...Case 1: Century National Bank Checking Account Customers Century National Bank has offices in several cities in the Midwest and in the southeastern part of the U.S. Dr. Robert Lindhal, president and CEO, would like to know the characteristics of his checking account customers. He asked Ms. Sofia Kovalevskaya, Director of Planning, to select a sample of customers and prepare a report. Assume that you work in the Planning Department of the Century National Bank and that you report to Ms. Kovalevskaya. She has called a meeting to discuss this project and has selected you to head a team of three employees of the department to take a random sample of customers and prepare a report for Dr. Lindhal. At this meeting, it is determined that the sample size will be 60 customers from branches in Cincinnati, OH, Atlanta, GA, Louisville, KY and Erie, PA. The variables decided upon are account balance, number of ATM transactions in the past month, number of other bank services used in the past month, whether or not the customer uses a debit card, and whether or not the customer has an interest-bearing checking account. The data resulting from this random sample of 60 checking account customers is presented below and in the accompanying Excel file (See Fig. 1). 1. Prepare a histogram of all balances, regardless of branch location. Describe the distribution. 2. Determine the mean and median of all the balances. Why are these measures different? 3. Determine the standard...
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...100; that is, class percentage = (class relative frequency) x 100. Summary of Graphical Descriptive Methods for Qualitative Data Bar Graph: The categories (classes) of the qualitative variable are represented by bars, where the height of each bar is either the class frequency, the class relative frequency, or the class percentage. Pie Chart: The categories (classes) of the qualitative variable are represented by slices of a pie (circle). The size of each slice is proportional to the class relative frequency. Pareto Diagram: A bar graph with the categories (classes) of the qualitative variable (i.e. the bars) arranged by height in descending order from left to right. Summary of Graphical Descriptive Methods for Quantitative Data Dot Plot: The numerical value of each quantitative measurement in the data set is represented by a dot on a horizontal scale. When data values repeat, the dots are placed above one another vertically. Stem-and-Leaf Display: The numerical value of the quantitative variable is partitioned into a “stem” and a “leaf”. The possible stems are listed in order in a column. The leaf for each quantitative measurement in the data set is placed in the...
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...exercise, you will use SAS/GRAPH to construct figures displaying the means and standard deviations. a. Construct a figure containing side-by-side boxplots using the INTERPOL=BOX option associated with a SYMBOL statement used with PROC GPLOT. ODS RTF file='D:\baileraj\Classes\Fall 2003\sta402\hw\hw5-prob1a.rtf’; proc gplot data=class.meat; title h=1.5 ‘Plot of log(Bacterial count) vs. packaging condition'; title2 h=1 '[boxplot is plotted for each condition]'; symbol1 interpol=box /* plots +/- 2 SD from the mean at each conc */ value=dot; plot logcount*condition; run; ODS RTF CLOSE; [pic] b. Construct a figure with the data points plotted. Superimpose the mean plus/minus 2 std. deviation segments on this plot. ODS RTF file='D:\baileraj\Classes\Fall 2003\sta402\hw\hw5-prob1b.rtf’; proc gplot data=class.meat; title h=1.5 ‘Plot of log(Bacterial count) vs. packaging condition'; title2 h=1 '[mean +/- 2SD is plotted for each condition]'; symbol1 interpol=STD2T /* plots +/- 2 SD from the mean at each conc */ /* T= add top and bottom to each 2 SD diff */ value=dot; plot logcount*condition; run; ODS RTF CLOSE; [pic] c. Construct a horizontal bar chart with the end of the bar at the value of the mean with a 1 SD whisker extending from this bar. /* from http://www.units.muohio.edu/doc/sassystem/sasonlinedocv8/sasdoc/sashtml/gref/zart-bar.htm If you want the error bars to represent a...
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...Measures of Location The average is also referred to as the arithmetic mean or simply the mean. The mean is a measure of the center of the data. Average and mean are used interchangeably to label the result of the sum of all measurements divided by the number of measurements. In mathematical notation the formula for calculating the sample mean is given below. x=x1+x2+…+xnn=i=1nxin If the value given represents the mean of all values in a population it is denoted μ. When the data are from a sample, the calculated value, in this case the mean, is referred to as a statistic. When the data represent the entire population, the value is referred to as a parameter. The primary goal of this course is to learn techniques for which we will use sample statistics to estimate or make inference about parameters. Example 1: Compute the mean of the list of numbers: 1, 5, 7, 10, 12 Answer: x=1+5+7+10+125=355=7 Example 2: In the Spring 2012 Elementary Statistics, 161 students submitted a valid numeric value, denoted xi , for the number of texts in the month prior to the date this data was collected and i=1161xi=251,832. The average number of texts in the month prior for the Spring 2012 Elementary Statistics class at ACPHS was _____________________________ Example 3: The MHEALTH.xlsx contains data for 40 male patients. Denote the variable for BMI (body mass index) as xi . Suppose i=140xi=1,039.9. The mean BMI in the male patients in this data set is _____________________....
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...COMM 291 Midterm Review Package Prepared by Angelica Cabrera 1. INTRODUCTION TO DATA AND VARIABLES Categorical vs. Quantitative Data Categorical Limited number – distinct categories No Quantitative Large number Yes Possible values for variable Measurement units? EXAMPLE. Which variables are quantitative and which are categorical? Employee # Age (years) Annual Income (in Performance 1,000s of dollars) Rating (1-5 scale) 5543 48 50 – 100 4.5 2431 34 20 – 49 3.9 7281 31 0 – 19 3.4 Job Type Management Clerical Maintenance 2. SURVEYS AND SAMPLING Population: _______ individuals with a common characteristic that you want to generalize about Parameter: fact or characteristic about _____________ Sample: ________ of population Statistic: fact or characteristic about ______________ EXAMPLE. Mattel claims that less than 5% of all its Hot Wheels toys are defective. When testing 100 Hot Wheels toys from a production run of 7000 toys, 7% were found to be defective. What is the: a) Population? c) Parameter? Poor (Biased) Sampling Convenience sampling: Choosing respondents that are __________ to obtain Voluntary response: Respondents volunteer, so those with __________ opinions are more likely to respond b) Statistic? d) Sample? Sampling Designs 1. Simple Random Sampling (SRS): Every individual has an equal chance of being selected 2. Stratified Random Sampling: Divide population into ______________ subgroups and randomly select from each stratum 3. Cluster Random...
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...OVERVIEW OF THE STATISTICS COMPONENT OF FINANCE (QUANTITATIVE TECHNIQUES) 203 Note: The ‘statistics’ component of the unit covers Topics 1, 2, 4, 5, 6, 7, 8, 10 and 11 only and will not cover all the concepts in these Topics. The following is an overview of what is required in each topic. You are recommended to do the quiz questions as shown and the tutorial questions. Week 1 (Descriptive Statistics) Concepts • Population vs. sample • Parameter vs. statistic and use of correct symbols • Types of data (nominal, ordinal, interval) • Displaying data: Nominal: Bar and pie charts Interval: Stem and leaf displays, histograms, ogives, box and whisker plots • Measures of central location (mean, median, mode) • Measures of dispersion/spread (standard deviation, variance) Do 1.7.3 Quiz Can attempt all 1.7.4 Topic Questions Week 2 (Sampling, Probability, Normal Distribution) Sampling Concepts • Types of sampling plans (simple random sampling, stratified random sampling, cluster sampling Probability Concepts • From a table of joint probabilities answer such questions as: P(A and B) P(A or B) P(A)|B Determine if events A and B are mutually exclusive or not Determine if events A and B are independent or not The Normal Distribution Question gives a population mean and population standard deviation for a normally distributed variable...
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...1. BASIC STATS Types of variables: Count: no of bedrooms/children, manufacture year Ordinal: categories, income brackets Nominal: gender, yes/no, manufacture model Continuous: distance, time, age, best cruising speed Graphs Scatterplots: shows if there is a linear relationship (positive increasing left to right, cov>0), doesn't measure strength Histogram: modality skewness (positive long tail to right), modal class, symmetry Box Plot: Skewness (short/long whisker, short bottom=positive), symmetric Empirical CDFs | |Sampl|Populat| | |e |ion | |Average/Mean |[pic]|μ | |Variance | s2 |σ2 | |Standard |s |Σ | |Deviation | | | |Correlation |r |ρ | Location of Percentiles = ___th observation = what figure? Arithmetic Mean Mean vs Median If symmetric, mean ≈ median If positive skew, mean > median If negative skew, mean < median Variance Measures spread Larger SD – ↑ risk – ↑ rate of return Eg Standard Deviation Coefficient of variation Measures spread Covariance Measures the strength (and direction) of linear relationship between 2 variables. If cov > 0, then as X increases, Y increases (positive slope). If cov < 0 = opposite. If cov=0, not linearly related Coefficient of correlation If r=-1, perfect negative linear relationship If r=+1, perfect positive relationship If r=0, no LINEAR relationship From...
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...SIX SIGMA exam preparation Here you are a pool of 200 questionS . For the exam there will be 50 questions from this pool (different wording, numbers etc. could be used in exam) to create the written or the oral sessions of the exam (words and numbers could be different from the questions presented here.) The Written Exam will start at 9:00 AM and will finish at 12:00 AM You will be allowed to bring in the classroom only a pen and a pencil / rubber and nothing else (no phone, NO pc Nor any other electronic device) the oral exam will start at 12:30 Up to 3 points will be given at the oral exam where we will review the answer given at the written exam and if needed we will choose other questions taken from the pool in order to probe the knowledge gained. The oral exam usually last 15 minutes per candidate. up to 2 points will be assigned by the teacher based on the presence and participation during the entire course. Slides and PDF Books (for your personal reference and interest only) are available on iCorsi. Lean Six Sigma Paolo Rossetti – 2014 – Page 1 of 12 POOLs of QUESTIONS 01 - Six Sigma in a Nutshell 1. 2. 3. 4. 5. 6. 7. 8. 9. What is quality? How Gavin defines quality using different “dimensions”? State and comment the 8 dimensions of quality. Which is the Six Sigma definition of quality? What does CTQ stands for? What types of CTQ exists? Which are the sources of variability in a product? How can variability be expressed in statistical terms? How Edward Deming...
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...R for Beginners Emmanuel Paradis ´ Institut des Sciences de l’Evolution Universit´ Montpellier II e F-34095 Montpellier c´dex 05 e France E-mail: paradis@isem.univ-montp2.fr ´ I thank Julien Claude, Christophe Declercq, Elodie Gazave, Friedrich Leisch, Louis Luangkesron, Fran¸ois Pinard, and Mathieu Ros for their comments and c suggestions on earlier versions of this document. I am also grateful to all the members of the R Development Core Team for their considerable efforts in developing R and animating the discussion list ‘rhelp’. Thanks also to the R users whose questions or comments helped me to write “R for Beginners”. Special thanks to Jorge Ahumada for the Spanish translation. c 2002, 2005, Emmanuel Paradis (12th September 2005) Permission is granted to make and distribute copies, either in part or in full and in any language, of this document on any support provided the above copyright notice is included in all copies. Permission is granted to translate this document, either in part or in full, in any language provided the above copyright notice is included. Contents 1 Preamble 2 A few concepts before starting 2.1 How R works . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Creating, listing and deleting the objects in memory . . . . . . 2.3 The on-line help . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Data with R 3.1 Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Reading data in a file . . . . . . . . . . ....
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...Landscape Ecol (2009) 24:375–390 DOI 10.1007/s10980-008-9312-x RESEARCH ARTICLE Effects of macro and micro-environmental factors on the species richness of terrestrial tardigrade assemblages in an Iberian mountain environment ´ ´ Noemı Guil Æ Joaquın Hortal Æ ´ nchez-Moreno Æ Annie Machordom Sara Sa Received: 1 November 2007 / Accepted: 20 November 2008 / Published online: 4 December 2008 Ó Springer Science+Business Media B.V. 2008 Abstract Tardigrade communities are affected by micro and macro-environmental conditions but only micro-environmental variables, and altitudinal gradients have been studied. We review previous reports of altitudinal effects and evaluate the influence by interacting macro- (climate, soils, biome, and others) and micro-environmental (vegetation, moss and leaf litter) factors on tardigrade assemblages at the Sierra de Guadarrama mountain range (Iberian Central System Mountains, Spain). Terrestrial tardigrade assemblages were sampled using standard cores to N. Guil (&) Zoological Museum, Natural History Museum, University of Copenhagen, Universitetsparken 15, Copenhagen DK-2100, OE, Denmark e-mail: nguillopez@gmail.com J. Hortal NERC Centre for Population Biology, Division of Biology, Imperial College London, Silwood Park Campus, Ascot, Berkshire SL5 7PY, UK J. Hortal Á A. Machordom ´ Departmento de Biodiversidad y Biologıa Evolutiva, Museo Nacional de Ciencias Naturales (CSIC), Madrid, Spain ´ S. Sanchez-Moreno ´ Departamento de Proteccion...
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...Probability & Statistics for Engineers & Scientists This page intentionally left blank Probability & Statistics for Engineers & Scientists NINTH EDITION Ronald E. Walpole Roanoke College Raymond H. Myers Virginia Tech Sharon L. Myers Radford University Keying Ye University of Texas at San Antonio Prentice Hall Editor in Chief: Deirdre Lynch Acquisitions Editor: Christopher Cummings Executive Content Editor: Christine O’Brien Associate Editor: Christina Lepre Senior Managing Editor: Karen Wernholm Senior Production Project Manager: Tracy Patruno Design Manager: Andrea Nix Cover Designer: Heather Scott Digital Assets Manager: Marianne Groth Associate Media Producer: Vicki Dreyfus Marketing Manager: Alex Gay Marketing Assistant: Kathleen DeChavez Senior Author Support/Technology Specialist: Joe Vetere Rights and Permissions Advisor: Michael Joyce Senior Manufacturing Buyer: Carol Melville Production Coordination: Lifland et al. Bookmakers Composition: Keying Ye Cover photo: Marjory Dressler/Dressler Photo-Graphics Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and Pearson was aware of a trademark claim, the designations have been printed in initial caps or all caps. Library of Congress Cataloging-in-Publication Data Probability & statistics for engineers & scientists/Ronald E. Walpole . . . [et al.] — 9th ed. p. cm. ISBN 978-0-321-62911-1...
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...STA1610/101/3/2016 Tutorial Letter 101/3/2016 Introduction to Statistics STA1610 Semesters 1 & 2 Department of Statistics IMPORTANT INFORMATION: This tutorial letter contains important information about your module and includes the assignment questions for both semesters. BAR CODE Learn without limits. university of south africa CONTENTS Page 1 INTRODUCTION ..................................................................................................................4 1.1 Tutorial matter.......................................................................................................................4 2 PURPOSE OF AND OUTCOMES FOR THE MODULE ..........................................................5 2.1 Purpose ...............................................................................................................................5 2.2 Outcomes.............................................................................................................................5 3 LECTURER(S) AND CONTACT DETAILS .............................................................................6 3.1 Lecturer(s)............................................................................................................................6 3.2 Department ..........................................................................................................................6 3.3 University ........................................................................
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...Essay Ethics is a social science that refers to the study of human conduct and moral principles. The role of ethics and moral judgement is increasingly playing a more significant role in the corporate world. The concepts of personal values and moral judgement are inextricably connected. As a result, I believe that as professionals, whether we choose to act according to our morally intelligent conclusion, or ignore it, is highly related to individuals underlying personal values. When professionals are required into making moral decisions, they are forced to rely on their ethical reasoning. However Maggid’s web article argues that, Modern Actuary ethics goes further than the ‘minimal legal compliance’ (Maggid, 2010). Whilst an individual may know the ethical code, does that mean he or she will choose the morally right choice? Maggid seeks to answer this issue by extending our understanding of ethics into a combination of three key components. These three components are ethical reasoning, underlying personal values and reasonable self-interest. Subsequently, Maggid supports the notion that personal values are related to whether we choose to act morally. Underlying personal values refers to an individual’s core philosophy that drives ones belief and psychological reasoning. From a young age, it is our personal values that drive our rational thinking. It is a form of identity that allows an individual to distinguish from what is right and wrong. The issue is, that in the corporate...
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...Activity 1 Q1. Why is assessing the project scope and other relevant documents important in the initial stages of the project management? Answer- The scope of the project will make the job of managing scope and scope changes throughout the project easier. It will also reduce the chances of ‘scope creep’ and misunderstandings about the goals of the project. The definition of project scope also forms the basis for establishing a formal agreement with the client about what the project will deliver and when it will deliver agreed outcomes. Q2. Define stakeholders? What role do stakeholders play in the project management? Answer- A person, group or organization that has interest or concern in an organization. Stakeholders can affect or be affected by the organization's actions, objectives and policies. Some examples of key stakeholders are creditors, directors, employees, government (and its agencies), owners (shareholders), suppliers, unions, and the community from which the business draws its resources. Not all stakeholders are equal. A company's customers are entitled to fair trading practices but they are not entitled to the same consideration as the company's employees. Project stakeholders are those entities within or outside an organization which: 1. sponsor a project, or 2. have an interest or a gain upon a successful completion of a project; 3. May have a positive or negative influence in the project completion. Examples of project stakeholders include...
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...Statistics and Computing Series Editors: J. Chambers D. Hand W. H¨ rdle a Statistics and Computing Brusco/Stahl: Branch and Bound Applications in Combinatorial Data Analysis Chambers: Software for Data Analysis: Programming with R Dalgaard: Introductory Statistics with R, 2nd ed. Gentle: Elements of Computational Statistics Gentle: Numerical Linear Algebra for Applications in Statistics Gentle: Random Number Generation and Monte Carlo Methods, 2nd ed. H¨ rdle/Klinke/Turlach: XploRe: An Interactive Statistical a Computing Environment H¨ rmann/Leydold/Derflinger: Automatic Nonuniform Random o Variate Generation Krause/Olson: The Basics of S-PLUS, 4th ed. Lange: Numerical Analysis for Statisticians Lemmon/Schafer: Developing Statistical Software in Fortran 95 Loader: Local Regression and Likelihood Marasinghe/Kennedy: SAS for Data Analysis: Intermediate Statistical Methods ´ Ruanaidh/Fitzgerald: Numerical Bayesian Methods Applied to O Signal Processing Pannatier: VARIOWIN: Software for Spatial Data Analysis in 2D Pinheiro/Bates: Mixed-Effects Models in S and S-PLUS Unwin/Theus/Hofmann: Graphics of Large Datasets: Visualizing a Million Venables/Ripley: Modern Applied Statistics with S, 4th ed. Venables/Ripley: S Programming Wilkinson: The Grammar of Graphics, 2nd ed. Peter Dalgaard Introductory Statistics with R Second Edition 123 Peter Dalgaard Department of Biostatistics University of Copenhagen Denmark p.dalgaard@biostat.ku.dk ISSN: 1431-8784 ISBN: 978-0-387-79053-4 DOI:...
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