Estimation

1. Estimation https://store.theartofservice.com/the-estimation-toolkit.html

2. Cost Cost estimation • When developing a business plan for a new or existing company, product, or project, planners typically make cost estimates in order to assess whether revenues/benefits will cover costs (see cost-benefit analysis). This is done in both business and government. Costs are often underestimated, resulting in cost overrun during execution. https://store.theartofservice.com/the-estimation-toolkit.html

3. Cost Cost estimation • Cost-plus pricing, is where the price equals cost plus a percentage of overhead or profit margin. https://store.theartofservice.com/the-estimation-toolkit.html

4. Spectral density - Estimation • The goal of spectral density estimation is to estimate the spectral density of a random signal from a sequence of time samples. Depending on what is known about the signal, estimation techniques can involve parametric or non-parametric approaches, and may be based on time-domain or frequency-domain analysis. For example, a common parametric technique involves fitting the observations to an autoregressive model. A common non-parametric technique is the periodogram. https://store.theartofservice.com/the-estimation-toolkit.html

5. Spectral density - Estimation • The spectral density is usually estimated using Fourier transform methods (such as the Welch method), but other techniques such as the maximum entropy method can also be used. https://store.theartofservice.com/the-estimation-toolkit.html

6. Exponential distribution - Parameter estimation • Suppose a given variable is exponentially distributed and the rate parameter λ is to be estimated. https://store.theartofservice.com/the-estimation-toolkit.html

7. IT risk management - Risk estimation • There are two methods of risk assessment in information security field, qualitative and quantitative. https://store.theartofservice.com/the-estimation-toolkit.html

8. IT risk management - Risk estimation • Purely quantitative risk assessment is a mathematical calculation based on security metrics on the asset (system or application) https://store.theartofservice.com/the-estimation-toolkit.html

9. IT risk management - Risk estimation • For example, if you consider the risk scenario of a Laptop theft threat, you should consider the value of the data (a related asset) contained in the computer and the reputation and liability of the company (other assets) deriving from the lost of availability and confidentiality of the data that could be involved https://store.theartofservice.com/the-estimation-toolkit.html

10. IT risk management - Risk estimation • Qualitative risk assessment (three to five steps evaluation, from Very High to Low) is performed when the organization requires a risk assessment be performed in a relatively short time or to meet a small budget, a significant quantity of relevant data is not available, or the persons performing the assessment don't have the sophisticated mathematical, financial, and risk assessment expertise required https://store.theartofservice.com/the-estimation-toolkit.html

11. IT risk management - Risk estimation • Risk estimation has as input the output of risk analysis and can be split in the following steps: https://store.theartofservice.com/the-estimation-toolkit.html

12. IT risk management - Risk estimation • assessment of the consequences through the valuation of assets https://store.theartofservice.com/the-estimation-toolkit.html

13. IT risk management - Risk estimation • assessment of the likelihood of the incident (through threat and vulnerability valuation) https://store.theartofservice.com/the-estimation-toolkit.html

14. IT risk management - Risk estimation • assign values to the likelihood and consequence of the risks https://store.theartofservice.com/the-estimation-toolkit.html

15. IT risk management - Risk estimation • The output is the list of risks with value levels assigned. It can be documented in a risk register https://store.theartofservice.com/the-estimation-toolkit.html

16. IT risk management - Risk estimation • During risk estimation there are generally three values of a given asset, one for the loss of one of the CIA properties: Confidentiality, Integrity, Availability. https://store.theartofservice.com/the-estimation-toolkit.html

17. Cost estimation in software engineering - Methods • Methods for estimation in software engineering include: https://store.theartofservice.com/the-estimation-toolkit.html

18. Cost estimation in software engineering - Methods • Evidence-based Scheduling Refinement of typical agile estimating techniques using minimal measurement and total time accounting. https://store.theartofservice.com/the-estimation-toolkit.html

19. Cost estimation in software engineering - Methods • PRICE Systems Founders of Commercial Parametric models that estimates the scope, cost, effort and schedule for software projects. https://store.theartofservice.com/the-estimation-toolkit.html

20. Cost estimation in software engineering - Methods • Program Evaluation and Review Technique (PERT) https://store.theartofservice.com/the-estimation-toolkit.html

21. Cost estimation in software engineering - Methods • SEER-SEM Parametric Estimation of Effort, Schedule, Cost, Risk. Mimimum time and staffing concepts based on Brooks's law https://store.theartofservice.com/the-estimation-toolkit.html

22. Cost estimation in software engineering - Methods • The Use Case Points method (UCP) https://store.theartofservice.com/the-estimation-toolkit.html

23. Test effort - Methods for estimation of the test effort • Following approaches can be used for the estimation: top-down estimation and bottom-up estimation https://store.theartofservice.com/the-estimation-toolkit.html

24. Test effort - Methods for estimation of the test effort • We can also use the following techniques for estimating the test effort: https://store.theartofservice.com/the-estimation-toolkit.html

25. Test effort - Methods for estimation of the test effort • Conversion of software size into person hours of effort directly using a conversion factor. For example, we assign 2 person hours of testing effort per one Function Point of software size or 4 person hours of testing effort per one use case point or 3 person hours of testing effort per one Software Size Unit https://store.theartofservice.com/the-estimation-toolkit.html

26. Test effort - Methods for estimation of the test effort • Conversion of software size into testing project size such as Test Points or Software Test Units using a conversion factor and then convert testing project size into effort https://store.theartofservice.com/the-estimation-toolkit.html

27. Test effort - Methods for estimation of the test effort • Compute testing project size using Test Points of Software Test Units. Methodology for deriving the testing project size in Test Points is not well documented. However, methodology for deriving Software Test Units is defined in a paper by Murali https://store.theartofservice.com/the-estimation-toolkit.html

28. Test effort - Methods for estimation of the test effort • We can also derive software testing project size and effort using Delphi Technique or Analogy Based Estimation technique. https://store.theartofservice.com/the-estimation-toolkit.html

29. Dan Galorath - Software sizing, estimation, and risk management • Barry Boehm, the Director of the Center for Software Engineering, University of Southern California, noted that this book helps to “identify the best investments for improving your software productivity and cycle time.” Though the book is written with SEER-SEM in mind, the general principles of the book apply to all types of software estimation models. https://store.theartofservice.com/the-estimation-toolkit.html

30. Estimation • Typically, estimation involves "using the value of a statistic derived from a sample to estimate the value of a corresponding population parameter" https://store.theartofservice.com/the-estimation-toolkit.html

31. Estimation - How estimation is done • An example of estimation would be determining how many candies of a given size are in a glass jar https://store.theartofservice.com/the-estimation-toolkit.html

32. Estimation - How estimation is done • However, a point estimation is likely to be incorrect, because the sample size - in this case, the number of candies that are visible - is too small a number to be sure that it does not contain anomalies that differ from the population as a whole https://store.theartofservice.com/the-estimation-toolkit.html

33. Estimation - Uses of estimation • A Fermi problem, in physics, is one concerning estimation in problems which typically involve making justified guesses about quantities that seem impossible to compute given limited available information. https://store.theartofservice.com/the-estimation-toolkit.html

34. Estimation - Uses of estimation • Estimation in project planning can be particularly significant, because plans for the distribution of labor and for purchases of raw materials must be made, despite the inability to know every possible problem that may come up https://store.theartofservice.com/the-estimation-toolkit.html

35. Estimation - Uses of estimation • An informal estimate when little information is available is called a guesstimate, because the inquiry becomes closer to purely guessing the answer. The "estimated" sign, ℮, is used to designate that package contents are close to the nominal contents. https://store.theartofservice.com/the-estimation-toolkit.html

36. Probit model - Maximum likelihood estimation • Suppose data set contains n independent statistical units corresponding to the model above. Then their joint log-likelihood function is https://store.theartofservice.com/the-estimation-toolkit.html

37. Probit model - Maximum likelihood estimation • The estimator which maximizes this function will be consistent, asymptotically normal and efficient provided that E[XX'] exists and is not singular. It can be shown that this log-likelihood function is globally concave in β, and therefore standard numerical algorithms for optimization will converge rapidly to the unique maximum. https://store.theartofservice.com/the-estimation-toolkit.html

38. Probit model - Maximum likelihood estimation • Asymptotic distribution for is given by https://store.theartofservice.com/the-estimation-toolkit.html

39. Probit model - Maximum likelihood estimation • and φ = Φ' is the Probability Density Function (PDF) of standard normal distribution. https://store.theartofservice.com/the-estimation-toolkit.html

40. Quantum phase estimation algorithm • In Quantum Computing, the quantum phase estimation algorithm is a quantum algorithm that finds many applications as a subroutine in other algorithms. The quantum phase estimation algorithm allows one to estimate the eigenphase of an eigenvector of a unitary gate, given access to a quantum state proportional to the eigenvector and a procedure to implement the unitary conditionally. https://store.theartofservice.com/the-estimation-toolkit.html

41. Quantum phase estimation algorithm - The Problem • Let U be a unitary operator that operates on m qubits. Then all of the eigenvalues of U have absolute value 1. Thus the spectrum of a unitary operator consists of phases . Given an eigenvector , such that , the objective is to estimate . The phase estimation algorithm solves this problem. https://store.theartofservice.com/the-estimation-toolkit.html

42. Quantum phase estimation algorithm - The Algorithm • Suppose we wish to compute the phases to an accuracy of n bits. We achieve this by subjecting our eigenvector of to a succession of n controlled operators, followed by the inverse of the quantum Fourier transform. The controlled operators are the powers of from to controlled . https://store.theartofservice.com/the-estimation-toolkit.html

43. Quantum phase estimation algorithm - The Algorithm • After putting the control lines into the Hadamard state, we have https://store.theartofservice.com/the-estimation-toolkit.html

44. Quantum phase estimation algorithm - The Algorithm • Applying the inverse of the quantum Fourier transform upon the n qubits yields https://store.theartofservice.com/the-estimation-toolkit.html

45. Quantum phase estimation algorithm - The Algorithm • If the phase is exactly a root of unity, the quantum Fourier transform will single out that phase in binary expansion. If not, there will be a probability distribution clustered around the correct phase. https://store.theartofservice.com/the-estimation-toolkit.html

46. Quantum phase estimation algorithm - The Algorithm • If is really a superposition of eigenstates, there is a weighted probability distribution over the individual eigenstates, with the weight given by the Born probabilities. This is because eigenstates corresponding to different eigenvalues are orthogonal. https://store.theartofservice.com/the-estimation-toolkit.html

47. Quantum phase estimation algorithm - The Algorithm • The efficiency of this algorithm depends on our access to . If we only have access via an oracle function, then we need exponentially many calls (in n) to the oracle to compute (for example) . If we have complete access to , then we can use exponentiation by squaring to compute the necessary powers of efficiently. https://store.theartofservice.com/the-estimation-toolkit.html

48. Bayesian decision theory - Minimum mean square error estimation • The most common risk function used for Bayesian estimation is the mean square error (MSE), also called squared error risk. The MSE is defined by https://store.theartofservice.com/the-estimation-toolkit.html

49. Bayesian decision theory - Minimum mean square error estimation • where the expectation is taken over the joint distribution of \theta and x. https://store.theartofservice.com/the-estimation-toolkit.html

50. Statistics - Interval estimation • Most studies only sample part of a population, so results don't fully represent the whole population https://store.theartofservice.com/the-estimation-toolkit.html