)
Defect detection in correlated noise by Nawanat Eua-Anant A thesis submitted to the graduate faculty in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE Major: Electrical Engineering Program of Study Committee: Aleksandar...
Iowa State >> CMU >> 484 (Fall, 2009)
k t v d { x d k { n k k p { k { k kj t t k p { v k q t j t j k d n { k q d f n k t v d { x d { d p { k { n k k p eg)\'YYw gt\"GgstlT\'y3e\"eot~PeYe{ k w { k { v k x j d | { k w | p p { w { { v k t d x d { d p { k { n k k...
Bowling Green >> ETD >> 11172008 (Fall, 2009)
CHILD ABUSE, RACISM AND THE STATE by Chase Parker Turner Under the Direction of Andrew Jason Cohen ABSTRACT Using a Millian framework, this thesis examines whether or not children are victims of child abuse when they are inculcated with racist b...
Bowling Green >> ETD >> 12152005 (Fall, 2009)
PERMISSION TO BORROW In presenting this thesis as a partial fulfillment of the requirements for an advanced degree from Georgia State University, I agree that the Library of the University shall make it available for inspection and circulation in acc...
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