numerical values‚ not percentages or ratios. Then calculate 2 using this formula‚ as shown in Table B.1. Note that we get a value of 2.668 for 2. But what does this number mean? Here’s how to interpret the 2 value: 1. Determine degrees of freedom (df). Degrees of freedom can be calculated as the number of categories in the problem minus 1. In our example‚ there are two categories (green and yellow); therefore‚ there is I degree of freedom. 2. Determine a relative standard to serve as the basis
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29 ! ! ! C5-q11 A:(1)IRR(DF):$-750000+$310000/(1+r)+$430000(1+r)^2+ $330000(1+r)^3=0 r=19.8253% IRR(SR)=$-2100000+$1200000/(1+r)+$760000(1+r)^2+$850000/ (1+r)^3=0 r=17.3641% since r1>r2 project deepwater fishing should be chosen. (2)incremental cash flow: initial investment: -$1350000 year1:$890000 year2:$330000 year3: $520000 IRR : $-1350000+$890000/(1+r)+$330000/(1+r)^2+$520000/(1+r)^3=0 r=15.76% since 15.76%>14% ‚ project submarine ride should be chosen. (3)NPV(df)=$-750000+$310000/(1+14%)+$430000/(1+14%)^2+
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Solutions‚ Chapter 2/HL ANSWERS TO CHAPTER 2 The Simple Regression Model Econometrics Economics of Innovation and Growth A = Problems B = Examples (from chapter 2) C = Cumputer Exercises 1 Solutions‚ Chapter 2/HL A: Problems 2.1 Let kids denote the number of children born to a woman‚ and let educ denote years of education for the woman. A simple model relating fertility to years of education is kids = β 0 + β1educ + u where u is the unobserved error. (i) (ii) What kind of factors
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is IBI = 52.9 + 0.460 Area Predictor Coef SE Coef T P Constant 52.923 4.484 11.80 0.000 Area 0.4602 0.1347 3.42 0.001 S = 16.5346 R-Sq = 19.9% R-Sq(adj) = 18.2% Analysis of Variance Source DF SS MS F P Regression 1 3189.3 3189.3 11.67 0.001 Residual Error 47 12849.5 273.4 Total 48 16038.8 Unusual Observations Obs Area IBI Fit SE Fit Residual St Resid 10 70.0 89.00 85
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To: Kirk Callow‚ Steelworks Inc. After a careful analysis of the Durabend (DB) and Duraflex (DF) lines it has become evident that there is volatile demand for specialty products‚ given that both DB and DF have high coefficients of variation of 1.08‚ 1.18 respectively (Exhibit 1). In the case of DB‚ the volatility can be attributed to the lack of high-volume customers (194/195)‚ and the abundance of low-volume purchasers (Exhibit 2). To solve this problem it will be necessary for Steelworks to
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This article was downloaded by: [66.171.228.253] On: 28 April 2014‚ At: 08:30 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House‚ 37-41 Mortimer Street‚ London W1T 3JH‚ UK International Journal of Testing Publication details‚ including instructions for authors and subscription information: http://www.tandfonline.com/loi/hijt20 Writing Essays on a Laptop or a Desktop Computer: Does It Matter? a Guangming
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ECMB12H3F L30 Quantitative Methods in Economics II Final Examination Wednesday December 14‚ 2011 2–4 pm Instructor: Victor Yu Last Name (Print): Solution First Name (Print): Student Number: Circle your section Aids allowed: L30 Wednesday Deferred Exam Calculator and two aid sheets (four 8.5"x11" pages) written or typed on both sides Time allowed: Two (2) hours This exam consists of 22 questions in 15 pages including this cover page. It is the student’s responsibility to hand in a complete exam
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Birth Order and the Judgmental Functions A Problem Set In Partial Fulfillment of the Requirements in SOCIAL STATISTICS MS. PHLORITA RIDAO Professor Prepared by: SHAHARA A. ABO NORIA B. USMAN AMIL KHARIM BAI ARCA SULTAN April 4‚ 2008 Birth Order and the Judgmental Functions I. Introduction Each individual possesses different sets of characteristics. In this study‚ various characteristics of a person are surveyed to link it with decision making pattern of the
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independent random variables and therefore can never be less than zero; it extends indefinitely in the positive direction. Actually the chi-square distributions constitute a family‚ with each distribution defined by the degrees of freedom (df) associated with it. For small df values the
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Confidence intervals and prediction intervals from simple linear regression The managers of an outdoor coffee stand in Coast City are examining the relationship between coffee sales and daily temperature. They have bivariate data detailing the stand ’s coffee sales (denoted by [pic]‚ in dollars) and the maximum temperature (denoted by [pic]‚ in degrees Fahrenheit) for each of [pic] randomly selected days during the past year. The least-squares regression equation computed from their data
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