returns in relation to the market index. It is calculated as: Beta (Mobil) = Covariance (Return of Mobil oil‚ Return of Market) / Variance (Return of Market). Using Linear least squares‚ the estimated beta is the same as that calculated using Regression analysis on Excel. Estimated Beta is 0.714 which implies that the total return of Mobil Oil’s stock is likely to move up and down 71.4% of the time when the market changes. As 0.714 < 1‚ Mobil Oil’s stock is less volatile than the overall Market
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IMM-TR-2002-12 Please direct communication to Hans Bruun Nielsen (hbn@imm.dtu.dk) Contents 1. Introduction 1 2. Modelling and Prediction 1 2.1. The Kriging Predictor . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2.2. Regression Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.3. Correlation Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3. Generalized Least Squares Fit 9 3.1. Computational Aspects . . . . . . . . .
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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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......................................................................................... 3 4. Results.............................................................................................................. 3 4.1 Simple linear regression and heteroskedacity analysis .................................................... 3 4.2 Correlation and residuals analysis .................................................................................... 6 4.3 Hypothesis testing
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Demand and Behavior © 2005 Prentice Hall‚ Inc. 4.1 Getting Information About C Ab t Consumer Behavior B h i Expert opinion Consumer surveys Test marketing and price experiments i t Analyses of census and other y historical data Regression analysis © 2005 Prentice Hall‚ Inc. 4.2 Managerial Rule of Thumb: Analyzing C A l i Consumer Behavior B h i Managers must consider 1. 2. 3. Whether the participating groups are truly representative of the larger population
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2.1 2.2 | Estimated regression equations. Independent Variable- Annual Income. Independent Variable- Household Size | 7 8 9 | 3 | Better predictor of annual credit card charges | 10 | 4 | Independent variables- Annual income and Household size | 11 | 5 | Forecasting Annual Credit Charge | 12 | 6 | Need for other independent variables | 13 | 7 | Test the significance of the overall regression model | 14 | 8 | Test the significance of the individual regression coefficients | 15-16
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AND REGRESSION Introduction Correlation and Regression Scatter Plot/Diagram Coefficient of Correlation Simple Linear Regression sanizah@tmsk.uitm.edu.my Learning objectives • Explain the concept of correlation • Calculate Pearson’s correlation coefficient and interpret the results • Calculate Spearman’s rank correlation for qualitative and quantitative data and interpret the results • Determine the regression equation for a set of data and interpret the equation • Use the regression equation
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Brooks/Cole‚ Cengage Learning 2 Three Tools we will use … • Scatterplot‚ a two-dimensional graph of data values • Correlation‚ a statistic that measures the strength and direction of a linear relationship between two quantitative variables. • Regression equation‚ an equation that describes the average relationship between a quantitative response and explanatory variable. Copyright ©2011 Brooks/Cole‚ Cengage Learning 3 3.1 Looking for Patterns with Scatterplots Questions to Ask about
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2008/09 and 2010/11 | | | | Table of Contents Introduction 2 Literature Review 2 Methodology 3 Variables Used – Characteristics of Workers 5 Results 7 Discussion 7 Bibliography 8 Appendix A 9 Selectivity Bias Logit Regression Results: 9 Introduction This paper explores the dynamics of gender wage discrimination in Pakistan for two data sets; Labour Force Survey for the year 2008/09 and 2010/11. We will explore whether or not women are discriminated against‚ as it
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your Part II Final Project assignment. When constructing a multiple regression model‚ we must ask the question “Does a linear relationship exist between the dependent variable and the independent variables. Based on the multiple regression model of the Virginia hospitals data‚ all of the independent variables are not significant predictors of the dependent variable. This is evidenced by the varying p-values shown in the regression analysis. The null hypothesis for each independent variable states
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