0
A complete probability distribution is always an objective listing of all possible events Since it is impossible to list all the possible outcomes from a single event probability distributions are o?
Wiki User
∙ 15y ago
Your question is not clear, but I will attempt to interpret it as best I can. When you first learn about probability, you are taught to list out the possible outcomes. If all outcomes are equally probable, then the probability is easy to calculate. Probability distributions are functions which provide probabilities of events or outcomes. A probability distribution may be discrete or continuous. The range of both must cover all possible outcomes. In the discrete distribution, the sum of probabilities must add to 1 and in the continuous distribtion, the area under the curve must sum to 1. In both the discrete and continuous distributions, a range (or domain) can be described without a listing of all possible outcomes. For example, the domain of the normal distribution (a continuous distribution is minus infinity to positive infinity. The domain for the Poisson distribution (a discrete distribution) is 0 to infinity. You will learn in math that certain series can have infinite number of terms, yet have finite results. Thus, a probability distribution can have an infinite number of events and sum to 1. For a continuous distribution, the probability of an event are stated as a range, for example, the probability of a phone call is between 4 to 10 minutes is 10% or probability of a phone call greater than 10 minutes is 60%, rather than as a single event.
Wiki User
∙ 15y ago
Add your answer:
Earn +20 pts
Development of Statistics?
Before 16001560s(published 1663) - Cardano'sLiber de ludo aleae attempts to calculate probabilities of dice throws1577- Bartolomé_de_Medina defends Catholic_probabilism, the view that in ethics one may follow a probable opinion even if the opposite is more probable17th century1654- Blaise_Pascaland Pierre_de_Fermatcreate the mathematical theory of Probability,1657- Christiaan_Huygens'sDe ratiociniis in ludo aleae is the first book on mathematical probability,1662- John_Graunt'sNatural and Political Observations Made upon the Bills of Mortality makes inferences from statistical data on deaths in London,1693- Edmund_Halleyprepares the first Mortality_tablestatistically relating death rate to age18th century1710- John_Arbuthnotargues that the constancy of the ratio of male to female births is a sign of divine providence,1713- Posthumous publication of Jacob_Bernoulli'sArs_Conjectandi, containing the first derivation of a Law_of_large_numbers,1724- Abraham_de_Moivrestudies mortality statistics and the foundation of the theory of http://wiki.answers.com/w/index.php?title=Annuitie&action=edit&redlink=1in Annuities on Lives,1733- Abraham de Moivre introduces the Normal_distributionto approximate the Binomial_distributionin probability,1739- David_Hume'sTreatise_of_Human_Natureargues that Inductive_reasoningis unjustified,1761- Thomas_Bayesproves Bayes'_theorem,1786- William_Playfair'sCommercial and Political Atlas introduces Line_chartand Bar_chartof data,19th century1801- Gausspredicts the orbit of Ceresusing a line of best fit1805- Adrien-Marie_Legendreintroduces the Method_of_least_squaresfor fitting a curve to a given set of observations,1814- Laplace'sEssai philosophique sur les probabilités defends a definition of probabilities in terms of equally possible cases, introduces Generating_functionand Laplace_transform, uses Conjugate_priorfor Exponential_families, proves an early version of the Bernstein von-Mises theorem on the asymptotic irrelevance of prior distributions on the limiting posterior distribution and the role of the Fisher_informationon asymptotically normal posterior modes.1835- Adolphe_Quetelet'sTreatise on Man introduces social science statistics and the concept of the "average man",1866- John_Venn'sLogic of Chance defends the Frequentistof probability.1883- Charles_Sanders_Peirceoutlines Frequentist_statistics, emphasizing the use of objective Randomizationin Randomized_experimentand in Sampling_(statistics). Peirce also invented an Optimal_designfor Response_surface_methodology.1880- Thorvald_N._Thielegives a mathematical analysis of Brownian_motion, introduces the Likelihood_function, and invents Cumulant.1888- Galtonintroduces the concept of Correlation20th century1900- Louis_Bachelieranalyzes Stock_pricemovements as a Stochastic_process,1908- Student's_t-distributionfor the mean of small samples published in English (following earlier derivations in German).1921- Keynes' Treatise on Probability defends a logical interpretation of probability,1928- Leonard_Henry_Caleb_Tippettand Ronald_Fisher'sintroduce Extreme_value_theory,1933- Andrey_Nikolaevich_Kolmogorovpublishes his book Basic notions of the calculus of probability (Grundbegriffe der Wahrscheinlichkeitsrechnung) which contains an Probability_axiombased on Measure_theory,1935- Ronald_Fisher'sDesign of Experiments (1st ed),1937- Jerzy_Neymanintroduces the concept of Confidence_intervalin statistical testing,1946- Cox's_theoremderives the axioms of probability from simple logical assumptions,1948- Claude_Shannon'sMathematical_Theory_of_Communicationdefines capacity of communication channels in terms of probabilities,1953- Nicholas_Metropolisintroduces the idea of thermodynamic Simulated_annealingmethods21st Century2003- E.T._Jaynes'sProbability Theory: The Logic of Science defends probability theory as a comprehensive method of evaluation of evidence2009- David_X._Li'sCopulaformula blamed as one cause of the Late_2000s_recession
What are the Tools and Techniques used for Qualitative Risk Analysis?
Prioritizing risks based on their probability of occurrence and their impact if they do occur is the central goal of qualitative risk analysis. Accordingly, most of the tools and techniques used involve estimating probability and impact.Risk probability and impact assessment - Risk probability refers to the likelihood that a risk will occur, and impact refers to the effect the risk will have on a project objective if it occurs. The probability for each risk and the impact of each risk on project objectives, such as cost, quality, scope, and time, must be assessed. Note that probability and impact are assessed for each identified risk.Methods used in making the probability and impact assessment include holding meetings, interviewing, considering expert judgment, and using an information base from previous projects.A risk with a high probability might have a very low impact, and a risk with a low probability might have a very high impact. To prioritize the risks, you need to look at both probability and impact.Assessment of the risk data quality - Qualitative risk analysis is performed to analyze the risk data to prioritize risks. However, before you do it, you must examine the risk data for its quality, which is crucial because the credibility of the results of qualitative risk analysis depend upon the quality of the risk data. If the quality of the risk data is found to be unacceptable, you might decide to gather better quality data. The technique to assess the risk data quality involves examining the accuracy, reliability, and integrity of the data and also examining how good that data is relevant to the specific risk and project for which it is being used.Risk urgency assessment - This is a risk prioritization technique based on time urgency. For example, a risk that is going to occur now is more urgent to address than a risk that might occur a few months from now.Probability and impact matrix - Risks need to be prioritized for quantitative analysis, response planning, or both. The prioritization can be performed by using a probability and impact matrix; a lookup table that can be used to rate a risk based on where it falls both on the probability scale and on the impact scale.Look at the table below: RXY, where X and Y are integers that represent risks in the two-dimensional (probability and impact) space.ProbabilityImpact0.000.050.150.250.350.450.550.650.750.900.20R11R12R13R14R15R16R17R18R190.40R21R22R23R24R25R26R27R28R290.60R31R32R33R34R35R36R37R38R390.80R41R42R43R44R45R46R47R48R49This is how you read this matrix. R21 has a 40% probability of occurring and will have a 5% impact on the project. Similarly R49 has a 80% probability and will have a 90% impact on the project.How to calculate the numerical scales for the probability and impact matrix and what they mean depends upon the project and the organization. However, remember the relative meaning: Higher value of a risk on the probability scale means greater likelihood of risk occurrence, and higher value on the impact scale means greater effect on the project objectives.Each risk is rated (prioritized) according to the probability and the impact value assigned to it separately for each objective. Generally, you can divide the matrix in the table above into three areas; high-priority risks represented by higher numbers, such as R49, medium-priority risks represented by moderate numbers, such as R25, and low-priority risks represented by lower numbers, such as R12. However, each organization has to design its own risk score and risk threshold to guide the risk response plan.Note that impact can be a threat (a negative effect) or an opportunity (a positive effect). You will have separate matrices for threats and opportunities. Threats in the high-priority area might require priority actions and aggressive responses. Also, you will want to capitalize on those opportunities in the high-priority area, which you can do with relatively little effort. Risks posing threats in the low-priority area might not need any response, but they must be kept on the watch list to ensure that you don't get any unwanted or unexpected surprises towards the end of the project.Risk categorization - You defined the risk categories during the risk management planning and risk identification processes. Now you can assign the identified risks to those categories. You can also revisit the categorization scheme, such as RBS, that you developed for your project, because now you have more information about risks for the project. Categorizing risks by their causes often helps you develop effective risk responses.Expert judgment - You may need expert judgment to assess the probability and impact of each risk. To find an expert, look for people who've had experience with similar projects in the not too distant past. While weighing the expert judgment, look for possible biases. Often experts are biased toward their area or idea.
What difference between Statistical Sampling and non-statistical sampling?
Statistical sampling is an objective approach using probability to make an inference about the population. The method will determine the sample size and the selection criteria of the sample. The reliability or confidence level of this type of sampling relates to the number of times per 100 the sample will represent the larger population. Non-statistical sampling relies on judgment to determine the sampling method,the sample size,and the selection items in the sample.
What is the difference between clustering and load balancing?
Clustering is a group of resources trying to achieve the same objective, whereas load balancing is having several different servers that are completely unaware of each other and trying to achieve the same objective.
What is the importance of linear programming?
It allows you to maximise or minimise objective functions, subject to constraints that are linear.
Difference between objective probability and subjective probability?
Objective probability is based on some basis of fact, experimentation, or analysis. Subjective probability is based on someones guess.
What is the objective of process capability?
The objective of process capability is to determine what is the probability of the process producing product within the tolerance or specification limits provided by the customer.
Which is the term for a narrator who reports the fact without opinions or feelings?
Such a narrator is described as an objective narrator. In reality it is impossible to be totally objective in reporting anything.
Which is the term for a narrator who reports the facts without opinions or feelings?
Such a narrator is described as an objective narrator. In reality it is impossible to be totally objective in reporting anything.
Which is not the objective of Public Procurement and Distribution system followed by Indian Government?
Control the production of food grains
Is the expected value of a random variable more cumbersome to work with then an objective probability distribution?
Not necessarily. The answer depends on the event under consideration.Not necessarily. The answer depends on the event under consideration.Not necessarily. The answer depends on the event under consideration.Not necessarily. The answer depends on the event under consideration.
What is the main objective of distributed system?
The main objective of distribution system isi. to minimize the duration of fault current.ii. to minimize the number of customers affected by the fault system.the secondary objectives of distribution system arei. to eliminate the safety hazards as fast as possibleii. to limit the service outagesiii. to protect the consumers apparatus
If to understand something you need to rely on your own experience and culture does this mean it is impossible to have objective knowledge?
Having knowledge that is influenced by personal experience and cultural background doesn't necessarily mean it's impossible to have objective knowledge. It just means that objectivity might be challenging to achieve. By being aware of our biases and actively seeking diverse perspectives, we can strive towards a more objective understanding of the world.
What are the objective of seed viability?
measures the viability of seed using tetrazolium chloride and germination tests.
What is the total magnification if you only use the eyepiece?
it's impossible to just use the eyepiece without an objective lens, but the eyepiece alone is 10x.
Why is it impossible to consider the literal qualities of artwork when examining nonobjective works?
Non-objective works are completely abstract and do not represent anything literal.
What requires commanders weigh the military advantage anticipated by an attack on a military objective?
proportionality The cost of the mission in terms of your own troop casualties, collateral damage and the probability of mission success.,
