... 1. What is xkcd? 2. What would happen at the equator if Earth stopped moving and its atmosphere did not ? 3. If the Earth lost its spin, would it ever get it back? 4. If you were to pitch a baseball at 90% the speed of light, would the ball slow down during its course? 5. Assuming you are a good swimmer, how long will it take you to blackout from fatigue in a pool? 6. Who were the inhabitants of the now Time Squares 1000 years ago? 7. What is the predecessor of the supercontinent Pangea? 8. What would hypothetically be the ratio of found soulmates if everyone would have one predetermined soulmate in the world? 9. How much energy does the Sun provide to the Moon (in light)? 10. What would happen if you were to fire the “confinement beam” towards the sky? 11. If you were to let loose argon, what would happen to it? 12. What is the best example of a mole (measurement unit)? 13. What would threaten the integrity of the DNS system? 14. How much power does a typical hair dryer draw? 15. According to Back to the Future, how much power is required to travel back in time? 16. Which power plants can last the longest without maintenance? 17. What is the thrust-to-weight ratio of an AK-47? 18. How high can a human throw an object? 19. How much pressure can a submarine withstand? 20. If your printer was able to print $100 bills, how much money would it produce in a year? 21. What would happen if you set off a nuclear bomb in the eye of a hurricane? Would the storm be immediately vaporized...
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...Porsche Consulting – THE MAGAZINE ThREE STEPS To AN EFFECTIvE STRATEGY “Of course we have a strategy,” is the answer any business leader would give when asked whether he or she has set mid- to long-term goals. Our captain of industry may even have his or her own strategist. And the strategy can surely be read somewhere; it’s been summarized in a presentation and announced to staff. but is that enough? porsche consulting’s observations have shown that many companies’ strategies do not have the desired effects. poor implementation is the most frequent cause. “Employees, in particular those on the lower rungs of the hierarchy, are not involved enough in implementation,” says Dirk pfitzer, a partner at porsche consulting. In many cases, poor communication is at fault. And: “Resolute and continuous control quickly falls by the wayside,” adds principal Fabian piontek. porsche consulting demonstrates how to develop an effective strategy in three steps. 60 Porsche Consulting – THE MAGAZINE STeP 1: CoRPoRaTe STRaTegy The company needs to define its vision and mission for the company as a whole as well as objectives in the customer, finance, employee, and market dimensions. The product strategy and core and cross-departmental strategies are then derived from the overarching company and brand strategy. market Sales vISIon/mISSIon STRaTegIC objeCTIveS Customer Customer enthusiasm Finance Return on capital employees Top employer and partner CoRPoRaTe STRaTegy...
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...Porsche: Guarding the Old While Bringing in the New Background on Porsche The Porsche Company was founded by Ferdinand Porsche who credited himself for the design of the original Volkswagen Beetle and Adolf Hitler’s people’s car. He had already gathered over 30 years of valuable experience before designing the Porsche. The first result of this work in automobile development was an electric car called the Lohner Porsche which was powered by wheel-hub motors. In 1948 Porsche engineering office started working under its own steam on the Type 356 VW Sports Car it marked the birth of the Porsche sports car. Today the Porsche engineering continues to take on engineering challenges of the future. The Problem The Porsche Company started to decline in sales due to its’ exclusive customers. Porsche became concerned about if there were enough products to keep the company afloat. The company tried to extend its brand outside of the box with making cars that were affordable to individuals who didn’t represent the Porsche brand. What factors are important to understanding this problem? The Porsche customers were upset, because there were different classes of people who owned this product. The customers exemplified attitude toward the product. “A customer’s attitude fit into a pattern changing ones attitude may require difficult adjustments in many others” (Kotler and Armstrong). Brand personality is a unique concept with this case. “Brand personality is the specific mix of human...
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...Voice over Internet Protocol (VoIP) is a rapidly emerging technology for voice communication that uses the ubiquity of IP-based networks to deploy VoIP client devices—such as desktop IP phones, mobile VoIP-enabled handheld devices, and VoIP gateways—in an increasing number of businesses and homes around the world. Windows CE 5.0 is a robust, real-time operating system platform that enables original device manufacturers (ODMs), original equipment manufacturers (OEMs), service providers (such as Internet service providers [ISPs], cable companies, and carriers), and enterprises to rapidly develop and deploy a wide range of devices that are part of an IP network and that have integrated VoIP functionality. The latest version of Windows CE includes an integrated, easy-to-use Telephony User Interface (TUI), a VoIP Application Interface Layer (VAIL) with extensive call control functionality, an interface to access contact and calendar data on Microsoft Exchange servers, advanced provisioning capabilities, and a complete network layer stack that facilitates VoIP-enabled device development and infrastructure integration. The information contained in this document represents the current view of Microsoft Corporation on the issues discussed as of the date of publication. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information presented after the...
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...Big as it is, Canepa Design is easy to miss. The discreet boxy building sits just off a busy street in the quiet northern California town of Scotts Valley, just up the winding highway from Santa Cruz. But for car lovers, this place beams like St. Peter’s, an inviting treasure chest stuffed with classic automobiles worthy of pilgrimage. Vintage racing Porsches rub sheetmetal shoulders with iconic ‘60s Ferraris, which sit mere wheel-wells away from the last Shelby Cobra to exit the factory gates. Some vehicles are being restored for their wealthy owners, others are being spiffed up to hit Canepa Design’s showroom, while a few enjoy some mechanical pampering before being returned to their places of honor upstairs in the on-site motorsports museum. “I never get tired of coming to work,” says Bruce Canepa, the racing driver who since 1980 — the heyday of his professional exploits behind the wheel of all manner of Porsche beasts — has quietly turned Canepa Design into one of the foremost auto restoration and classic car sales shops in the nation. “Besides, I’m too obsessed with being in control of all the details to stop coming in.” Obsession and control can be a dangerous cocktail. But not in Canepa’s case. His hands-on personality means the cars coming out of this 70,000-square-foot shop often exceed the exacting standards of his monied clientele. When Canepa leans over the exposed engine bay of the aforementioned 1967 Cobra 427, he points out that “everything on this car...
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...Unit 2 DB Subjective Probability “ A probability derived from an individual's personal judgment about whether a specific outcome is likely to occur. Subjective probabilities contain no formal calculations and only reflect the subject's opinions and past experience.” (investopedia.com, 2013) There are three elements of a probability which combine to equal a result. There is the experiment ,the sample space and the event (Editorial board, 2012). In this case the class is the experiment because the process of attempting it will result in a grade which could vary from an A to F. The different grades that can be achieved in the class are the sample space. The event or outcome is the grade that will be received at the end of the experiment. I would like to achieve an “A” in this class but due to my lack of experience in statistical analysis, my hesitation towards advanced mathematics, and the length of time it takes for me to complete my course work a C in this class may be my best result. I have a 1/9 chance or probability to receive an “A” in the data range presented to me which is (A,A-,B,B-,C,C-,D,D- AND F). By the grades that have been posted I would say that the other students have a much better chance of receiving a better grade than mine. I have personally use subjective probability in my security guard business in bidding on contracts based on the clients involved , the rates that I charge versus the rates other companies charge and the amount of work involved...
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... Probability – the chance that an uncertain event will occur (always between 0 and 1) Impossible Event – an event that has no chance of occurring (probability = 0) Certain Event – an event that is sure to occur (probability = 1) Assessing Probability probability of occurrence= probability of occurrence based on a combination of an individual’s past experience, personal opinion, and analysis of a particular situation Events Simple event An event described by a single characteristic Joint event An event described by two or more characteristics Complement of an event A , All events that are not part of event A The Sample Space is the collection of all possible events Simple Probability refers to the probability of a simple event. Joint Probability refers to the probability of an occurrence of two or more events. ex. P(Jan. and Wed.) Mutually exclusive events is the Events that cannot occur simultaneously Example: Randomly choosing a day from 2010 A = day in January; B = day in February Events A and B are mutually exclusive Collectively exhaustive events One of the events must occur the set of events covers the entire sample space Computing Joint and Marginal Probabilities The probability of a joint event, A and B: Computing a marginal (or simple) probability: Probability is the numerical measure of the likelihood that an event will occur The probability of any event must be between 0 and 1, inclusively The sum of the...
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...= {-20, -19, …, -1, 0, 1, …, 19, 20} Number of people arriving at a bank in a day: S = {0, 1, 2, …} Inspection of parts till one defective part is found: S = {d, gd, ggd, gggd, …} Temperature of a place with a knowledge that it ranges between 10 degrees and 50 degrees: S = {any value between 10 to 50} Speed of a train at a given time, with no other additional information: S = {any value between 0 to infinity} 4 Sample Space (cont…) Discrete sample space: One that contains either finite or countable infinite set of outcomes • Out of the previous examples, which ones are discrete sample spaces??? Continuous sample space: One that contains an interval of real numbers. The interval can be either finite or infinite 5 Events A collection of certain sample points A subset of the sample space Denoted by ‘E’ Examples: • Getting an odd number in dice throwing experiment S = {1, 2, 3, 4,...
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...the stage where one can begin to use probabilistic ideas in statistical inference and modelling, and the study of stochastic processes. Probability axioms. Conditional probability and independence. Discrete random variables and their distributions. Continuous distributions. Joint distributions. Independence. Expectations. Mean, variance, covariance, correlation. Limiting distributions. The syllabus is as follows: 1. Basic notions of probability. Sample spaces, events, relative frequency, probability axioms. 2. Finite sample spaces. Methods of enumeration. Combinatorial probability. 3. Conditional probability. Theorem of total probability. Bayes theorem. 4. Independence of two events. Mutual independence of n events. Sampling with and without replacement. 5. Random variables. Univariate distributions - discrete, continuous, mixed. Standard distributions - hypergeometric, binomial, geometric, Poisson, uniform, normal, exponential. Probability mass function, density function, distribution function. Probabilities of events in terms of random variables. 6. Transformations of a single random variable. Mean, variance, median, quantiles. 7. Joint distribution of two random variables. Marginal and conditional distributions. Independence. iii iv 8. Covariance, correlation. Means and variances of linear functions of random variables. 9. Limiting distributions in the Binomial case. These course notes explain the naterial in the syllabus. They have been “fieldtested” on the class of 2000...
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...[pic] [pic] Markov Chain [pic] Bonus Malus Model [pic] [pic] This table justifies the matrix above: | | | |Next state | | | |State |Premium |0 Claims |1 Claim |2 Claims |[pic]Claims | |1 | |1 |2 |3 |4 | |2 | |1 |3 |4 |4 | |3 | |2 |4 |4 |4 | |4 | |3 |4 |4 |4 | | | | | | | | |P11 |P12 |P13 |P14 | | | |P21 |P22 |P23 |P24 | | | |P31 |P32 |P33 |P34 | | | |P41 |P42 |P43 |P44 | | | | ...
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...Permutations The word ‘coincidence’ is defined as an event that might have been arranged though it was accidental in actuality. Most of us perceive life as a set of coincidences that lead us to pre-destined conclusions despite believing in a being who is free from the shackles of time and space. The question is that a being, for whom time and space would be nothing more than two more dimensions, wouldn’t it be rather disparaging to throw events out randomly and witness how the history unfolds (as a mere spectator)? Did He really arrange the events such that there is nothing accidental about their occurrence? Or are all the lives of all the living beings merely a result of a set of events that unfolded one after another without there being a chronological order? To arrive at satisfactory answers to above questions we must steer this discourse towards the concept of conditional probability. That is the chance of something to happen given that an event has already happened. Though, the prior event need not to be related to the succeeding one but must be essential for it occurrence. Our minds as I believe are evolved enough to analyze a story and identify the point in time where the story has originated or the set of events that must have happened to ensure the specific conclusion of the story. To simplify the conundrum let us assume a hypothetical scenario where a man just became a pioneer in the field of actuarial science. Imagine him telling us his story in reverse. “I became...
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...presence with probability 0.99. If it is not present, the radar falsely registers an aircraft presence with probability 0.10. We assume that an aircraft is present with probability 0.05. What is the probability of false alarm (a false indication of aircraft presence), and the probability of missed detection (nothing registers, even though an aircraft is present)? A sequential representation of the sample space is appropriate here, as shown in Fig. 1. Figure 1: Sequential description of the sample space for the radar detection problem Solution: Let A and B be the events A={an aircraft is present}, B={the radar registers an aircraft presence}, and consider also their complements Ac={an aircraft is not present}, Bc={the radar does not register an aircraft presence}. The given probabilities are recorded along the corresponding branches of the tree describing the sample space, as shown in Fig. 1. Each event of interest corresponds to a leaf of the tree and its probability is equal to the product of the probabilities associated with the branches in a path from the root to the corresponding leaf. The desired probabilities of false alarm and missed detection are P(false alarm)=P(Ac∩B)=P(Ac)P(B|Ac)=0.95∙0.10=0.095, P(missed detection)=P(A∩Bc)=P(A)P(Bc|A)=0.05∙0.01=0.0005. Application of Bayes` rule in this problem. We are given that P(A)=0.05, P(B|A)=0.99, P(B|Ac)=0.1. Applying Bayes’ rule, with A1=A and A2=Ac, we obtain P(aircraft present | radar registers) =...
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...1.M/G/ Queue a. Show that Let A(t) : Number of arrivals between time (0, t] “ n should be equal to or great than k” since if n is less than k (n<k), Pk(t)=0 Let’s think some customer C, Let’s find P{C arrived at time x and in service at time t | x=(0,t)] } P{C arrives in (x, x+dx] | C arrives in (0, t] }P{C is in service | C arrives at x, and x = (0,t] } Since theorem of Poisson Process, The theorem is that Given that N(t) =n, the n arrival times S1, S2, …Sn have the same distribution as the order statistics corresponding to n independent random variables uniformly distributed on the interval (0, t) Thus, P{C is in service | C arrives between time (0, t] } Since let y=t-x, x=0 → y=t, x=t →y=o, dy=-dx Therefore, In conclusion, ------ (1) 1-a Solution Since b. let 1-b Solution ------------------------------------------------- 2. notation Page 147 in “Fundamentals of Queuing Theory –Third Edition- , Donald Gross Carl M. Harris a. b. ------------------------------------------------- ------------------------------------------------- ------------------------------------------------- 3. a. let X=service time (Random variable) and XT=total service time (Random variable) X2=X+X, X3=X+X+X, ….. f2(x2)...
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...Probability & Mathematical Statistics | “The frequency concept of Probability” | [Type the author name] | What is probability & Mathematical Statistics? It is the mathematical machinery necessary to answer questions about uncertain events. Where scientists, engineers and so forth need to make results and findings to these uncertain events precise... Random experiment “A random experiment is an experiment, trial, or observation that can be repeated numerous times under the same conditions... It must in no way be affected by any previous outcome and cannot be predicted with certainty.” i.e. it is uncertain (we don’t know ahead of time what the answer will be) and repeatable (ideally).The sample space is the set containing all possible outcomes from a random experiment. Often called S. (In set theory this is usually called U, but it’s the same thing) Discrete probability Finite Probability This is where there are only finitely many possible outcomes. Moreover, many of these outcomes will mostly be where all the outcomes are equally likely, that is, uniform finite probability. An example of such a thing is where a fair cubical die is tossed. It will come up with one of the six outcomes 1, 2, 3, 4, 5, or 6, and each with the same probability. Another example is where a fair coin is flipped. It will come up with one of the two outcomes H or T. Terminology and notation. We’ll call the tossing of a die a trial or an experiment. Where we...
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...Model Answers for Chapter 4: Evaluating Classification and Predictive Performance Answer to 4.3.a: Leftmost bar: If we take the 10% "most probable 1’s(frauds)” (as ranked by the model), it will yield 6.5 times as many 1’s (frauds), as would a random selection of 10% of the records. 2nd bar from left: If we take the second highest decile (10%) of records that are ranked by the model as “the most probable 1’s (frauds ” it will yield 2.7 times as many 1’s (frauds), as would a random selection of 10 % of the records. Answer to 4.3.b: Consider a tax authority that wants to allocate their resources for investigating firms that are most likely to submit fraudulent tax returns. Suppose that there are resources for auditing only 10% of firms. Rather than taking a random sample, they can select the top 10% of firms that are predicted to be most likely to report fraudulently (according to the decile chart). Or, to preserve the principle that anyone might be audited, they can establish differential probabilities for being sampled -- those in the top deciles being much more likely to be audited. . Answer to 4.3.c: Classification Confusion Matrix Predicted Class 1 (Fraudulent) Actual Class 1 (Fraudulent) 0 (Non-fraudulent) Error rate = 0 (Non-fraudulent) 30 58 32 920 n0,1 + n1,0 32 + 58 = = 0.0865 = 8.65% n 1040 Our classification confusion matrix becomes Classification Confusion Matrix Predicted Class 1 (Fraudulent) ...
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