ABSTRACT Wheat is the staple food of people in Pakistan. Depending upon rapidly growing population‚ the wheat requirements vary from time to time that creates complications for policy makers. The main objective of the study was to forecast as accurately as possible‚ the population and wheat requirements in Punjab province for the year 2010-11. For this purpose a time series data regarding population‚ wheat production and wheat requirements were collected from the National statistics. An exponential smoothing
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Multiple Choice Questions 1. Forecasts a. become more accurate with longer time horizons b. are rarely perfect c. are more accurate for individual items than for groups of items d. all of the above e. none of the above One purpose of short-range forecasts is to determine a. production planning b. inventory budgets c. research and development plans d. facility location e. job assignments Forecasts are usually classified by time horizon into three categories a. short-range‚ medium-range‚ and long-range
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applications of forecasting Defining forecasting General steps in the forecasting process Qualitative techniques in forecasting Time series methods The Naive Methods Simple Moving Average Method Weighted Moving Average Exponential Smoothing Evaluating the forecast accuracy Trend Projections Linear Regression Analysis Least Squares Method for Linear Regression Decomposition of the time series Selecting A Suitable Forecasting Method More on Forecast Errors Review Exercise CHAPTER 6 FORECASTING TECHNIQUES
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9/5/14 Chapter 5 Forecasting To accompany Quantitative Analysis for Management‚ Tenth Edition‚ by Render‚ Stair‚ and Hanna Power Point slides created by Jeff Heyl © 2008 Prentice-Hall‚ Inc. © 2009 Prentice-Hall‚ Inc. Introduction n Managers are always trying to reduce uncertainty and make better estimates of what will happen in the future n This is the main purpose of forecasting n Some firms use subjective methods n Seat-of-the pants methods‚ intuition‚ experience n There are also
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Box-Jenkins Modeling and Forecasting of Monthly Electric Consumption of PANELCO III Customers ______________________________ A Special Problem Presented To The Panel of Evaluators Mathematics Department Pangasinan State University Urdaneta City _______________________________ In Partial Fulfillment of The Requirement for the Degree of Bachelor of Science in Mathematics Major in Statistics ______________________________ By: Jake Anthony E. CantubaMarch 2014 APPROVAL SHEET In partial
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TIME SERIES MODELS FOR FORECASTING NEW ONE-FAMILY HOUSES SOLD IN THE U.S. INTRODUCTION The housing market has been weak since its recent peak in 2005. Then‚ the sharp drop in the housing prices in 2007 contributed to the subprime loan crisis [1]. This dramatic change in the housing market not only affects the construction industry‚ but also may have a significant impact on the whole economy [3]. We are still in the midst of the housing problem with the increase in the delinquency rate and
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in Pakistan Syed Ali Raza ⁎‚ Syed Tehseen Jawaid 1 IQRA University‚ Karachi-75300‚ Pakistan a r t i c l e i n f o a b s t r a c t This study investigates the impact of terrorism activities on tourism in Pakistan by using the annual time series data from the period of 1980 to 2010. Johansen and Jeuuselius and ARDL bound testing cointegration approach confirms the valid long run relationship between terrorism and tourism. Results indicate the significant negative impact of terrorism on tourism
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1. INTRODUCTION 1.1 Company Profile Toyota Motor‚ the world’s largest automotive manufacturer (overtaking GM in 2008)‚ designs and manufactures a diverse product line-up that includes subcompacts to luxury and sports vehicles‚ as well as SUVs‚ trucks‚ minivans‚ and buses. Its vehicles are produced either with combustion or hybrid engines‚ as with the Prius. Toyota’s subsidiaries also manufacture vehicles: Daihatsu Motor produces mini-vehicles‚ while Hino Motors produces trucks and buses. Additionally
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and the main ingredients necessary for implementation of this forecasting procedure. Linear trend forecasting is used to impose a line of best fit to time series historical data (Harvey‚ 1989; McGuigan et al.‚ 2011). It is a simplistic forecasting technique that can be used to predict demand (McGuigan et al.‚ 2011)‚ and is an example of a time series forecasting model. * CYCLICAL COMPONENT In weekly or monthly data‚ the cyclical component describes any regular fluctuations. It is a non-seasonal
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TREND PROJECTIONS: Seasonal Variations with Trends * is essentially concerned with the study of movement of variable through time. * requires a long and reliable time series data. * is used under the assumption that the factors responsible for the past trends in variables to be projected will continue to play their part in future in the same manner and to the same extend as they did in the past in determining the magnitude and direction of the variable. Limitations: * The first
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