ociety’s Use of Quantitative Models Serita A Riggens MSA 640 Quantitative Applications in Decision-Making Central Michigan University Instructor: Dr. Howard E Van Auken August 11‚ 2010 Society’s use of Quantitative Methods The use of quantitative models in real-world decision making is a practice that has become the norm in society. Decision makers have searched continually for ways to reduce or eliminate the uncertainties in the decision making process. For decades
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An estimated regression coefficient is 10 with a standard error of 5. The null hypothesis is that the partial regression coefficient equals zero. What is the value of the t-statistic for testing the null hypothesis of the regression coefficient? Choose one answer. | a. 2 | | | b. 0.5 | | | c. 5 | | | d. 1 | | Correct Marks for this submission: 1/1. Question 2 Marks: 1 Given the demand function in log-linear form: Q = 120 - 1.5P + 12ADV where Q = quantity‚ P = price‚ and ADV
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that are distributed to young adults and adults who are currently using at least one of the three most popular mobile communication devices in Jakarta: Blackbery‚ iPhone‚ and Android. The data are then gathered and transformed into three different regression model‚ where test of significances can be taken towards them. The result is that the three mobile communication devices are each fulfilling a different specific need of the customers. These differences are actually their competitive advantage over
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Hypothesis’‚ Expert Financial Planning Advice for Industry Leaders‚ New York‚ John Wiley and Sons. 5. Cohen J.‚ Cohen P.‚ West S.G. and Aiken L.S. (2003)‚ Applied Multiple Regression/Correlation Analysis for the Behavioural Sciences‚ 2nd Edition‚ New Jersey‚ Lawrence Erlbaum Associates. 6. Draper N.R and Smith H. (1998)‚ Applied Regression Analysis‚ Wiley Series in Probability and Statistics‚ New York‚ John Wiley and Sons. 7. Ekpo A.H. and Umoh O.J. (2007)‚ An Overview of the Nigerian Economic Growth
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uses additional independent variables and another transforms the independent variable. • Addition of Independent Variables Often the reason autocorrelation occurs in regression analyses is that one or more important predictor variables have been left out of the analysis. For example‚ suppose a researcher develops a regression forecasting model that attempts to predict sales of new homes by sales of used homes over some period of time. Such a model might contain significant autocorrelation. The
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"THE INFLUENCE OF DEMOGRAPHIC FACTORS AND VALUES ON THE CONSUMERS’ WILLINGNESS TO PAY MORE FOR GREEN PRODUCTS” I.INTRODUCTION 1.1. Research Background Nowadays‚ people have become more aware of their environment. They try to slow down the process of global warming in many different ways. One of the efforts to slow down the process of global warming is that now people try to create‚ produce‚ and market the environmental-friendly products or‚ also known as‚ green products. People who pay attention
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A retail store has recently hired you as a consultant to advice on economic conditions. One important indicator that the retail store is concerned about is the unemployment rate. The retail store has found that an increase in the unemployment rate will cause a lack of consumer spending in their stores. Retail stores use the unemployment rate to estimate how much inventory to keep at their stores‚ which is important in maintaining cost effectiveness. In this consultant role you will apply calculations
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GSM 5000 MANAGERIAL ECONOMICS PROBLEM-BASED LEARNING 2 PROFESSOR DR. MAD NASIR SHAMSUDIN GROUP 5: AZLINA IDRIS MOHD KHAIRUL AINUDDIN MD ZIN ONG WEE HONG VOO LIDY GM04172 GM04218 GM04213 GM01460 1. Learning Issues 1.1 What is forecasting? Forecasting is the process of making statements about future happenings based on the previous data collected. Forecasting usually is an estimation of the future data‚ happenings‚ trends‚ values‚ etc for the specified date. A commonplace example is estimation of
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Definition of variables R 3 2.2 Japan 3 2.3 USA 3 2.4 India 3 3 Hypothesis / Research Question(1/2) R 3 4 Methodology (2) M 3 4.1 Correlation 3 4.2 Simple Regression 4 4.3 Multiple Regression 4 4.4 Measure of fit 4 4.5 Level of Significance 5 5 Data 5 6 Findings 6 6.1 Simple Regression 6 6.2 Multiple Regressions (5) T 7 6.2.1 All factors 7 6.2.2 All factors excluding children born outside of marriage 7 6.2.3 Education factor 8 6.2.4 Economic factors 8 6.2.5
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between the demand of borrowers and the inabilities to meet these demands. Purpose The purpose of this paper is to analyse the liquidity risk and the impact of liquidity risk on performance of the manufacturing sector. Methodology Least square regression model is used in this study. Data of manufacturing sector is used to achieve the objective of this research paper. ROA and EPS are used as measures of liquidity risk and performance. Findings This study shows that liquidity risk has a great impact
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