................................................................... 4 Justification for the Chosen Variables................................... 4 Regression Analysis................................................................... 9 Explanation of results.............................................................. 9 Comments on Regression Analysis........................................ 11 Elasticity......................................................................................
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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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CHAPTER 7 THE TWO-VARIABLE REGRESSION MODEL: HYPOTHESIS TESTING QUESTIONS 7.1. (a) In the regression context‚ the method of least squares estimates the regression parameters in such a way that the sum of the squared difference between the actual Y values (i.e.‚ the values of the dependent variable) and the estimated Y values is as small as possible. (b) The estimators of the regression parameters obtained by the method of least squares. (c) An estimator
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Credits 3 Prerequisites EPSE 482 and EPSE 481 Instructor Dr. Amery Wu Course Correspondence email at amery.wu@ubc.ca Office Hours By appointment via email Textbook Cohen‚ J.‚ Cohen‚ P.‚ & Stephen‚ G. West‚ and Leona S. Aiken (2003). Applied multiple regression/correlation analysis for the behavioral sciences (Third Edition) ISBN: 978-0805822236 Other Support The Department of ECPS provides methodology support to its students who are taking quantitative research-related courses or who need quantitative
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CORRELATION & LINEAR REGRESSION Prof. Jemabel Gonzaga-Sidayen Spearman rank order correlation coefficient rho (rs) • Spearman rho is really a linear correlation coefficient applied to data that meet the requirements of ordinal scaling • Formula: rs = 1 - 6 Σ D i 2 N3 - N – Di = difference between the ith pair of ranks – R(Xi) = rank of the ith X score – R(Yi) = rank of the ith Y score – N = number of pairs of ranks Try this Subject Proportion of Similar Attitudes (X) Attraction (Y) Rank of
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Demand Forecasting Demand forecasting • Why is it important • How to evaluate • Qualitative Methods • Causal Models • Time-Series Models • Summary Production and operations management Product Development long term medium term short term Product portifolio Purchasing Manufacturing Distribution Supply network designFacility Partner selection location Distribution network design and layout Derivatuve Supply Demand forecasting is product developmentcontract the starting ? point
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Forecasting Models NMIMS Forecasting techniques Qualitative models time series models causal models 1.Delphi method 1.moving averages 1.regression analysis 2.Opinion poll 2.exponential smoothing 2.multiple regression 3.Historical Analogy 3.econometric models 4.Field Surveys 5.Business barometers 6.Extrapolation Technique 7.Input-Out put Analysis 8.Lead Lag Analysis 9.Sales force composites 10.Consumer Market survey Simple Average Method
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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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bill payments will help our business and our customer’s personal and internal finances. To validate the relationship between the amount of a bill and the number of days late it is for both commercial and residential accounts‚ we apply a linear regression method to generate an accurate statistical analysis of the data. By using this form of analysis‚ we will be able to answer the following questions with the information provided. * Does the size of the bill somehow relate to the number of days
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Quantitative Applications in Management & Research ASSIGNMENT PROGRAM: SEMESTER-I Subject Name : Quantitative Applications in Management & Research Permanent Enrollment Number (PEN) : Roll Number (SEN) : Student Name : INSTRUCTIONS a) Students are required to submit all three assignment sets. ASSIGNMENT Assignment A Assignment B Assignment C b) c) d) e) DETAILS Five Subjective Questions Three Subjective Questions + Case Study 40 Objective Questions MARKS 10 10 10 Total weightage given to these
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