...are: Data/Data Analysis/Random Number Generator. Here are the definitions of the input variables taken from Excel Help: Number of Variables Enter the number of columns of values that you want in the output table. If you do not enter a number, Microsoft Office Excel fills all columns in the output range that you specify. Number of Random Numbers Enter the number of data points that you want to see. Each data point appears in a row of the output table. If you do not enter a number, Excel fills all rows in the output range that you specify. Distribution Click the distribution method that you want to use to create random values. Uniform This is characterized by lower and upper bounds. Variables are drawn with equal probability from all values in the range. A common application uses a uniform distribution in the range 0...1. The dialog box, with the variables, is shown below. Here three lists of random numbers are requested – in case the first list has duplicated numbers. The companies are numbered from 2 to 69 in the data set. Choose the uniform distribution. Format the output to the nearest whole number to get your ten company numbers. In the example, the first column has no duplicated company numbers and so can be used without any adjustments – using the remaining columns. The output, formatted to the nearest whole number, is shown first. Random Numbers between 2 and 69 and Random Number Generator Dialog Box 61 16 60 3 58 14 26 19 53 38 34 6 14 21 64 47 22 58 51 44 19 6 18 62...
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...Available online at www.sciencedirect.com ScienceDirect Solar Energy 112 (2015) 68–77 www.elsevier.com/locate/solener Short-term reforecasting of power output from a 48 MWe solar PV plant Yinghao Chu, Bryan Urquhart, Seyyed M.I. Gohari, Hugo T.C. Pedro, Jan Kleissl, Carlos F.M. Coimbra ⇑ Department of Mechanical and Aerospace Engineering, Jacobs School of Engineering, Center for Renewable Resource Integration, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA Department of Mechanical and Aerospace Engineering, Jacobs School of Engineering, Center for Energy Research, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA Received 31 March 2014; received in revised form 14 November 2014; accepted 17 November 2014 Communicated by: Associate Editor Frank Vignola Abstract A smart, real-time reforecast method is applied to the intra-hour prediction of power generated by a 48 MWe photovoltaic (PV) plant. This reforecasting method is developed based on artificial neural network (ANN) optimization schemes and is employed to improve the performance of three baseline prediction models: (1) a physical deterministic model based on cloud tracking techniques; (2) an autoregressive moving average (ARMA) model; and (3) a k-th Nearest Neighbor (kNN) model. Using the measured power data from the PV plant, the performance of all forecasts is assessed in terms of common error statistics (mean bias, mean absolute error...
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...UQ Business School MKTG7510 Market and Consumer Research Introduction to SPSS Course Coordinator: Dr Ravi Pappu Introduction to SPSS This is a brief on how to use SPSS. The purpose of this chapter is to introduce you to SPSS. You are encouraged to actively access the interactive help of SPSS and ask your tutor questions if you get lost. You are expected to spend time outside of class (i) familiarising yourself with SPSS and (ii) completing the homework exercises to complete the Assignment 2 (Research Report) for assessment (20% of your MKTG7510 Grade). SPSS is accessible in all computer labs in Building 39A and 35. Please check current times of access to the computer labs and plan your study time accordingly. 1. How to create a new project? (1) Click on the ‘Start’ menu. Click on ‘All Programs’. (2) You will find the folder ‘Data Analysis and Stats Programs’ - click it to open the folder. (3) Click on the ‘IBM SPSS statistics’ folder. Click on the ‘IBM SPSS Statistics’ icon. (4) Once SPSS is open, the screen (see Fig. 1) asks you what you would like to do: either type in the data manually, or find an existing data file (as circled below). Figure 1: Create a New Project MKTG7510 Market and Consumer Research S1-2014 P. 1/20 2. What are the main components of an SPSS ‘project’? When you open a new ‘project’ you will see the Data Editor window where the raw data are visible. However, there are multiple windows that allow you to interact with the data...
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...Project Management Institute A Guide to the Project Management Body of Knowledge (PMBOK® Guide) – Fifth Edition Licensed To: Jorge Diego Fuentes Sanchez PMI MemberID: 2399412 This copy is a PMI Member benefit, not for distribution, sale, or reproduction. Library of Congress Cataloging-in-Publication Data A guide to the project management body of knowledge (PMBOK® guide). -- Fifth edition. pages cm Includes bibliographical references and index. ISBN 978-1-935589-67-9 (pbk. : alk. paper) 1. Project management. I. Project Management Institute. II. Title: PMBOK guide. HD69.P75G845 2013 658.4’04--dc23 2012046112 ISBN: 978-1-935589-67-9 Published by: Project Management Institute, Inc. 14 Campus Boulevard Newtown Square, Pennsylvania 19073-3299 USA Phone: +610-356-4600 Fax: +610-356-4647 Email: customercare@pmi.org Internet: www.PMI.org ©2013 Project Management Institute, Inc. All rights reserved. “PMI”, the PMI logo, “PMP”, the PMP logo, “PMBOK”, “PgMP”, “Project Management Journal”, “PM Network”, and the PMI Today logo are registered marks of Project Management Institute, Inc. The Quarter Globe Design is a trademark of the Project Management Institute, Inc. For a comprehensive list of PMI marks, contact the PMI Legal Department. PMI Publications welcomes corrections and comments on its books. Please feel free to send comments on typographical, formatting, or other errors. Simply make a copy of the relevant page of the book, mark the error, and...
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...the monthly earnings? X = monthly earnings = revenues – costs = P x M – ( L + 3,995 + 11 x M ) = (P – 11 ) x M – L – 3,995 Which of these quantities are random variables? P M L X = = = = price of prix fixe meal number of meals sold labor cost monthly earnings 3 (X is a function of random variables, so it is a random variable) Assumptions Regarding the Behavior of the Random Variables M P = number of meals sold per month We assume that M obeys a Normal distribution with µ = 3,000 and σ = 1,000 = price of the prix fixe meal We assume that P obeys the following discrete probability distribution Scenario Very healthy market Healthy market Not so healthy market Unhealthy market Price of Prix Fixe Meal $20.00 $18.50 $16.50 $15.00 Probability 0.25 0.35 0.30 0.10 L = labor costs per month We assume that L obeys a uniform distribution with a minimum of $5,040 and maximum of $6,860 4 The Behavior of the Random Variables, cont. X = earnings per month We do not know the distribution of X . We assume, however, that X = (P – 11 ) x M – L – 3,995 Always ask the following questions in any management analysis: • •...
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...oil-sand layer, and the primary recovery rate (in barrels per acre per foot of thickness). Based on geological information, the following probability distributions have been estimated –Estimate of Productive Area Acres Probability 8,000 - 9,000 0.05 9,000 - 10,000 0.10 10,000 - 11,000 0.15 11,000 - 12,000 0.35 12,000 - 13,000 0.25 13,000 - 14,000 0.10 –Estimate of Pay Thickness Smallest Value: 15 ft.. Most Likely Value: 50 ft.. Largest Value: 120 ft.. –Estimate of Primary Recovery Uniform Distribution: Minimum Value: 20 bbl./acre-ft.. Maximum Value: 90 bbl./acre-ft.. Using @Risk Open @Risk (it is available under Start->Programs->Palisade Decision Tools->@Risk 4.5 for Excel). Excel should come up with 2 additional toolbars. For this problem we will need the following distributions: Productive area: discrete distribution, described by a histogram, represented by RISKHISTOGRM(8000, 14000, {0.05, 0.10, 0.15, 0.35, 0.25, 0.10}) Pay thickness: triangular distribution, with a minimum value of 15ft, maximum value of 120ft, and most likely value of 50ft, represented in @Risk by RISKTRIANG(15, 50, 120) Primary Recovery Rate: uniform distribution, with any value being equally likely in the interval 20-90 bbl./acre-ft, represented by RISKUNIFORM(20,90). @Risk offers the above functions...
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...The Review of Economic Studies Ltd. Alternative Theories of Distribution Author(s): Nicholas Kaldor Source: The Review of Economic Studies, Vol. 23, No. 2 (1955 - 1956), pp. 83-100 Published by: The Review of Economic Studies Ltd. Stable URL: http://www.jstor.org/stable/2296292 Accessed: 22/03/2010 10:03 Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/action/showPublisher?publisherCode=resl. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission. JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. The Review of Economic Studies Ltd. is collaborating with JSTOR to digitize, preserve...
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...In the second half of the 19th century neoclassical economists emerged as a revolt against the school of classical economic thought. Neoclassical economics sought to explain the maximisation of utility with an underlying theory of rational choice theory. In the post war period a movement occurred that sought to synthesis the macroeconomic, long run theories of John Maynard Keynes with the microeconomic, short-run theories of neoclassical economics. This essay will explore how John Hick’s ISLM synthesis of Keynes is related to the neoclassical growth model. In addition it will explore how both the growth model and theory of capital reproduce problems inherent in the explanation of multi-sector economies. The Keynesian revolution, one of the “most significant events in twentieth century economic science,” disputed society’s adherence to classical laissez-faire economics (Yaroufakis, Halevi, & Theocarakis, 2011). The unsubstantiated ability of the self-clearing markets to maintain equilibrium was challenged during the early twentieth century by John Maynard Keynes in his text The General Theory. Yet as the depression strengthened and the global economic climate changed, a group of economists emerged that ceased to be either anti- or pro-Keynesian. Known as the Neo-Keynesians, they incorporated the macroeconomic framework of Keynes’ theory for understanding short run aggregate issues while the neoclassical model remained relevant for long term microeconomic analysis of growth. With...
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...STOCHASTIC FRONTIER ANALYSIS 1 MOTIVATION • Usual textbook presentations treat producers as successful optimizers. They maximize production, minimize cost, and maximize profits. • Conventional econometric techniques build on this paradigm to estimate production/cost/profit function parameters using regression techniques where deviations of observed choices from optimal ones are modeled as statistical noise. • However though every producer may attempt to optimize, not all of them may succeed in their efforts. For example, given the same inputs, and the same technology, some will produce more output than others, i.e., some producers will be more efficient than others. • Econometric estimation techniques should allow for the fact that deviations of observed choices from optimal ones are due to two factors: failure to optimize i.e., inefficiency due to random shocks • Stochastic Frontier Analysis or SFA is one such technique to model producer behavior. 2 USEFULNESS OF SFA • SFA produces efficiency estimates or efficiency scores of individual producers. Thus one can identify those who need intervention and corrective measures. Since efficiency scores vary across producers, they can be related to producer characteristics like size, ownership, location, etc. Thus one can identify source of inefficiency. SFA provides a powerful tool for examining effects of intervention. For example, has efficiency of the banks changed after deregulation? Has this change varied across...
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...Ian Wamboldt Math 533- Applied Managerial Statistics Course Project Part C February 10, 2013 1. Generate a scatterplot for CREDIT BALANCE vs. SIZE, including the graph of the "best fit" line. Interpret. ------------------------------------------------- MINITAB OUTPUT: ------------------------------------------------- Regression Analysis: Credit Balance($) versus Size ------------------------------------------------- ------------------------------------------------- The regression equation is ------------------------------------------------- Credit Balance($) = 2591 + 403 Size ------------------------------------------------- ------------------------------------------------- ------------------------------------------------- Predictor Coef SE Coef T P ------------------------------------------------- Constant 2591.4 195.1 13.29 0.000 ------------------------------------------------- Size 403.22 50.95 7.91 0.000 ------------------------------------------------- ------------------------------------------------- ------------------------------------------------- S = 620.162 R-Sq = 56.6% R-Sq(adj) = 55.7% ------------------------------------------------- ------------------------------------------------- ------------------------------------------------- Analysis of Variance ------------------------------------------------- ------------------------------------------------- Source DF ...
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...ECMT 1010 ASSIGNMENT 2012 QUESTION 1 Fig 1.1 Histogram illustrating the number of years of education undertaken by farmers in Fiji. Figure 1.1 shows a histogram of the number of years of education Fijian farmers have undergone. From the histogram we see that the years of education range from 0 to 18 years with the data approximately normally distributed. Most frequently education years is above 7 with the most frequent number of years being between 7 and 11 years. The least frequent years of education is above 16 years with only 4 out of the 308 sampled farmers having more than 16 years of education. QUESTION 2 Fig 2.1 Scatter plot of department store and food retail turnover. Figure 2.1 is a scatter plot of the turnovers for food retailing and department stores from 1982-2008. There is a clear upward trend indicating that as food retail turnover has risen department store turnover has also risen. The data follows a fairly distinct trend line with few outliers indicating a strong positive correlation between the two variables. QUESTION 3 Fig 3.1 Pareto chart for the state-wide production of milk in 2007-08. Figure 3.1 is a pareto chart constructed from the milk production per state in 2007-08. The pareto chart shows that Victoria yields the highest production of milk with over 60% of milk produced in 2007-08 coming from Victoria. The state with the lowest production of milk is Tasmania contributing only approximately 3% of to the total milk production...
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...Additional information, including supplemental material and rights and permission policies, is available at http://ite.pubs.informs.org. Vol. 9, No. 1, September 2008, pp. 1–9 issn 1532-0545 08 0901 0001 informs ® doi 10.1287/ited.1080.0014 © 2008 INFORMS INFORMS Transactions on Education Using Simulation to Model Customer Behavior in the Context of Customer Lifetime Value Estimation Shahid Ansari, Alfred J. Nanni Accounting and Law Division, Babson College, Wellesley, Massachusetts 02457 {sansari@babson.edu, nanni@babson.edu} Dessislava A. Pachamanova, David P. Kopcso Mathematics and Science Division, Babson College, Wellesley, Massachusetts 02457 {dpachamanova@babson.edu, kopcso@babson.edu} T his article illustrates how simulation can be used in the classroom for modeling customer behavior in the context of customer lifetime value estimation. Operations research instructors could use this exercise to introduce multiperiod spreadsheet simulation models in a business setting that is of great importance in practice, and the simulation approach to teaching this subject could be of interest also to marketing and accounting instructors. At Babson College, the spreadsheet simulation exercise is part of an integrated one-case teaching day of the marketing, accounting, and operations research disciplines in the full-time MBA program, but the exercise is directly transferable to stand-alone courses as well. In our experience, students have...
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...University of Phoenix Material Time to Practice – Week Two Complete Parts A, B, and C below. Part A 1. Why is a z score a standard score? Why can standard scores be used to compare scores from different distributions? It is a scores relationship to the mean indicating whether it is above or below the mean. It does this by converting scores to z score. Yes – keep going – just a bit more is needed.2 out of 3 pts 2. For the following set of scores, fill in the cells. The mean is 74.13 and the standard deviation is 9.98. |Raw score |Z score | |68.0 |-.6142 | |?58.16 yes! |–1.6 | |82.0 |.789 | |92.09 |1.8 | |69.0 |-.514 | |69.14 |–0.5 | |85.0 |1.089 | |91.096 |1.7 | |72.0 |-.213 no | Calculations? Always include either calculations or SPSS output. 2 out of 3 pts 3. Questions 3a through 3d are based on a distribution of scores with [pic] and the standard deviation = 6.38. Draw a small picture to help you see what is required. a. What is the probability of a score falling between a raw score of 70 and 80? .5668ok b. What is the probability of a score falling above a raw score of 80? .2166ok c. What is the probability of a score falling between a raw score of 81 and 83? .0686 close d. What is the probability of a score falling below a raw score...
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...Chapter 5, Problem 1. The equivalent model of a certain op amp is shown in Fig. 5.43. Determine: (a) the input resistance. (b) the output resistance. (c) the voltage gain in dB. 8x104vd Figure 5.43 for Prob. 5.1 Chapter 5, Solution 1. (a) (b) (c) Rin = 1.5 MΩ Rout = 60 Ω A = 8x104 Therefore AdB = 20 log 8x104 = 98.0 dB Chapter 5, Problem 2 The open-loop gain of an op amp is 100,000. Calculate the output voltage when there are inputs of +10 µV on the inverting terminal and + 20 µV on the noninverting terminal. Chapter 5, Solution 2. v0 = Avd = A(v2 - v1) = 105 (20-10) x 10-6 = 1V PROPRIETARY MATERIAL. © 2007 The McGraw-Hill Companies, Inc. All rights reserved. No part of this Manual may be displayed, reproduced or distributed in any form or by any means, without the prior written permission of the publisher, or used beyond the limited distribution to teachers and educators permitted by McGraw-Hill for their individual course preparation. If you are a student using this Manual, you are using it without permission. Chapter 5, Problem 3 Determine the output voltage when .20 µV is applied to the inverting terminal of an op amp and +30 µV to its noninverting terminal. Assume that the op amp has an open-loop gain of 200,000. Chapter 5, Solution 3. v0 = Avd = A(v2 - v1) = 2 x 105 (30 + 20) x 10-6 = 10V Chapter 5, Problem 4 The output voltage of an op amp is .4 V when the noninverting input is 1 mV. If the open-loop gain of the op amp is 2 × 106, what is the inverting...
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...through the LED. 2. Incoherent Light- Photons emitted from the junction where the p and n regions meet are not in phase, nor are they launched in the same direction. These are out of phase photons 3. Laser- Acronym for light amplification by stimulated emission of radiation. It produces a coherent source of light with a narrow beam and a narrow spectral bandwidth. Used to provide the high-powered, tightly controlled light wavelengths necessary for high-speed, long-distance optical fiber transmissions. 4. Output Pattern- The output pattern or NA of the light source directly relates to the energy coupled into the core of the optical fiber. The output pattern of a laser light source is very narrow, allowing a majority of the light energy to be coupled into the core of a single-mode or multimode optical fiber. 5. Output Power- Optical output power levels are normally expressed as the amount of light coupled into a one meter optical fiber. Laser output power varies depending on the application. 6. Modulation Speed- Light Emitting Transistors are responsible for the modulation speed. Modulation speed of 4.3 gigahertz was reached in 2009. 7. Core Diameter Mismatch- When 2 different cores are 2 different circumferences. 8. Current- The flow of electrons in a conductor. 9. PIN Photodiode- works like a PN photodiode; however, it is manufactured to offer better performance. The better performance comes in the form of improved efficiency and greater speed. ...
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