DEPARTMENT OF MANAGEMENT STUDIES, NIT TRICHY AN ASSIGNMENT ON TIME SERIES ANALYSIS PLANNING AND CONTROL OF OPERATONS FAWAZ MOHAMED KUTTY 215112035 MBA Ist YEAR TIME SERIES ANALYSIS A time series may be defined as a set of values of a variable collected and recorded in a chronological order of the time intervals. Time series is used by statisticians to describe the flow of economic activity. In short time series refers to the data depending on time. It refers to a set of observations
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A point estimation is a sample statistic that gives a good guess about a population parameter. In the same way, a point estimate of the mean overpayment is simply a good guess about what the average overpayment for the population is. Investigating all 1,000 claims and obtaining the overpayment amount for each would either be impractical, unfeasible or both. Thus, the auditor deems a sample size of 50 claims to be adequate and sufficiently representative of the entire population. The mean overpayment
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Chapter 8 Statistical Inference: Estimation for Single Populations LEARNING OBJECTIVES The overall learning objective of Chapter 8 is to help you understand estimating parameters of single populations, thereby enabling you to: 1. Know the difference between point and interval estimation. 2. Estimate a population mean from a sample mean when ( is known. 3. Estimate a population mean from a sample mean when ( is unknown. 4. Estimate
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A Novel Channel Estimation Algorithm for 3GPP LTE Downlink System Using Joint Time-Frequency Two-Dimensional Iterative Wiener Filter Jinfeng Hou, Jian Liu School of Communication and Information Engineering University of Electronic Science and Technology of China (UESTC) Chengdu 611731, China Email: houjinfeng@gmail.com, liuj@uestc.edu.cn Abstract—The channel estimation algorithms are employed in 3GPP Long Term Evolution (LTE) downlink system to assist the coherent demodulation of Orthogonal Frequency
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Time Series and Forecasting Learning Team A Quantitative Reasoning for Business/501 August 23, 2011 Dr. Champion Time Series and Forecasting The purpose of this paper is for statisticians from the accountant department of Norton Company to compute the quarterly seasonal index for the years of 2003 through 2006 by using the ratio-to-moving-average method. In addition, the accountants will deseasonalize the data and determine the trend equation. Furthermore, the statisticians
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forecasting computer sales based on at least one year of sales data helps management. By using exponential smoother factor (alpha) we will react and adjust more slowly as the value reaches 0. As alpha gets higher we will have a more positive effect on demand and the forecast will be for an increase. The initial smoothing constant alpha and the trend factor beta are set by the manager with a value from 0 to 1. It is adjusted accordingly and a comparison of the results is use in the forecast. * Describe
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7.31 MLE’s and MOM 31. Maximum Likelihood Estimation and Method of Moments Definition of MLE’s Easy Examples Trickier Examples Invariance Property of MLE’s Method of Moments 1 7.31 MLE’s and MOM Definition of MLE’s Definition: Consider an i.i.d. random sample X1, . . . , Xn, where each Xi has p.d.f./p.m.f. f (x). Further, suppose that θ is some unknown parameter from Xi. The likelihood function is L(θ) ≡ n f (xi). i=1 Definition: The maximum likelihood estimator (MLE) of θ is the
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Forecasting LANCELOT PALMER HSM/260 July 5, 2015 Florence Wisn Forecasting Exercise 9.1 The following data represent total personnel expenses for the Palmdale Human Service Agency for past four fiscal years: 20X1 $5,250,000 20X2 $5,500,000 20X3 $6,000,000 20X4 $6,750,000 For moving averages and weighted moving averages, use only the data for the past three fiscal years. For weighted moving averages, assign a value of 1 to the data for 20X2, a value of 2 to the data for 20X3
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Econ 9720: Econometrics II GSU Department of Economics, Spring 2016 Practice Midterm Questions (No Solution will be Provided) 1. Suppose the data generating process (the true relationship) is y = Xβ + ε, where E[ε|X] = 0, E[εε |X] = σ 2 I n ; and X includes an intercept term. You do not observe the data set Z = [y X]. Instead you observe 150 15 50 Z Z = 15 25 0 50 0 100 2 Compute the least squares estimators β, s2 , R2 and RAdj (the adjusted R2 ). Is there anything to be gained
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chi-square, t and F distributions Large sample methods Convergence in probability Convergence in law Continuity Theorem for mgfs Major Theorems WLLN CLT Continuity Theorem Corollaries Delta Method Chapter 7 – Point Estimation Method of Moments Maximum Likelihood Estimation Transformation Property of MLE Comparing statistical procedures Risk function Inadmissibility and admissibility Mean squared error Properties of Estimators Unbiasedness Consistency Mean-squared error consistency
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