A brief overview of the classical linear regression model What is a regression model? Regression versus correlation Simple regression Some further terminology Simple linear regression in EViews -- estimation of an optimal hedge ratio The assumptions underlying the classical linear regression model Properties of the OLS estimator Precision and standard errors An introduction to statistical inference 27 27 28 28 37 2.6 2.7 2.8 2.9 v 40 43 44 46 51 vi Contents
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M. (1992)‚ “An experimental approach to making retail store environmental decisions”‚ Bakewell‚ C.‚ Mitchell‚ V.W. and Rothwell‚ M. (2006)‚ “UK Generation Y male fashion consciousness”‚ Journal of Belsey‚ D.A.‚ Kuh‚ E. and Welsch‚ R.E. (2004)‚ Regression Diagnostics: Identifying Influential Data and Sources of Bocock‚ R. (1993)‚ Consumption‚ Routledge‚ London. Burton‚ S.‚ Netemeyer‚ R.G. and Lichtenstein‚ D.R. (1995)‚ “Gender differences for appearance-related attitudes and Cash‚ T.F. and Pruzinsky
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the Human Resources Information System (HRIS) of a company‚ called here “Engineering Solutions‚” and analyzes the drivers of potential for promotion among a sample of engineers. The methods used consist of basic statistical procedures‚ multiple regressions‚ ordered logits‚ and decompositions. The results show which variables are the main drivers of potential for promotion in this organization‚ which are minor drivers‚ and which do not matter at all. Statement of Confidentiality: This manuscript
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True or False: Correlations are used to help identify the relationship between two variables. For example‚ the amount of rain in July (x) and the size of tomatoes in August (y) could be analyzed using correlation and regression techniques. True Feedback: Correlation and regression are the techniques statisticians use for analyzing relationships between variables. 5. The scores of the top ten finishers in a recent LPGA Valley of the Stars Tournament are listed below. (Source: Los Angeles Times)
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Multiple Regression Analysis 16 3. Multiple Regression Analysis The concepts and principles developed in dealing with simple linear regression (i.e. one explanatory variable) may be extended to deal with several explanatory variables. We begin with an example of two explanatory variables‚ both of which are continuous. The regression equation in such a case becomes: Y = α + β1x1 + β2 x2 It is customary to replace α with β 0‚ and so all future regression equations would be written as
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in dollars)‚ which are stored in the file shore.xls. Use the data in that file to answer the following questions: • Use Kstat or Excel to construct a scatterplot for these data with size on the horizontal axis. • Use Kstat to dtermine the estimated regression equation. • Predict the selling price for a home with 2‚600 square feet. 3. Accesss bschools2002.xls which contains data regarding the top 30 business schools based on the 2002 Business Week ratings. Many business schools surveys including this
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Assumptions and Conditions for using Statistical Tests Bright Eugene Imo State University‚ Owerri ABSTRACT All statistical procedures have underlying assumptions‚ some more stringent than others. In some cases‚ violation of these assumptions will not change substantive research conclusions. In other cases‚ violation of assumptions will undermine meaningful research. Establishing that one’s data meet the assumptions of the procedure one is using in an expected component of all quantitatively
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term is negative‚ which of the following can you say with certainty? a. b. c. d. e. The BIAS will be -4.00 The BIAS will be 4.00 The BIAS will be greater than 4.00 The BIAS will be less than -4.00. Nothing can be concluded about the BIAS 5. In regression analysis‚ the _____________ tells you how much the dependent variable will increase when the independent
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INTRODUCTION LITERATURE REVIEW The current economic crisis has become a major concern of all nations today. It has led policymakers and economists to rethink about the instrument for economics stability. One of the most damaging consequences of this crisis is the consumption instability‚ which negatively affects risk adverse agents’ welfare. As mentioned by Athahasoulis and van Wincoop (2000) as well as Pallage and Robes (2003)‚ consumption instability could have detrimental consequences for
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The following table gives the amount of fertilizer (in pounds) used and the yield of corn (in bushels)for each of the seven acres. Fertilizer Used (x) 120 80 100 70 88 75 110 Yield of Corn(y) 138 112 129 96 119 104 134 a. Find the least squares regression line. a. Calculate r and r2 and explain what they mean. b. Predict the yield of corn if the fertilizer used is 105 pounds. c. Construct a 98% confidence interval for B. d. Test at the 5% significance level if B is different from zero. 2. The following
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