EXPERIMENT NO 2 FACT TABLE FOR UNIVERSITY DATABASE AIM:-Creation of dimension table and fact table for University Database. THEORY:- DIMENSION TABLE In data warehousing‚ a dimension table is one of the set of companion tables to a fact table. The fact table contains business facts or measures and foreign keys which refer to candidate keys (normally primary keys) in the dimension tables. Contrary to fact tables‚ the dimension tables contain descriptive attributes (or fields) which are typically
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1. Make a table for the software project‚ technical infrastructure project‚ and the combined projects. To better interpret the results of the EVA‚ you may choose to graph the cumulative spending over time for the software project‚ technical infrastructure project‚ and the combined projects. 3. Answer The questions below: 3.1. Which of the two components are underperforming according to the plan? How do you know this? 3.2. Are the components of the project within budget? How do you know?
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University of Hong Kong Introductory Econometrics (ECON0701)‚ Fall 2013 22 November 2013 Further Issues in Using OLS with Time Series Data • Last time‚ we discussed the assumptions necessary for OLS parameter estimates to be consistent in a time series context. • We also compared these assumptions with the assumptions necessary for OLS to be unbiased. • In general‚ the conditions are more restrictive for OLS to be unbiased than for OLS to be consistent‚ so in a time series context‚ it
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Publisher: VJBooks Inc. Editor: Vijay Gupta Author: Vijay Gupta www.vgupta.com About the Author Vijay Gupta has taught statistics‚ econometrics‚ SPSS‚ LIMDEP‚ STATA‚ Excel‚ Word‚ Access‚ and SAS to graduate students at Georgetown University. A Georgetown University graduate with a Masters degree in economics‚ he has a vision of making the tools of econometrics and statistics easily accessible to professionals and graduate students. At the Georgetown Public Policy Institute he received rave reviews
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Instrumental variables and panel data methods in economics and finance Christopher F Baum Boston College and DIW Berlin February 2009 Christopher F Baum (Boston College) IVs and Panel Data Feb 2009 1 / 43 Instrumental variables estimators Regression with Instrumental Variables What are instrumental variables (IV) methods? Most widely known as a solution to endogenous regressors: explanatory variables correlated with the regression error term‚ IV methods provide a way to
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Introduction to Stata Christopher F Baum Faculty Micro Resource Center Boston College August 2011 Christopher F Baum (Boston College FMRC) Introduction to Stata August 2011 1 / 157 Strengths of Stata What is Stata? Overview of the Stata environment Stata is a full-featured statistical programming language for Windows‚ Mac OS X‚ Unix and Linux. It can be considered a “stat package‚” like SAS‚ SPSS‚ RATS‚ or eViews. Stata is available in several versions: Stata/IC
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Bufn758Q—Quantitative Investment Strategy Name & Student ID #:_____________________ Professor Wermers Spring 2010 Numeric Investors 1. Briefly discuss the two momentum strategies of Numeric Investors. First approach – Based on past changes in analysts’ estimates‚ where they would cluster their forecasts of company earnings‚ and would revise their earnings estimates incrementally instead of big jumps. This would force analysts to update their estimates and would make consensus estimates
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TABLE OF CONTENTS List of Tables i List of Figures iv Abstract v Key Terms ix CHAPTER-1 Introduction 1.1 Introduction to Dividends 1 1.2 A Short History of Dividend Policy 6 1.3 Dividend Policy 9 1.4 Economic Rationale to Dividends 12 1.5 Dividend Policy and its Linkages with other Financial Policies 15 1.6 Pure Vs Smoothed Residual Dividend Policy 16 1.7 Dividend Declaration Process 17 1.8 Alternative Forms of Dividends 18
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Basic Estimation Techniques In order to implement the various techniques discussed in this class‚ the students must be able to determine the mathematical relation between the economic variables that make up the various functions used in economicsdemand functions‚ production functions‚ cost functions‚ and others. For example‚ a manager often must determine the total cost of producing various levels of output. As you will see later‚ the relation between total cost (C) and quantity
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Uttam Kumar Dey Marketing Analytics Assignment 3 Part 1 Do box office rankings affect a movie’s long-term box office performance? To understand the effect of a movie’s box office rankings has on its box office performance‚ Nonlinear Estimation models can be used to interpret the relationships and make recommendations. First‚ let us familiarize ourselves with the relationship‚ in the movies database‚ we are trying to analyze. This relationship is as below: Subsequent Earnings = b0*exponential(b1*ReleaseRank)
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