Expectations‚ Variances & Covariances The Rules of Summation n å xi ¼ x1 þ x2 þ Á Á Á þ xn covðX; YÞ ¼ E½ðXÀE½XÞðYÀE½YÞ i¼1 n ¼ å å ½x À EðXÞ½ y À EðYÞ f ðx; yÞ å a ¼ na x y i¼1 n covðX;YÞ r ¼ pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi varðXÞvarðYÞ n å axi ¼ a å xi i¼1 n i¼1 n n i¼1 i¼1 E(c1X þ c2Y ) ¼ c1E(X ) þ c2E(Y ) E(X þ Y ) ¼ E(X ) þ E(Y ) å ðxi þ yi Þ ¼ å xi þ å yi i¼1 n n n i¼1 i¼1 å ðaxi þ byi Þ ¼ a å xi þ b å yi i¼1 n var(aX þ bY þ cZ ) ¼ a2var(X) þ b2var(Y ) þ c2var(Z
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THE EFFECTS OF FDI INFLOWS ON HOST COUNTRY ECONOMIC GROWTH Abstract: Numerous researchers have investigated the impact of FDI on economic growth. Unfortunately‚ the empirical literature has produced conflicting conclusions. Specifically‚ this paper assesses whether the FDI impact on economic activities in the host countries. This study by using a panel data for 94 countries over the 1991–2010 period‚ shows that there is not a strong complementary connection between FDI and economic
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Regression with Time Series Data Week 10 Main features of Time series Data Observations have temporal ordering Variables may have serial correlation‚ trends and seasonality Time series data are not a random sample because the observations in time series are collected from the same objects at different points in time For time series data‚ because MLR2 does not hold‚ the inference tools are valid under a set of strong assumptions (TS1-6) for finite samples While TS3-6 are often too restrictive
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ECON 1203 Progress Report: 1. * Descriptive statistics (Emphasis of course so far) * What are the key features of data? * How can we best describe these features so that analysis is informative * Inferential statistics (Emphasis of course to come) * Extracting information about population parameters on basis of sample statistics * What does a sample mean tell us about a population mean? * Typically only alternative because difficult or impossible
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1394 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING‚ VOL. 25‚ NO. 6‚ JUNE 2013 Spatial Approximate String Search Feifei Li‚ Member‚ IEEE‚ Bin Yao‚ Mingwang Tang‚ and Marios Hadjieleftheriou Abstract—This work deals with the approximate string search in large spatial databases. Specifically‚ we investigate range queries augmented with a string similarity search predicate in both euclidean space and road networks. We dub this query the spatial approximate string (SAS) query
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DKH Enterprises Inc. Lantana‚ Florida Office Manager 08/2005-04/2014 • Create monthly reports being given to the Accountant • Maintain company records up to date. • Make federal income tax payments. • Invoice customers and collect payments • Pay sales tax to the Department of Revenue • Assist customers with inquiries and sales • Maintain customer accurate information • Problem solves with vendors and
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Classical Linear Regression Models and Relaxing their Assumptions Seid Nuru seidnali@yahoo.com August 2012 > The Classical Linear Regression Models Introduction The Simple Regression Model The Multiple Linear Regression Models Violations of the Assumptions of CLRMs Definition • Econometrics is the application of statistical‚ and mathematical techniques to the analysis of economic data with a purpose of verifying or refuting economic theories. Theory Mathematical Model Econometric
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allowances‚ assumptions‚ exclusions‚ cost risks and opportunities‚ and any deviations from standard practices. In addition the BOE is a documented record of pertinent communications that have occurred and agreements that have been made between the estimator and other project stakeholders. A well prepared basis of estimate will: • • • • • • • • • Document the overall project scope. Communicate the estimator’s knowledge of the project by demonstrating an understanding of scope
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functions for ICA. These contrast functions enable both the estimation of the whole decomposition by minimizing mutual information‚ and estimation of individual independent components as projection pursuit directions. The statistical properties of the estimators based on such contrast functions are analyzed under the assumption of the linear mixture model‚ and it is shown how to choose contrast functions that are robust and/or of minimum variance. Finally‚ we introduce simple fixed-point algorithms for practical
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Fault-Tolerant Observers Supervisor: Dr Imad M. Jaimoukha Designing of a robust fault-tolerant observer that guarantees a certain level of performance for real-time applications. By: YEO Zhi-Wei‚ Laurence 00566245 Overview For any system such as a factory plant‚ an aircraft jet engine system or a water pumping system‚ it can be replicatedi by a mathematical model which includes the affecting dynamics based on observations and assumptions. However‚ it would be practically challenging
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