SOCIALLY RESPONSIBLE INVESTMENT: IS IT PROFITABLE?∗ PHOEBUS J. DHRYMES Columbia University July 1997; revised June 1998 1 What is Socially Responsible? Before we can answer the question we posed in the title‚ we need to define just what is “socially” responsible. Evidently‚ the meaning varies with time and place‚ since social responsibility is defined by a group’s cultural and ethical values. For example in the middle ages lending with interest was not considered ethical‚ let alone “socially
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Assignment-4 (Chs. 10‚ 12 and 13 : these chapters are marked different in the 7th ed. Chs 12 and 13 of the 6th ed are marked as Chs 13 and 14 in the 7th ed) Due by Midnight of Sunday‚ June 29th‚ 2014 (Dropbox 4): Total 125 points True/False (two points each) Chapter10 1. In an experiment involving matched pairs‚ a sample of 15 pairs of observations is collected. The degree of freedom for the t statistic is 14. true 2. In testing the difference between two means from two independent populations‚
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Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 20.16667 1.373732 14.6802 4.3E-08 17.1058 23.22753 17.1058 23.22753 Period -0.07692 0.186653 -0.41212 0.688949 -0.49281 0.338967 -0.49281 0.338967 From regression output‚ t = -.412 and p = .689. A stationary model seems appropriate since the linear term‚ Period‚ is not significant. 7.1 c. Forecast for January -- 19‚ for upcoming year – 12*19 = 228 7.1 d. Forecast for January -- 20.4 e. 4 month
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worksheet file BONUS and as a csv file Bomus_CW_2. a) Explain what is meant by a traditional regression model. Hence i. Define R2‚ and explain how it can be used to compare competing regression models and why R2_adjusted is needed. ii. Explain what is meant by a t-test within the context of regression modelling. iii. Discuss the differences between a multiple regression model and a GLM. b) Use a 2-stage GLM procedure to model
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Probabilities 6. Theoretical Distributions 7. Sampling & Sampling Distributions 8. Estimation 9. Testing of Hypothesis in case of large & small samples 10. Chi-Square 11. F-Distribution and Analysis of variance (ANOVA) 12. Simple correlation and Regression 13. Business Forecasting 14. Time Series Analysis 15 . Index Numbers Indian B2B site for Manufacturers & Exporters Q: What’s the definition of Statistics ? A : Statistics are usually defined as: 1. A collection of numerical data that measure
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qualified respondents in Commart Thailand 2011 Event at Queen Sirikit Convention Center on 17th – 20th March 2011. A total of 191 respondents were participated in this study. The data were analyzed and summarized with SPSS software and binary logistic regression analysis was used to examine which sale promotion factors that impact on consumers’ purchasing decision of Portable PC Acer and Compaq & HP. The results of this research is indicated that the sale promotion factors “Offer member card for discount”
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frequently trade; (4) the beta is not necessarily a complete measure of risk (you may need multiple betas). Regression parameters There are 3 key decisions: • Relative index • Date range • Period or returns interval Raw vs. adjusted beta The beta of a stock can be presented as either an adjusted or raw beta. Raw beta‚ also known as historical beta‚ is obtained from linear regression based on the observed relationship between the security’s return (using historical data) and the returns on an index
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previous years (EXHIBIT A). We see that 1977 and 1978 show unusually high sales. This can imply that sales do not necessarily depend on time. This is confirmed by a regression of sales in $ with time. Even though the R-squared value is not low 56.9%‚ the actual sales for 1978 does not lie in the 95% confidence interval predicted our regression (=(-7016.76 + 129.2*78 + 2(619))) (EXHIBIT B). From reading the case study‚ price seems to be a big factor determining sales. The Brazil frost disaster that
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Model To Be Studied By Residual 1. The regression function is not linear. 2. The error terms do not have constant variance. 3. The error terms are not independent. 4. The model fits all but one or few outliers‚ 5. The error terms are not normally distributed. 6. One or several important predictor(s) have been omitted from the model. Diagnostic For Residuals Six diagnostic plots to judge departure from the simple linear regression model * Plot of residuals against predictor
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Crime Rates: An Econometric Analysis using population‚ unemployment and growth Table of Contents I. Introduction A.) Background of the Study B.) Problem Statement C.) Objectives D.) Significance of the Study E.) Scope and Limitations II. Review of Related Literature III. Operational Framework A.) Variable List B.) Model Specification C.) A-priori Expectations IV. Methodology A.) Data B.) Preliminary Tests V. Results and Discussions VI. Conclusion
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