...George Alan O'Dowd at Barnehurst Hospital in Bexley, Kent on 14 June 1961, to Gerald and Dinah O'Dowd, who were originally from Thurles, County Tipperary in Ireland. He is one of six children. His siblings are Richard, Kevin, David, Gerald, and Siobhan. He was a follower of the New Romantic movement which was popular in Britain in the early 1980s. George and his friend Marilyn were regulars at The Blitz, a trendy London nightclub run by Steve Strange of the group Visage. George and Marilyn also worked at the nightclub as cloakroom attendants. Boy George's androgynous style of dressing caught the attention of music executive Malcolm McLaren, who arranged for George to perform with the group Bow Wow Wow, featuring Annabella Lwin. Boy George's tenure with Bow Wow Wow proved quite popular, much to the dismay of Lwin, the group's actual lead singer. His association ended soon afterwards and he started his own group with bassist Mikey Craig. Next came Jon Moss (who had drumming stints with The Damned and Adam and the Ants), and then Roy Hay. The group called themselves In Praise of Lemmings, but the name was later abandoned, and they settled on the name Culture Club The band recorded demos that were paid for by EMI Records but the label declined to sign them. Virgin Records expressed interest in signing the group in the UK, while Epic Records would handle the US distribution. They recorded their debut album Kissing to Be Clever and it was released in 1982. The single...
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...This is Pow Wow He stood among the dancers in the grass arena, still and poised, ready to outperform his competition. Finally, the loudspeakers rang out with the beat of the drum, setting the dancers into motion for the last time that year. Slowly, they began to come alive quickening their pace as the singers cried out their song. His steps were perfect, each one placed with meaning, precisely timed with the beat. His feathers bobbed up and down, echoing his movements. Beads of sweat streamed down his painted face and caught at the end of his nose before being thrown onto the moccasin-beaten grass. His bells and headdress shook with each step, the red and yellow colors of his regalia blurring as he spun. His heart raced as the song reached its peak, his hands wet with nervous sweat. He timed his steps, concentrated on the beat, and took a deep breath, preparing for the move that would bring him victory: a complete and perfect handspring. As his feet came down over his body, thousands of Indians around the arena caught their breath. He pretended not to notice, continuing to pound his moccasins into the ground in rhythm with the drum. As the last beat rang, he froze his body in the stance of a warrior, posing as still as he had before the song began. His chest heaved and sweat poured down his broad, smiling face. I joined my family and the crowd in cheering for him, proud to be his niece. Dancers like him and moments like these are what keep our culture alive. This is why I love...
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...| An Introduction to Cribbing Isomophs, Gaussian Elimination and The Hidden Markov Model | | Abstract While looking into cryptography and the building blocks that make up ciphers and theory, a mix of time and effort has produced concrete methods of cryptanalysis to identify the temporal pattern recognitions and algorithms necessary to decrypt cipher-text back to its plaintext root. This paper will look at the process of cribbing isomorphs to reveal the plaintext message, Gaussian Elimination and the process of back substitution, and the Hidden Markov Model to view visible output to that which was once hidden. Table of Contents Introduction 2 Cribbing Isomorphs 3 The Hidden Markov Model 4 Gaussian Elimination 5 Conclusion 6 Introduction In any cryptanalysts toolbox, there are a number of methods at their dispense which can aid in the deciphering of crypto-text messages back into their native plaintext message. Since the dawn of man, ways have been invented to hide secret information in an attempt to keep secret an intent, hide a plan, cover up a bad deed or whisper softly over distances. Encryption has proven the means to get this data over a medium and ensure that the integrity of the message arrives intact. Many times this information is intercepted and then the deciphering process begins. By knowing a certain amount about a message, cryptanalysts are able to piece the remaining message together by using cribbing, algorithms and back substitution...
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...JET Copies 1 JET Copies Dr.Robert Joseph Strayer University Cheryl Barnett JET Copies 2 James Banks had driven form Southgate to a Klecko's Copy Center. While waiting in line, he talk to Robin. They notice that a lot of the customers were students from Southgate Apartment or other apartments that were close. They felt it was a waste of time and inconvenience to stand in line to make copies. Klecko's should move it operation near the State University since most of the customers in line was from the University. James Banks got an idea how to make some money by starting a copy business. James Banks, Ernie Moore and Terrie Jones are friends that liver at the Southgate Apartments. They first purchase a reliability copier and name the business JET Copies. Ernie Moore did some research on the University copy machine, since the one that they purchase was similar to the University copy. He found out the University copy broke down frequently and how long it took between the days to repaired. This may cause them to lose revenue. James, Ernie, and Terri purchase a smaller backup copies to use when the main copier brake down. A simulation mode was develop to know how frequently the copier might break down and the time ...
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...Bibliography III Taylor, B. W. (2011). Introduction to Management Science. Upper Saddle River: Prentice Hall. Assignment Week 3 Samuel Leonard Blake Introduction to Management Science MAT 540 Dr. Simona Barb April 28, 2013 (III Taylor, 2011) Assignment Week 3 Brief Description JET Copies was created to solve a problem of resource scarcity, James, Ernie, and Terri all students were having challenges trying to get their copies. They were traveling to another building outside of theirs and several students were using the same copier. James thought they could make a lot of money if they purchased a copier and charged students 0.10 cents a copy. So James, Ernie, and Terri borrowed $18,000.00 dollars from their parents and went into business. Simulation Estimate I took the example from page 647 in our text and used it as a template to create my model. I used the discrete distribution table shown on page 679 to gather my information that stipulated repair times in days with the corresponding probability. The probability distribution was located on page 679 as well and gave a 0 to 6 week timeframe between break downs. I used the continuous probability function in excel to show the values. To compute the loss of revenue over 1 year I calculated all values then divided them by the cumulative total divided by 52 weeks for an average annual cost of $ 58,231 dollars. Case Problem Question Should JET buy a backup copier for $ 8000...
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...given to the weighted average time until cash flows are received, and is measured in years. Modified duration is the name given to the price sensitivity and is the percentage change in price for a unit change in yield. When yields are continuously compounded Macaulay duration and modified duration will be numerically equal. When yields are periodically compounded Macaulay and modified duration will differ slightly, and there is a simple relation between the two. Stochastic modeling "Stochastic" means being or having a random variable. A stochastic model is a tool for estimating probability distributions of potential outcomes by allowing for random variation in one or more inputs over time. The random variation is usually based on fluctuations observed in historical data for a selected period using standard time-series techniques. Distributions of potential outcomes are derived from a large number of simulations (stochastic projections) which reflect the random variation in the input(s). Maximum Likelihood Function In statistics, maximum-likelihood estimation (MLE) is a method of estimating the parameters of a statistical model. When applied to a data set and given a statistical model, maximum-likelihood estimation provides estimates for the model's...
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...Indian Institute of Management Bangalore PGP 2009-10 Quantitative Methods I Mid Term Examination Time: 2 hours 30 minutes Name:____________________________ Maximum Marks: 50 Roll. No.________________ Section____ Question No. | 1 | 2 | 3 | 4 | 5 | Total | Maximum Marks | 3+2+2+2+4=13 | 2+3+4=9 | 2+2*1+2*1.5+4=11 | 3+6+2=11 | 3+3=6 | 50 | Student’s Score | | | | | | | Instructions: This is an open-book (1 text-book), open note exam; however you are not allowed to share material with other students. Use of calculator is permitted, but not computer (laptop). Please do not seek any clarifications. To get any credit, you must * circle/clearly indicate your final answer (in the space, whenever provided); * answer all questions in the space provided, * State any assumptions that you make. Assumptions made should be reasonable. * Show calculations and provide reasons to support your answers. Do not attach any additional sheets; use the back sides, if necessary. 1. A highway restaurant is trying to plan its capacity. It finds that on average during the peak lunch hour, which is between 1pm and 2 pm, vehicles arrive at a rate of 1 vehicle per 6 minutes. (Each vehicle on average has four customers, and they can all sit on one table.) To avoid incurring a loss on a particular day, there should be at least 6 vehicles arriving during the peak lunch hour period. (a) What is the probability of the restaurant incurring...
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...Introduction Background of the study It is a common issue to test whether the stock prices are efficient, in most of the researches. It is found that long term returns are more predictable than short term and thus mean reverting. Studies mainly focus on autocorrelation of returns in different time lengths. In finance theory it says when autocorrelation of stock prices is there, then prices are predictable. Therefore, it is evidence against the random walk hypothesis of stock prices. Objective of the study The objective of this research is to test the efficiency of stock prices in Sri Lanka studying autocorrelation of short and long term returns in the CSE. Significance of the study There was a previous research on the predictability of short term returns and mean reversion in long term return in Sri Lanka in two important ways. 1. Previous studies utilized only shorter time periods because lack of long time series, since Sri Lankan stock market commenced only in 1985 and some researchers only used limited data which is the shorter time series available. Whereas the present study uses all-time series of returns available as at the research date (18.5 years from 1985-2003). 2. All previous papers tested only the short term returns except one. But this study examines short term predictability as well as long term mean reversion. It is important to use a long time series when predicting the long term returns to obtain correct results. Therefore, this...
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...Notation Guide Introduction Throughout the semester, you will see a wide variety of symbols used repeatedly from week to week. These symbols are usually defined when the text introduces them, but it is sometimes hard to remember exactly what symbols/operators mean what. Here are two handy reference tables to keep them straight. This list is not exhaustive, rather it is designed to include symbols that are used often, not just as a "one-off." Also note that in some occasions, the text will sometimes use certain symbols/variables for purposes other than the ones listed here. For example, π usually refers to the population proportion and r usually refers to the coefficient of correlation. However, on occasions the textbook will use π to refer to the numerical constant 3.1415926... and in chapter 11, r is used as the number of factors in a 2 way ANOVA. You should ensure that the usage in the text matches the description given here. The first table contains symbols, the second operators. In this first table, the first column contains the symbol itself and the second column a brief definition of what that symbol refers to. In cases where there is a particular equation defined by the symbol, a page reference can be found in the third column. Some of the symbols rely upon having a browser that will render HTML 4.0 and a unicode font installed on your computer, so if you have trouble seeing any of the symbols you may need to switch browsers or update your browser. Symbol | Meaning/Definition...
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...Case Problem 1 If you assume that the number of days needed to repair a copier is random, you can generate a random number using the Excel RAND function which I denoted r2 between 0 and 1. If 0 < r2 < 0.2 then it takes 1 day 0.2 < r2 < 0.65 then it takes 2 days 0.65 < r2 < 0.90 then it takes 3 days 0.9 < r2 < 1 then it takes 4 days 2 The probability distribution of the random variable varies between the times of 0 to 6 weeks, with the probability increasing as time goes on. This can be approximated by the function f(x) = x/18 for 0 < x < 6 Therefore, the distribution function is f(x) = x-square divided by 36 for 0 < x < 6 If we set this equal to another random number r1 that is between 0 and 1 then R1 = x-square divided by 36 which results to x = 6√ r1 3 Since the number of copies sold per day is a uniform probability distribution between 2000 to 8000 copies, I made r3 a random number between 2000 to 8000. To get the amount of business lost on a particular day, I take r3 X (repair time), and the lost revenue is then equal to 0.1 X r3 X repair time, since they charge $0.10 per copy. 4 After running the simulation, I got the following results: Repair Distribution P(x) Cumulative Repair time 0.2 0.000 1.000 0.45 0.200 2.000 0.25 0.065 3.000 0.1 0.900 4.000 Break Down Random R1 Time b/w Breakdowns (weeks) Cumulative Time Random R2 Repair Time Random R3 Revenue Lost 1 0.438236 3.971962 3.972 0.654621 3 6816 2044.8 2 0.284183 3.198529...
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...1.10 Sampling Distributions The main objective of most statistical inference is to draw conclusion about the population parameters based on samples studies that is quite small in comparison to the size of the population. In order that conclusion of sampling theory and statistical inference valid, samples must be chosen so as to the representation of a population. For example, Television executives want to know the proportion of television viewers who watch that network’s program. Particularly determining the proportions that are watching certain programs is impractical and prohibitively expensive. One possible alternative method can be providing approximation by observing what a sample of 1,000 television viewer’s watch. Thus they estimate the population proportion by calculating the sample proportion. Similarly, the field of quality control illustrates yet another reason for sampling. In order to ensure that a production process is operating properly, the operations manager needs to know the proportion of defective units that are being produced. If the quality-control technician must destroy the unit in order to determine whether or not it is defective, there is no alternative to sampling: a complete inspection of the population would destroy the entire output of the production process. We know that the sample proportion of television viewers or of defective items is probably not exactly equal to the population proportion we want it to estimate. Nonetheless, the sample statistic...
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... mean (average) Variance 2 probability density function 1 x 2 1 exp f x 2 2 cumulative density function 1 t 2 1 F x dt exp 2 2 Standard Normal Density X ~ N 0,1 probability density function n x cumulative density function x N x 1 1 exp x 2 2 2 x important result: standardization 1 exp t 2 dt 2 2 1 if X~N , 2 and Z= then Z~N 0,1 X- 1 Mathematical Expectation: Given a random variable X and its pdf f x we define the expectation of the function g X to be the integral E g X g x f x dx Note that g X is also a random variable The Moment Generating Function (MGF) The MGF of a random variable X is a function of t denoted by M X t E e xt which is an expectation MGF of normal If X ~ N , 2 1 x 1 Xt xt Then M X t E e e e 2 2 Lognormal Distribution: 2 1 t 2t 2 dx e 2 Y has the lognormal distribution with parameters , 2 if: its logarithm is normally distributed X log e Y ~ N , 2 . This in turn means that Y e X 2 The cumulative density function of Y is log e y FY y Pr Y y N x 12 1 2t where N x ...
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...Statistics for Business [pic] Discrete and Continuous Probability Distributions Business Statistics With Canadian Applications Hummelbrunner Rak Gray Third Edition Week6 Pages 261-263 chapter 8 Pages 288-314, 320-325 chapter 9 Arranged by: Neiloufar Aminneia Probability distribution A probability distribution is a list of all events of an experiment together with the probability associated with each event in a tabular form. It is used for business and economic problems. We learned frequency distribution to classify data, relating to actual observations and experiments but probability distribution describes how outcomes are expected to vary. Probability distribution for rolling a true die x P(x) Events Frequencies Probability 1 1 1/6 2 1 1/6 3 1 1/6 4 1 1/6 5 1 1/6 6 1 1/6 Total: 1 [pic] Probability distribution for tossing 3 coins x P(x) Events(# of Heads) Frequencies Probability 0 1 (TTT))...
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...number of times that a copier machine would break down over a year’s time, the average repair time days, and lastly the estimated loss of revenue that occurred when the machine broke down each time. The owners of the company needed this information to make a decision on whether or not to purchase a backup copier. I decided to start with the table on page 679, which identified the probability of the days that it took the copier to get fixed each time that it broke down. I calculated the cumulative probability before beginning my simulation. I figured that in a year’s time with a max of six weeks between breakdowns according to the information given, that the copier would break down approximately 15 times. In Excel, I created fifteen random numbers (RN1) based upon my cumulative probability and probability distribution given. After which, I determined an average number of repair days for each break down which calculated to be about 2 days. The second step taken to begin this simulation was to determine the frequency of how often the copier would break down. The probability given was based upon speaking with staff members from the college of business and notating that the probability of the break downs increased the longer the copier went without breaking down. In looking at the visual provided on page 679, I was able to use the formula y=mx+b in order to find the probability for each week, given at week six, 0.33. I first found the slope using rise over run 0.33/6 which...
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...mathematical expression which gives the sum of four rolls of a die. To do this, we could let Xi , i = 1, 2, 3, 4, represent the values of the outcomes of the four rolls, and then we could write the expression X 1 + X 2 + X 3 + X4 for the sum of the four rolls. The Xi ’s are called random variables. A random variable is simply an expression whose value is the outcome of a particular experiment. Just as in the case of other types of variables in mathematics, random variables can take on different values. Let X be the random variable which represents the roll of one die. We shall assign probabilities to the possible outcomes of this experiment. We do this by assigning to each outcome ωj a nonnegative number m(ωj ) in such a way that m(ω1 ) + m(ω2 ) + · · · + m(ω6 ) = 1 . The function m(ωj ) is called the distribution function of the random variable X. For the case of the roll of the die we would assign equal probabilities or probabilities 1/6 to each of the outcomes. With this assignment of probabilities, one could write P (X ≤ 4) = 1 2 3 2 CHAPTER 1. DISCRETE PROBABILITY DISTRIBUTIONS to mean that the probability is 2/3 that a roll of a die will have a value which does not exceed 4. Let Y be the random variable which represents the toss of a coin. In this case, there are two possible outcomes, which we can label as H and T. Unless we have reason...
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