...Business I. The essential elements of a contract and the applicable remedies in the event of a breach. Four Essential Elements of a Contract An agreement must contain four essential elements to be regarded as a contract. If any one of them is missing, the agreement will not be legally binding. a. Offer There must be a definite, clearly stated offer to do something. For example: A quotation by sub-contractor to the main contractor and an offer to lease. An offer does not include ball park estimates, requests for proposals, expressions of interest, or letters of intent. An offer will lapse: * when the time for acceptance expires * if the offer is withdrawn before it is accepted * After a reasonable time in the circumstances (generally the greater the value of the contract, the longer the life of the offer). Invitation to treatAn invitation to treat is a mere declaration of willingness to enter into negotiations; it is not an offer, and cannot be accepted so as to form a binding contract. An agreement is not created if there is an acceptance of the invitation to treat.An invitation to treat is part of the preliminaries of negotiation, whereas an offer is legally binding once accepted, subject to compliance with the terms of the offer. For example: Invitations to treat are advertisements, price lists, circulars and catalogues. | b. Acceptance Only what is offered can be accepted. This means that the offer must be accepted exactly as offered without...
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...UNIT 14: HOSPITALITY CONTRACT AND EVENT MANAGEMENT Get assignment help for this unit at assignmenthelpuk@yahoo.com LO1 Understand external factors that affect planning and management in the event and contract sectors Diversity of sector: employee catering; hospital catering; school meals; conference centres; location and outdoor events; banqueting; private functions Types of service provision: food and beverage services; accommodation services; reception; facilities management; linen and laundry; cleaning; administration; hotel services; maintenance; security; purchasing; human resource services Component elements of the contract/event: menu design; food and beverage service style; staffing; timing; space layout; decoration; entertainment; lighting and sound External factors: socio-cultural; economic; political; technological; environmental; legal LO2 Understand the operational issues which affect the success of event management Elements of project management: action planning; product knowledge; decision-making; scheduling; administration; client liaison; component elements of the event; liaison with internal/external providers (executive chef, restaurant/bar manager, HR manager, front office, AV technician, florist, artiste/agent) Food and beverage systems: suitability of menu design; type of food service system for a particular contract and event catering situation; suitability of purchasing; delivering and storage systems Marketing and sales issues: product placement;...
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...Task 1 answer Diversity of sectors Hospitality industry includes a range of businesses that relates to provision of products and services. It is a multi-billion dollar industry that depends on leisure and disposable income. There are several sectors under the term hospitality industry which are as the following: 1. Hotels 2. Restaurants 3. Contract services 4. Pubs, Bars, casinos and nightclubs 5. Event A hotel is a place that has rooms in which people can stay when they are traveling where they can get food, shelter and other services. Hotels are divided into several categories such as boutique hotel, business hotel and motels. It is also classified into different star-rating, usually from one to five stars when one star hotel is much worse than a 5 star hotel. For example, Hilton hotel is a five-star hotel, and Euston Square Hotel is a three-star hotel (https://www.expedia.co.uk/3Star-London-Hotels.s30-0-d178279.Travel-Guide-Filter-Hotels) . These hotels are classified according to the different level of quality of service and products that each brand offers. However star-rating is different in every country. (http://www.businessdictionary.com/definition/hotel.html#ixzz46jZ6gtGl) Restaurants A restaurant is a place where people pay and sit to eat food that are cook and served. http://www.merriam-webster.com/dictionary/restaurant Restaurants are also classified into several groups as the following: 1. Fast food 2. Fast casual 3. Fine dining ...
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...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 to fulfill the agreement. I currently provided security guard services in 11 Asian owned shopping centers and with right elements in place will gain...
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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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...Memorandum To: CC: From: Date: Re: The Cincinnati Enquirer Kristen DelGuzzi Ashley N. Ear; September 16, 2007 Data Analysis of Hamilton County Judges Probabilities used to assist with Ranking of Hamilton County Judges After the current statistics were gathered to produce data analysis regarding Hamilton County Judges, we can come to a conclusion and rank judges appropriately by their probability to be appealed, reversed and a combination of the both. With the provided data analysis, I have included statistics to all probabilities including: total cases disposed, appealed cases, reversed cases, probability of appeal, rank by probability of appeal, probability of reversal, rank by probability of reversal, conditional probability of reversal given appeal, rank by conditional probability of reversal given appeal and overall sum of ranks. The judges that rank the highest (i.e. 1st, 2nd, 3rd) have the lowest probability to have appealed cases, reversed cases and lowest conditional probability of reversed cases given appeal. In my opinion, by ranking the judges as such, we can see how often their ruling is upheld, which is ultimately desirable when concerning the credibility of a judge. I have provided rankings for all three different courts including: Common Pleas Court, Domestic Relations Court and Municipal Court. These overall rankings are gathered by summing up all of the rankings by the three probability variables. I have also provided data analysis which interprets who...
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...2 Broad Study in Statistics Descriptive (mô tả) : provides simple summaries about the data collected & about the preliminary observations that have beed made. Such summaries maybe either quantitative (numerical measures) or visual (e.g. simple-to-understand graphs) e.g. Present a summary report of this year business result to management Inferential (suy luận) : are systems of procedures that can be used to draw conclusions from datasets arising from systems affected by random variation. The type of inferential statistical procedure used depends upon the type of data collected as well as the distribution of the data. The procedures are usually used to test hypotheses and establish probability. e.g. Estimate the IQ score of Kaplan students by observing a small group of students Population : e.g. A population is a collection of all individuals, objects, or measurement of interest Sample : e.g. A sample is a portion or part of the population of interst MCQ 1. The process of using sample statistics to draw conclusions about true population parameters is called Statistical inference. Keywords: inferential statistics 2. Those methods involving the collection, presentation, and characterization of a set of data in order to properly describe the various features of that set of data are called Descriptive statistics. 3. The collection characteristics of the employees of a particular firm is an example of Descriptive statistics. 4. The estimation of the population...
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