...sample. A statistic is usually used to estimate a parameter. 1.22 A Gallup poll indicated that 74% of Americans who had yet to retire look to retirement accounts as major funding sources when they retire. Interestingly, 40% also said that they looked to stocks or stock market mutual fund investments as major funding sources when they retire. (data extracted from D.Jacobs, “Investors Look Beyond Social Security to Fund Retirement,” www.gallup.com, March 28, 2011). The results are based on telephone interviews conducted March 24, 2011, with 1,000 or more adults living in the United States, aged 18 and older. a. Describe the population of interest. They are the Americans that have not retire yet. b. Describe the sample that was collected. They are the 18 and older group of adults. c. Is 74% a parameter or a statistic? Explain. It is a parameter. It measuring the characteristic of the population. d. Is 40% a parameter or a statistic? It is a statistic. It is measuring the characteristic of a sample. 1.23 The Data and Story Library (DASL) is an online library of data files and stories that illustrate the use of basic statistical methods. Visit lib.stat.cmu.edu/ click “DASL” under the Main Menu on the left side...
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...A STUDY ON TRAFFIC RULES VIOLATIONS IN CHENNAI (VANDALUR AND PERANGALUTUR) Submitted By: Praveen Kumar A, MBA. INTRODUCTION Violations in traffic laws are very common in a highly populated country like India. The conditions are even worse in metro cities like Delhi, Mumbai Bangalore and Chennai. The accidents associated with these violations cause a huge loss to life and property. Same is the case in Chennai. Being a metro city and a highly populated one also, has a lot of road accidents every year. Despite this the violations in traffic laws do not reduce. A lot of people disobey the rules every day sometimes willingly and sometimes because they are forced to do so because of others. The major traffic laws in India are wearing a helmet in case of two wheelers, putting a seat belt in case of cars, driving on the right side of the road including overtaking from the right direction, over speeding in certain restricted areas, not obeying the traffics signals and stooping the car after the finish line. It is mainly because of these violations that major accidents occur. It should be recognized that the highway is a social situation, in which people are interacting. However the drivers are unknown to each other in most of the cases and the interaction between them very brief and non-recurring. The communication between them is very limited and that also through mechanical aids like lights and horns. The main objective of these laws is to minimize the confusion...
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...least two different tools, analyze the data and present your conclusions. Using the Pareto Chart, the number of times all items were graded with the lowest rating “F” was plotted. Based on this chart and on the 80-20 principle, the top 3 items namely Speed of service, Print Program, and Seating were causing the rest of the complaints on the rest of the items. Cumulative Frequency Speed of Service Printed Program Seating Entertainment Traffic Pricing Parking Prices Season Ticket Plans Selection of Foods Subtotal 0 182 182 100% 0 182 100% 76 30 25 19 15 10 5 2 Frequency 76 106 131 150 165 175 180 182 Percentage 42% 58% 72% 82% 91% 96% 99% 100% Q2. How could the survey have been more useful? Since the survey was supposed to address complains, it could have been more useful if the tools was designed to determine “failure” instead of grading items on a scale. Out of the 10 items, the management could focus on identified aspect with a failed rating and the surge of overwhelming data would be avoided. Q3. What is the next step? Following the Pareto principle, management should address the complaints on Speed of service, Printed Program, and Seating as they are causing 80% of the complaints. Reaction: A qualitative data collected backed up with quantitative tools is an effective way of addressing issues that affect quality. In order to avoid being overwhelmed, it is important to establish the goals first before setting out to gather data. There are three things that could...
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...Analysis of Factors Affecting Restaurant Businesses Course Name: Principles of Marketing Course ID: MKT 201 Section: 0001 Date of Submission: August 13, 2012 ------------------------------------------------- Prepared By: 1. NusratJahan ID: 10204101 Serial: 22 2. RahnumaAkram Sabah ID:10204043 Serial:14 3. AbantiSaha ID:10204080 Serial:18 4. RatulRaihan ID: 5. Nazir Ahmed Zihad ID:10204036 Serial:12 ------------------------------------------------- Summary Over the years, numbers of people having foods at restaurants are increasing. As a result, number of restaurants has also increased with the demand. Busy life of city inhabitants compels to have meals at restaurants rather cooking at home. With the passage of time, peoples also became aware about the food quality, environment and interior design of restaurants. Previous research conducted on consumers has shown that location has been a factor influencing a consumer’s decision to go to a restaurant. It has also shown that locational variables such as environment and packet quality have been considered important to consumers. Consumer demographics if affects the consumer perception then it may affect the consumer’s decision to go to a restaurant. The correlation between different locational variables we found out by calculating Pearson’s correlation coefficient and effect of the demographic features on consumer preference was found out through cross tabulation. The results...
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...Report Business Research Methods The third homework (about the descriptive statistics) Question 1: Explain the difference of Mobile Contents Use by gender Crosstabs Case Processing Summary | | Cases | | Valid | Missing | Total | | N | Percent | N | Percent | N | Percent | Sex * Music | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Movie | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * DMB | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Phone Decorating | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Sport | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Game | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * MMS | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Adult | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Animation | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Mobile banking | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Map | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Stock Trading | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Chatting | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * News/Weather | 300 | 100.0% | 0 | .0% | 300 | 100.0% | Sex * Music Crosstab | | Music | Total | | 0 | 1 | | Sex | 1 | Count | 91 | 97 | 188 | | | % within Sex | 48.4% | 51.6% | 100.0% | | | % within Music | 65.0% | 60.6% | 62.7% | | 2 | Count | 49 | 63 | 112 | | | % within Sex | 43.8% | 56.3% | 100.0% | | | % within Music | 35.0% | 39.4% | 37.3% | Total | Count | 140 | 160 | 300 | | % within Sex | 46...
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...Table of Content Acknowledgement: 3 Executive summary: 4 Methodology 7 Sampling Design 8 Limitation of the report 9 Analysis of finding 10 Terminology 10 Findings 11 SQ 7 Caste Ethnicity * Q5 Support Fairly 3 SQ 8 Occupation * Q5 Support Fairly 6 SQ 8 Occupation * Q8 Post Earth quake 8 SQ 5 Age * Q8 Post Earth quake 10 SQ 5 Age * Q5 Support Fairly 11 SQ 6 Gender * Q1 Problem Addressed 13 Conclusion: 16 Annex 17 SQ 8 Occupation * Q1 Problem Addressed 17 SQ5Age * Q2Satisfied 18 SQ6Gender * Q2Satisfied 19 SQ7CasteEthnicity * Q2Satisfied 20 SQ8Occupation * Q2Satisfied 21 SQ5Age * Q3InfoReliefAndSupport 23 SQ6Gender * Q3InfoReliefAndSupport 24 SQ7CasteEthnicity * Q3InfoReliefAndSupport 25 SQ8Occupation * Q3InfoReliefAndSupport 26 SQ5Age * Q4NGoWork 28 SQ6Gender * Q4NGoWork 29 SQ7CasteEthnicity * Q4NGoWork 30 SQ8Occupation * Q4NGoWork 32 SQ6Gender * Q5SupportFairly 33 SQ 5 Age * Q6 Prepared For Winter 34 SQ 6 Gender * Q6 Prepared For Winter 35 SQ 7 Caste Ethnicity * Q6 Prepared For Winter 37 SQ 8 Occupation * Q6 Prepared For Winter 38 SQ 5 Age * Q7 Problem rel Children 39 SQ 6 Gender * Q7 Problem rel Children 41 SQ 7 Caste Ethnicity * Q7 Problem rel Children 42 SQ 8 Occupation * Q7 Problem rel Children 43 SQ 7 Caste Ethnicity * Q8 Post Earth quake 44 SQ 5 Age * Q9 Increase Voilence 45 SQ 6 Gender * Q9 Increase Voilence 46 SQ 7 Caste Ethnicity * Q9 Increase Violence 47 SQ 8 Occupation * Q9 Increase...
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...QTM 3650 – Prof. Rick Cleary Class #9–Mon. 2/24/14 I.) Logistics - Return and discuss homework problems 12-14; notes below. Collect problems 15-16. - Final exam time for our class: Wednesday 5/7, 3:30 – 5:30 PM, Gerber 103. - Review schedule for upcoming two weeks: 10Weds. 2/26 | Classification and Regression TreesProject 1 dueTake home part of Exam #1 assigned | Text Chapter 9 | 11Mon. 3/3 | Finish Chapter 9Catch up/review/special topics/Visualization tools | - | 12Weds. 3/5 | EXAM #1In class portion and take home due | - | - A note on ground rules for the in-class exam: Each student may bring one 8.5” by 11” review sheet to the exam with them; OK to write on both sides. The sheet will be turned in with the exam. No electronic devices of any kind except for a calculator that can do simple arithmetic. It’s OK to use a hand-held device (not a laptop) as a calculator as long as it is used only for that purpose. If students don’t have a calculator, Prof. Cleary will have a couple on hand, or students may use the podium computer to do calculations only in Excel. Any connection to the internet or communication to others (texting, etc.) during the exam will be considered an academic integrity violation. - Today’s topics: Finish Chapter 8 on the naïve Bayes estimator and review the Chi-Squared test of independence. II.) Comments on homework problems 12-14. Problem 12.) While we reviewed this last...
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...Kengeri Campus, Bangalore. Review On Cluster Analysis Submitted for the partial fulfillment of process related to Continuous Internal Assessment 1 In Marketing Analytics [MBA - III Trimester (MBA 322)] Submitted By P.Akhilesh 1420419 Cluster analysis? The Cluster Analysis is an explorative analysis that tries to identify structures within the data. Cluster analysis is also called segmentation analysis .It is used to identify groups of cases if the alignment is not previously known. Because it is explorative it does make any distinction between dependent and independent variables. The different cluster analysis methods that SPSS offers can handle binary, nominal, ordinal, and scale (interval or ratio) data. Different types of cluster analysis include: * Hierarchical Analysis * Two step Cluster * K-means cluster From the given data set: * We have both continuous and categorical variables * Continuous variables include: Attitude towards Nike, Awareness towards Nike, Preference for Nike Loyalty for Nike and purchase intention for Nike * Categorical variables: Sex, Hierarchical Cluster Analysis: Ward method: * Here it is clearly mentioned that from cluster one 15 respondents are available , from cluster two 15 respondents are available and from cluster three 10 respondents are available * We have 5 missing respondents who had not respondent or the data is inaccurate Interpretation for Agglomeration...
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...INTRODUCTION TO BUSINESS ANALYTICS Sumeet Gupta Associate Professor Indian Institute of Management Raipur Outline • Business Analytics and its Applications • Analytics using Data Mining Techniques • Working with R BUSINESS ANALYTICS AND ITS APPLICATIONS What is Business Analytics? Analytics is the use of: data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models to help managers gain improved insight about their business operations and make better, fact-based decisions. Evolution of Business Analytics? • Operations research • Management science • Business intelligence • Decision support systems • Personal computer software Application Areas of Business Analytics • Management of customer relationships • Financial and marketing activities • Supply chain management • Human resource planning • Pricing decisions • Sport team game strategies Why Business Analytics? • There is a strong relationship of BA with: • profitability of businesses • revenue of businesses • shareholder return • BA enhances understanding of data • BA is vital for businesses to remain competitive • BA enables creation of informative reports Global Warming Poll Winner Sales Revenue Predicting Customer Churn Credit Card Fraud Loan Default Prediction Managing Employee Retention Market Segmentation Medical Imaging Analyzing Tweets stylus ...
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...Project FOX Fad or Expedient? - Perceptions of Consumers and Organisations on Green Marketing. Mieke van Kaam a research proposal – 22 April 2012 Table of Contents 1. Background 3 2. Problem statement 3 3. Research objectives 4 4. The scope and limitations of the proposed research 4 5. Literature review 6 5.1. Green fever –A load of Greenwash or not. 6 5.2. How green can you go? 7 5.3. Lets collaborate! 7 5.4. Consumer evolution 8 6. Research plan 9 6.1. Description of research subjects and design 9 6.2. Sampling plan 9 6.3. Instruments 9 6.4. Procedures 9 7. Proposed methods for processing, analysing and interpreting data 11 7.1. Quantitative 11 7.2. Qualitative 11 8. Timeline 12 9. Potential outcomes and conclusion 13 10. Reference list 14 11. Appendix A 15 Background * Green marketing is the product modifications and/or changes in production processes, * packaging and advertising, made by companies to ensure that the final consumer product * is environmentally safe. * This is a simple definition for green marketing, but how many consumers and organisations * in South Africa (SA) actually understand the essence of green marketing. And if they do, * what are their viewpoints on green marketing and how was it shaped? Do organisations see * it as a fad attribute that's merely added to a product to ensure premium pricing options * and eventually higher profits for the company...
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...SPSS Inc. software products, please visit our Web site at http://www.spss.com or contact SPSS Inc. 233 South Wacker Drive, 11th Floor Chicago, IL 60606-6412 Tel: (312) 651-3000 Fax: (312) 651-3668 SPSS is a registered trademark and the other product names are the trademarks of SPSS Inc. for its proprietary computer software. No material describing such software may be produced or distributed without the written permission of the owners of the trademark and license rights in the software and the copyrights in the published materials. The SOFTWARE and documentation are provided with RESTRICTED RIGHTS. Use, duplication, or disclosure by the Government is subject to restrictions as set forth in subdivision (c) (1) (ii) of The Rights in Technical Data and Computer Software clause at 52.227-7013. Contractor/manufacturer is SPSS Inc., 233 South Wacker Drive, 11th Floor, Chicago, IL 60606-6412. Patent No. 7,023,453 General notice: Other product names mentioned herein are used for identification purposes only and may be trademarks of their respective companies. Windows is a registered trademark of Microsoft Corporation. Apple, Mac, and the Mac logo are trademarks of Apple Computer, Inc., registered in the U.S. and other countries. This product uses WinWrap Basic, Copyright 1993-2007, Polar Engineering and Consulting, http://www.winwrap.com. Printed in the United States of America. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by...
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...the Analysis of Survey Data March 2001 The University of Reading Statistical Services Centre Biometrics Advisory and Support Service to DFID © 2001 Statistical Services Centre, The University of Reading, UK Contents 1. Preparing for the Analysis 5 1.1 Introduction 5 1.2 Data Types 6 1.3 Data Structure 7 1.4 Stages of Analysis 9 1.5 Population Description as the Major Objective 11 1.6 Comparison as the Major Objective 12 1.7 When Weighting Matters 13 1.8 Coding 14 1.9 Ranking & Scoring 15 2. Doing the Analysis 17 2.1 Approaches 17 2.2 One-Way Tables 18 2.3 Cross-Tabulation: Two-Way & Higher-Way Tables 18 2.4 Tabulation & the Assessment of Accuracy 19 2.5 Multiple Response Data 20 2.6 Profiles 21 2.7 Looking for Respondent Groups 22 2.8 Indicators 23 2.9 Validity 25 2.10 Summary 26 2.11 Next Steps 26 © SSC 2001 – Approaches to the Analysis of Survey Data 3 4 © SSC 2001 – Approaches to the Analysis of Survey Data 1. Preparing for the Analysis 1.1 Introduction This guide is concerned with some fundamental ideas of analysis of data from surveys. The discussion is at a statistically simple level; other more sophisticated statistical approaches are outlined in our guide Modern Methods of Analysis. Our aim here is to clarify the ideas that successful data analysts...
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...Business AnAlytics And intelligence Course Starting: 29 June 2014 Application Deadline: Early Decision 28 February 2014 Regular Decision 15 April 2014 (Batch 5) (Classes conducted on-campus as well as off-campus) Certificate Programme on Business Analytics and Intelligence BATCH 5 in god We trust, All Others Must Bring data - W edwards deming he theory of bounded rationality proposed by nobel laureate Herbert Simon is evermore significant today with increasing complexity of the business problems; limited ability of human mind to analyze alternative solutions and the limited time available for decision making. introduction of enterprise resource planning (eRP) systems has ensured availability of data in many organizations; however, traditional eRP systems lacked data analysis capabilities that can assist the management in decision making. Business Analytics is a set of techniques and processes that can be used to analyse data to improve business performance through fact-based decision making. Business Analytics is the subset of Business intelligence, which creates capabilities for companies to compete in the market effectively. Business Analytics is likely to become one of the main functional areas in most companies. Analytics companies develop the ability to support their decisions through analytic reasoning using a variety of statistical and mathematical techniques. thomas devonport in his book titled, “competing on analytics: the new science of winning”, claims...
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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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...Data Mining Algorithms for Classification BSc Thesis Artificial Intelligence Author: Patrick Ozer Radboud University Nijmegen January 2008 Supervisor: Dr. I.G. Sprinkhuizen-Kuyper Radboud University Nijmegen Abstract Data Mining is a technique used in various domains to give meaning to the available data. In classification tree modeling the data is classified to make predictions about new data. Using old data to predict new data has the danger of being too fitted on the old data. But that problem can be solved by pruning methods which degeneralizes the modeled tree. This paper describes the use of classification trees and shows two methods of pruning them. An experiment has been set up using different kinds of classification tree algorithms with different pruning methods to test the performance of the algorithms and pruning methods. This paper also analyzes data set properties to find relations between them and the classification algorithms and pruning methods. 2 1 Introduction The last few years Data Mining has become more and more popular. Together with the information age, the digital revolution made it necessary to use some heuristics to be able to analyze the large amount of data that has become available. Data Mining has especially become popular in the fields of forensic science, fraud analysis and healthcare, for it reduces costs in time and money. One of the definitions of Data Mining is; “Data Mining is a process that consists of applying data analysis and discovery...
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