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    Data Analysis Using Spss

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    QUANTITATIVE DATA ANALYSIS USING SPSS AN INTRODUCTION FOR HEALTH AND SOCIAL SCIENCE Pete Greasley Quantitative Data Analysis Using SPSS Quantitative Data Analysis Using SPSS An Introduction for Health & Social Science Pete Greasley Open University Press McGraw-Hill Education McGraw-Hill House Shoppenhangers Road Maidenhead Berkshire England SL6 2QL email: enquiries@openup.co.uk world wide web: www.openup.co.uk and Two Penn Plaza‚ New York‚ NY 10121-2289‚ USA First published

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    Although demand forecasting is usually the responsibility of the sales and/or marketing functions‚ it is a very important input into the capacity planning and control decision‚ and so is of interest to operations managers. After all‚ without an estimate of future demand it is not possible to plan effectively for future events‚ only to react to them. It is therefore important to understand the basis and rationale for these demand forecasts. As far as capacity planning and control is concerned‚ there

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    UNIT 8 SALES : FORECASTING‚ BUDGET AND CONTROL Structure 8.0 8.1 8.2 8.3 8.4 Objectives Introduction Sales Forecasting Sales Quotas 8.3.1 How Quotas Are Set? 8.3.2 Attributes of A Good Sales Quota Plan Sales Budgeting 8.4.1 8.4.2 8.4.3 8.4.4 8.4.5 Purpose Methods Preparation Implementation and Feedback Mechanism Flexibility 8.5 8.6 Sales Control Methods of Sales Control 8.6.1 Sales Analysis 8.6.2 Marketing Cost Analysis 8.6.3 Sales Management Audit 8.7 8.8 8.9 Let Us Sum Up Key

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    Comparative Politics

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    Comparative Politics and Disasters: Assessing Substantive and Methodological Contributions David A. McEntire and Sarah Mathis Emergency Administration and Planning Department of Public Administration University of North Texas P.O. Box 310617 Denton‚ Tx 76203-0617 Abstract The following chapter illustrates how the discipline of comparative politics may help increase our understanding of disasters in other countries as well as promote more effective emergency management institutions

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    EXECUTIVE SUMMARY A capital market is a market for securities (debt or equity)‚ where business enterprises (companies) and governments can raise long-term funds. It is defined as a market in which money is provided for periods longer than a year. Capital markets may be classified as primary markets and secondary markets. In primary markets‚ new stock or bond issues are sold to investors via a mechanism known as underwriting. In the secondary markets‚ existing securities are sold and bought among

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    usage of data warehousing and data mining techniques to enhance the productivity of the business. The study of the processes is analysed so as to get the need of adaptation according to inherent demands of these industries in near future. The main topics we are discussing here are:   a) Data warehousing   b) Data Mining   c) ETL   d) Data Mart An attempt has been made to analyse different ways of using these for the enhancement in the different field. Data warehousing and current trends. Data Warehouse

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    General Motors and Toyota Comparative Organization Analysis Organizational Theory Week 3 May 2013 Table of Contents Introduction 3 Organizations and Industry Context 3 Firm Structures 6 Strengths‚ Weaknesses‚ Opportunities and Threats 9 Insights from the Structural Frame 14 Conclusion 15 Illustrations 18 Works Cited 21 Introduction General Motors (GM) and Toyota are both well-known companies in the automotive industry. They offer quality products to their

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    Data Warehouses and Data Marts: A Dynamic View file:///E|/FrontPage Webs/Content/EISWEB/DWDMDV.html Data Warehouses and Data Marts: A Dynamic View By Joseph M. Firestone‚ Ph.D. White Paper No. Three March 27‚ 1997 Patterns of Data Mart Development In the beginning‚ there were only the islands of information: the operational data stores and legacy systems that needed enterprise-wide integration; and the data warehouse: the solution to the problem of integration of diverse and often redundant

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    (Kwik Trend Analysis) Measure Value Future Period Forecast Error Measures 9. 1‚362‚143. Bias (Mean Error) -0.0156 10. 1‚455‚952. MAD (Mean Absolute Deviation) 50‚773.7969 11. 1‚549‚762. MSE (Mean Squared Error) 3‚498‚808‚832. 12. 1‚643‚572. Standard Error (denom=n-2=6) 68‚301.3828 13. 1‚737‚381. Regression line 14. 1‚831‚191. Demand (y) = 517857.2 15. 1‚925‚000. + 93‚809.5234 * Time (x) 16. 2‚018‚810. Statistics 17. 2‚112‚619. Correlation coefficient 0.9642 18. 2‚206‚429. Coefficient

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    FINAL REPORT ON COMPARATIVE MARKET ANALYSIS OF SAMSUNG ELECTRONICS VIS- A- VIS ITS COMPETITORS SAMSUNG INDIA ELECTRONICS PVT LTD. REPORT ON COMPARATIVE MARKET ANALYSIS OF SAMSUNG ELECTRONICS VIS- A- VIS ITS COMPETITORS A report submitted in partial fulfillment of the requirements of MBA Program of Jaipuria Institute of Management TABLE OF CONTENTS Acknowledgement…………………………………………………………………………4

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