Business Statistics I: QM 1 Lecture N otes by Stefan W aner (5th printing: 2003) Department of Mathematics‚ Hofstra University BUSINESS STATISTCS I: QM 001 (5th printing: 2003) LECTURE NOTES BY STEFAN WANER TABLE OF CONTENTS 0. Introduction................................................................................................... 2 1. Describing Data Graphically ...................................................................... 3 2. Measures of Central Tendency
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part you thoroughly motivate your interest in the time series you are about to analyze. You should argue why it is of interest and importance to model your data series. You also briefly report what you do in your project and what results and conclusions you reach. 3. Data. In this section you describe where and how you got the data. Carefully describe all data characteristics‚ length of your time series‚ and frequency. Make a graph of your data series; you could also make a table with summary statistics
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dream about the winter day in America ‚ one day I can touch the snow‚ the thing is impossible in my country. But while I came to US‚ my thinking was complete change. I felt tired and kind a scare of winter. I am tried of my house heater. It was an old model so it not produce heat enpugh to warm my house. There are no school on snowing day‚ but I have a lot stuff to do at home‚. I had to help my father clean up my sidewalk‚ clean up my car . And Going out side in the winter time was became my nightmare
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Case 5-1 Income Smoothing a. Firstly‚ investors tend to invest in companies with stable earnings rather than one with volatile earnings. With stable earnings‚ there will be more likely an issuance of dividends and investors could easily predict the company’s future earnings compared to one with unstable earnings. With consistent earnings generated‚ it gives investors a secured feeling that it will again generate earnings as predicted. Confidence in the growth of rate of earnings is crucial because
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TIME SERIES AND FORECASTING McGrawHill/Irwin Copyright © 2010 by The McGrawHill Companies‚ Inc. All rights reserved. Time Series and its Components TIME SERIES is a collection of data recorded over a period of time (weekly‚ monthly‚ quarterly)‚ an analysis of history‚ that can be used by management to make current decisions and plans based on long-term forecasting. It usually assumes past pattern to continue into the future Components of a Time Series 1. 2. 3. 4. Secular Trend – the smooth
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Chapter 08.02 Euler’s Method for Ordinary Differential Equations After reading this chapter‚ you should be able to: develop Euler’s Method for solving ordinary differential equations‚ determine how the step size affects the accuracy of a solution‚ derive Euler’s formula from Taylor series‚ and use Euler’s method to find approximate values of integrals. 1. 2. 3. 4. What is Euler’s method? Euler’s method is a numerical technique to solve ordinary differential equations of the form
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Introduction The income smoothing literature has been the centre of attention in the accounting world for the past few decades. When companies experience economic turbulence due to a poor performance year‚ they turn to the accounting management department to resolve the bottom line. A strategy that managers can approach is changing the true information content of the company. As a result this has led managers to resort to smoothing their income. Many questions have been raised whether or not it
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Forecasting Models: Associative and Time Series Forecasting involves using past data to generate a number‚ set of numbers‚ or scenario that corresponds to a future occurrence. It is absolutely essential to short-range and long-range planning. Time Series and Associative models are both quantitative forecast techniques are more objective than qualitative techniques such as the Delphi Technique and market research. Time Series Models Based on the assumption that history will repeat itself‚
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Analysis of Financial Time Series Third Edition RUEY S. TSAY The University of Chicago Booth School of Business Chicago‚ IL A JOHN WILEY & SONS‚ INC.‚ PUBLICATION Analysis of Financial Time Series WILEY SERIES IN PROBABILITY AND STATISTICS Established by WALTER A. SHEWHART and SAMUEL S. WILKS Editors: David J. Balding‚ Noel A. C. Cressie‚ Garrett M. Fitzmaurice‚ Iain M. Johnstone‚ Geert Molenberghs‚ David W. Scott‚ Adrian F. M. Smith‚ Ruey S. Tsay‚ Sanford Weisberg Editors Emeriti:
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Time Series Models for Forecasting New One-Family Houses Sold in the United States Introduction The economic recession felt in the United States since the collapse of the housing market in 2007 can be seen by various trends in the housing market. This collapse claimed some of the largest financial institutions in the U.S. such as Bear Sterns and Lehman Brothers‚ as they held over-leveraged positions in the mortgage backed securities market. Credit became widely available to unqualified borrowers
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