...General Motors Accounting Analysis and Business Solution | | Table of Contents Introduction2 Hypothesis and Methodology 3 Analysis of Problems4 Variables5 Primary and Secondary Sources6 Resources6 Sample………………………………………………………………………………………………………7 Test Statistics……………………………………………………………………………………………….8 Final Recommendations…………………………………………………………………………………….9 Conclusion………………………………………………………………………………………………...10 Appendices………………………………………………………………………………………………..11 Survey……………………………………………………………………………………………………..12 References…………………………………………………………………………………………………13 Introduction Every time you pick up a newspaper and read the business section, you hear about a major company having some sort of financial problems. These problems are most likely a result of accounting errors and in some instances because of these errors, companies have had major financial issues they could not recover from. Over the years General Motors has been one of those companies. General Motors Company, commonly known as GM, is an American Multinational Corporation, headquartered in Detroit, Michigan. GM designs, manufactures, markets and distributes vehicles and vehicle parts and as well as sells financial services. General Motors produces vehicles in 120 countries under ten different name brands (General Motors Corp.). Recently, management at General Motors has decided to start tracking the number of errors in the accounting department. The number of errors will be tracked per accountant and...
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...United States Army Adjutant General School Mission Command Essay The Battle of Dong Ap Bia: Hamburger Hill LTC Weldon Honeycutt CPT Shamika Hill Adjutant General Captain Career Course 003-16 June 30, 2016 For ten long days, American and South Vietnamese Army troops fought alongside each other to gain control of Dong Ap Bia, Hill 937. Their mission was to search and destroy the North Vietnamese Army (NVA) and the Viet Chong (VC), believed to have grouped in the A Shau Valley area. The battle now known as Hamburger Hill begun May 10, 1969 and is recognized as one of the last major encounter between the Americans and the NVA. Lieutenant Colonel (LTC) Weldon Honeycutt, commander of the 3rd Battalion, 187th Infantry Regiment (3-187th) was determined to accomplish his assigned mission and seize Hill 937. After ten long hard fought days and many casualties from both sides, the 3-187th successfully occupied the hill on May 20. But at what cost? LTC Honeycutt improper use of the mission command principles; understanding, assessing, and visualizing; led to a prolonged engagement which cost additional casualties. Hamburger Hill occurred during Operation Apache Snow, the second part of a three-phased campaigned aimed at annihilating the NVA bases in the treacherous A Shau Valley. LTC Honeycutt and the 3-187th received the mission to search and destroy the Peoples of Vietnam Forces (PAVN) in the A Shau Valley in order to block the NAV from infiltrating the northern providence...
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...While being informed by a Vietnamese translator that he was fighting The Pride of Ho Chi Minh, or the highly elite 29th Regiment of the North Vietnamese Army (NVA), Lieutenant Colonel Weldon Honeycutt responded by saying “Tell him I’m glad to know they’re around here. Tell him that’s why we came here- to kill North Vietnamese soldiers-and if they are around, they’ll just be doin’ us a favor78-79.” This bold statement, stated by the commander of a unit tasked to take a hill with heavily fortified enemy positions, shows that he understood his mission- plain and simple. LTC Honeycutt, commander of the 3d Battalion, 187th Infantry (the "Rakkasans"), lead his unit in accordance with the doctrinal tasks of mission command. LTC Honeycutt was responsible for the United States victory on Hamburger Hill through the operations process activities by correctly understanding the operational environment, properly directing his unit and combat power, accurately assessing the evolving situation, and properly leading his subordinates to a successful mission. A profane, outspoken, fiercely competitive man, Honeycutt was the prototype of the hard-nosed commander. Born in 1931 in the mill town Greensboro, North Carolina, Honeycutt had lied, at sixteen with only a sixth-grade education, about his age and enlisted in the army. At five feet eight, other officers thought he lacked having command presence. Because of this, he applied himself to the military life with a religious passion and quickly...
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...of statistical significance. Use the Inference Toolbox to conduct a large sample test for a population mean. Compare two-sided significance tests and confidence intervals when doing inference. Differentiate between statistical and practical “significance.” Explain, and distinguish between, two types of errors in hypothesis testing. Define and discuss the power of a test. AP Outline Fit: IV. Statistical Inference: Estimating population parameters and testing hypotheses (30%–40%) B. Tests of significance 1. Logic of significance testing, null and alternative hypotheses; P-values; one- and two-sided tests; concepts of Type I and Type II errors; concept of power 4. Test for a mean (large sample -- ( known) What you will learn: A. Significance Tests for µ (( known) 1. State the null and alternative hypotheses in a testing situation when the parameter in question is a population mean µ. 2. Explain in nontechnical language the meaning of the P-value when you are given the numerical value of P for a test. 3. Calculate the one-sample z-statistic and the P-value for both one-sided and two-sided tests about the mean µ of a Normal population. 4. Assess statistical significance at standard levels α by comparing P to α. 5. Recognize that significance testing does not measure the size or importance of an effect. 6. Recognize when you can use the z test and when the data collection design or a small sample from a skewed...
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...A STUDY AFFECTIVE FACTOR EXTRACTION USING METHODOLOGY FOR KANSEI ENGINEERING Tsung-Hsing Wang Department of Life Product Design, SHU-TE University of Science and Technology, Taiwan ROC Email: ch8136@stu. edu. tw Abstract In recent years, the study affective factor selection (AFs) for Kansei engineering (KE) has been an important issue in the industrial design field. Consumers’ affective responses (CARs) are usually presented in the form of a choice of adjectives. Based on the KE concept, this study conducted Factor Analysis (FA), Clustering Analysis (CA) and Procrustes Analysis (PA) to select the CARs from mobile phones product’s shape. First, in the initial stage of the study, 60samples of mobile phones were collected from the fashion market place. Twenty-two pairs of adjectives describing the mobile phones were used for a Semantic Differential (SD) experiment. K-means was implemented to find the clustering segmentations of the CARs according to the factor loading from FA, and to obtain representative pairs of adjectives within the clustering segmentations. In the meanwhile, PA was also used to decide adjective priorities according to the sorting rule. Finally, these two methods were analyzed and compared. Keywords: Kansei engineering, Affective factor, Methodology, Clustering analysis, Procrustes analysis. Introduction In the field of consumer market, the appearance of a product tends to be an important factor affecting consumers’ purchasing decision...
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...A Handbook of Statistical Analyses using SAS SECOND EDITION Geoff Der Statistician MRC Social and Public Health Sciences Unit University of Glasgow Glasgow, Scotland and Brian S. Everitt Professor of Statistics in Behavioural Science Institute of Psychiatry University of London London, U.K. CHAPMAN & HALL/CRC Boca Raton London New York Washington, D.C. Library of Congress Cataloging-in-Publication Data Catalog record is available from the Library of Congress This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage or retrieval system, without prior permission in writing from the publisher. The consent of CRC Press LLC does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from CRC Press LLC for such copying. Direct all inquiries to CRC Press LLC, 2000 N.W. Corporate Blvd., Boca Raton, Florida 33431. Trademark Notice:...
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...ANNUAL REPORT 2011-12 Government of India Ministry of Statistics and Programme Implementation Sardar Patel Bhawan New Delhi - 110001 Website: http//mospi.gov.in. CONTENTS Chapters Page Vision Mission Introduction Development and Highlights National Statistical Commission Central Statistical Office National Sample Survey Office Coordination of Statistical Activities Computer Centre Statistical Services Indian Statistical Institute Twenty Point Programme Infrastructure and Projects Monitoring Member of Parliament Local Area Development Scheme Hindi Promotion Other Activities ANNEXES I IA IB IC ID IE IF IG IH II IIIA IIIB IVA IVB IVC V VI VII VIII Organisation Charts Ministry of Statistics & Programme Implementation Administration National Statistical Commission Central Statistical Office National Sample Survey Office Computer Centre Programme Implementation Wing Abbreviations used Allocation of Business to the Ministry Project, Seminar/Conference/Workshop and Travel Grant Assistance sanctioned during 2010-11 Project, Seminar/Conference/Workshop and Travel Grant Assistance sanctioned during 2011-12 (Up to December, 2011) Statement of Budget Estimate (SBE) -2011-12 Total Plan Gross Budgetary Support (GBS) for 2010-11 (BE and RE) for North-Eastern Region. Total Plan Gross Budgetary Support (GBS) for 2011-12 (BE and RE) for North-Eastern Region. Performance of Monthly Monitored Items under TPP-2006 (April, 2010 to March, 2011) Performance of Monthly Monitored Items under TPP-2006...
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...Mathematics HL First examinations 2008 b DIPLOMA PROGRAMME MATHEMATICS HL First examinations 2008 International Baccalaureate Organization Buenos Aires Cardiff Geneva New York Singapore Diploma Programme Mathematics HL First published in September 2006 International Baccalaureate Organization Peterson House, Malthouse Avenue, Cardiff Gate Cardiff, Wales GB CF23 8GL United Kingdom Phone: + 44 29 2054 7777 Fax: + 44 29 2054 7778 Web site: www.ibo.org c International Baccalaureate Organization 2006 The International Baccalaureate Organization (IBO) was established in 1968 and is a non-profit, international educational foundation registered in Switzerland. The IBO is grateful for permission to reproduce and/or translate any copyright material used in this publication. Acknowledgments are included, where appropriate, and, if notified, the IBO will be pleased to rectify any errors or omissions at the earliest opportunity. IBO merchandise and publications in its official and working languages can be purchased through the IB store at http://store.ibo.org. General ordering queries should be directed to the sales and marketing department in Cardiff. Phone: +44 29 2054 7746 Fax: +44 29 2054 7779 E-mail: sales@ibo.org Printed in the United Kingdom by Antony Rowe Ltd, Chippenham, Wiltshire. 5007 CONTENTS INTRODUCTION 1 NATURE OF THE SUBJECT 3 AIMS 6 OBJECTIVES 7 SYLLABUS OUTLINE 8 SYLLABUS DETAILS 9 ASSESSMENT OUTLINE 53 ASSESSMENT DETAILS ...
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...Statistical Tool | Use/s | Level of Measurement | Formula | 1. Z – test | to determine whether two population means are different when the variances are known and the sample size is large. Source: http://www.investopedia.com/terms/z/z-test.asp#ixzz2LEqfeJnN | IV – NominalDV – Interval | | 2. T – test | to compare the means when the population mean is known but the population variance is unknown.Also when the population standard deviation is unknown but the sample standard deviation can be computed.Source:Basic Statistics Book | OrdinalInterval | | 3. F – test | used when comparing statistical models that have been fitted to a data set, in order to identify the model that best fits the population from which the data were sampled.Source:http://en.wikipedia.org/wiki/F-test | Ordinal Interval | | 4. Spearman rank | measures the strength of association between two ranked variablesSource:https://statistics.laerd.com/statistical-guides/spearmans-rank-order-correlation-statistical-guide.php | NominalOrdinal | | 5. Pearson R | used in the sciences as a measure of the strength of linear dependence between two variables.Source:http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient | Interval | | 6. Chi – square | to test the difference between an actual sample and another hypothetical or previously established distribution such as that which may be expected due to chance or probabilitycan also be used to test differences between...
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...the AIU data set in order to complete a regression analysis for benefits & intrinsic, benefits & extrinsic and benefit and overall job satisfaction. Plus giving an overview of these regressions along with what it would mean to a manager (AIU Online). Introduction Regression analysis can help us predict how the needs of a company are changing and where the greatest need will be. That allows companies to hire employees they need before they are needed so they are not caught in a lurch. Our regression analysis looks at comparing two factors only, an independent variable and dependent variable (Murembya, 2013). Benefits and Intrinsic Job Satisfaction Regression output from Excel SUMMARY OUTPUT Regression Statistics Multiple R 0.018314784 R Square 0.000335431 The portion of the relations explained Adjusted R Square -0.009865228 by the line 0.00033% of relation is Standard Error 1.197079687 Linear. Observations 100 ANOVA df SS MS F Significance F Regression 1 0.04712176 0.047122 0.032883 0.856477174 Residual 98 140.4339782 1.433 Total 99 140.4811 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 4.731133588 1.580971255 2.992549 0.003501 1.593747586 7.86852 Intrinsic -slope 0.055997338 0.308801708 0.181338 0.856477 -0.5568096 0.668804 Line equation is benefits =4.73 + 0.0559 (intrinsic) ...
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...statistical analysis is descriptive analysis. Descriptive statistics can summarize responses from large numbers of respondents in a few simple statistics. When a sample is obtained, the sample descriptive statistics are used to make inferences about characteristics of the entire population of interests. Descriptive analysis is the transformation of data in a way that describes the basic characteristics such as tendency, distribution, and variables. A examples of this would be if a company wanted to find out what type of bonus employees prefer. Descriptive statistics are used to explain the basic properties of these variables. One descriptive statistics that is used to explain the basic properties of variables is Mean, Median, and Modes. These terms all would be descriptive statistics for the above example by describing the central tendency in different ways. The mean would reflect the average answer that is given. The Median would provide the answer that is the central or middle range answer. The mode would be the answer that was given the most often. A second descriptive statistic that is used to explain the basic properties of variables is Tabulation. This refers to the orderly arrangement of data in a table or other summary format. When the tabulation process is done by hand, the term tallying is used. Simple tabulation tells how frequently each response or bit of information occurs. A third descriptive statistic used to explain the basic properties of variables is Cross...
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...Hypothesis Testing – Two Sample * H0 : µ1 = µ2 H1 : µ1 ≠ µ2 * Case 1 -- If you know population variances, use this and Normal table * Case 2 -- If you know only sample variances, and samples are large, use this and Normal table * Case 3 -- If you know only sample variances, and samples are small, and unknown population variances can be assumed identical, use this and t-table, with n1 + n2 - 2 df. (sp is called “pooled estimate of σ”) * We use standard error of difference to compute (actual) t Hypothesis Testing – Means Of Dependent (Paired) Samples tActual * pooled estimate of population proportion Regression and Correlation Simple Linear Regression (Only 1 independent variable, and linear relationship) Regression Coefficients Using Method Of Least Squares, we get: Standard Error Of Estimate Correlation: * Variation of y around the regression line * Variation of y around its own mean * Coefficient of Determination Direct Computation of r: Chi-Square * Make working table as follows: * List observed frequency cells, fo , in 1st column. * Compute expected frequency, fe , for each cell, and write in 2nd column. * fe = RT*CT/n where RT = row total, CT = column total, n = total no of observations in all cells of data table. * Compute (fo – fe ) for each row of working table in column 3 * Compute...
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...TERM END EXAMINATIONS,MARCH-2013 BACHELOR OF COMMERCE, YEAR – III ELEMENTARY STASTISTICS Time: 3 hours M.Marks:60 SECTION A Note: - Attempt any 4 questions. All questions carry equal marks. (4 X 5) The answer should be limited upto 200 words. 1) What is statistics? Explain the nature and limitations of statistics? 2) What is frequency distribution? What are the different types of frequency distribution? 3) What is frequency curve? Explain cumulative frequency curve with example? 4) Suppose mean of a series of 5 item is30.four values are respectively, 10, 15, 30 and 35.estimate the missing 5th value of the series. ANSWER : Mean = (10+15+30+35+x)/5=30 Therefore, x=(30*50)-( 10+15+30+35) i.e x = 150-90, hence x=60 5) Calculate median of the following distribution of data. Class interval | 0-5 | 5-10 | 10-20 | 20-30 | 30-50 | 50-70 | 70-100 | frequency | 12 | 15 | 25 | 40 | 42 | 14 | 8 | n= 12+15+25+40+42+14+8=156 Hence median is at the average of n/2 & (n/2 +1) positon i.e 78th & 79th position Class interval | 0-5 | 5-10 | 10-20 | 20-30 | 30-50 | 50-70 | 70-100 | frequency | 12 | 15 | 25 | 40 | 42 | 14 | 8 | Position 12 27 52 92 134 148 156 6) Calculate the coefficient of correlation...
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...Problem 1: i) All the coefficients are significant, because t (crit) = 1,96 is smaller than the absolute values of these three coefficients β1, β2 and β3. Estimated equation is: Log (wage) = 0.128 + 0.0904educ + 0.041exper – 0.000714exper2 (0.106) (0.0075) (0.0052) (0.000116) n = 526, R2 = 0.30 ii) Yes, the coefficient is significant because t-statistics absolute value 6,16 is greater than t (critical value) at 1 % significance level which is 2,586 in this case. iii) Return to the fifth year of experience: 100 * [0.041-2*(0.000714)*4] = 3,53% Return to the 20th year of experience: 100 * [0.041-2*(0.000714)*19] = 1,39% iv) x* = 0.0410089/(2*(-0.0007136)) = -28.7338 28.7338 There are 121 people in the sample with at least 29 years of experience. Problem 2: a) SSE + SSR = SST SST – SSE = SSR SSR = 7160,41429–10.6243285= 7149,79 b) n =524 c) R2 = SSE/SST = 10.62/7160.41 = 0,001484 d) t = (-0,4682478/0,5306473) = -0,88241 e) t = coefficient/ std. error coefficient / t = (5,944174/34,96) = 0,170028 f) F = t^2 = (-0,88241)^2 = 0,778645 Problem 3: Model 1: a) Coefficient on variable cigs indicates that one cigarette smoked per day reduces birth weight by 0,44 %. Therefore, the effect on birth weight from smoking 10 more cigarettes will be that it reduces birth weight by 4,4 %. b) In model 1, a white child is predicted to weight 5,5 % more than a non-white child on...
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...LLR 1st Quarter Report Project Name: Address: Project Manager: Area Manager: Staff Team: Volunteers: Contents 1. Introduction 2. Service Activity 3. Referrals 4. Outcomes 5. Engagement 6. Incidents 7. Feedback 8. Staff Development 9. Project Development 10. Conclusion 1. Introduction This report is based on the activities undertaken by ------- for the period between This initial introductory period has been a very successful initiation period in terms of the increasing number of referrals and assessments received and conducted, in addition to the rising number of service user (SU) engagements. During this reporting period, LLR inducted four new staff members who all completed LLR’s in-house training on the LLR, Health and Safety as well as File and Data Management training. Referrals over the few months have grown steadily with positive client engagement in groups, 1-2-1 counselling and 1-2-1 Recovery Plan Sessions. During this short period we have already observed an increasing number of SU’s being very committed to their recovery journey and we expect their commitment to be reflected in their continued growth and change. The staff and management team have also been very supportive and continue to provide us with regular group space ensuring group activities got underway. Although actual attendance numbers for the group have been relatively...
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