Words of Estimative Probability
Words of Estimative Probability (WEP or WEPs) are terms used by intelligence analysts in the production of analytic reports to convey the likelihood of a future event occurring. They express the extent of their confidence in the finding. A well-chosen WEP provides a decision maker with an unambiguous estimate upon which to base a decision. WEP usage is not standard across the US Intelligence Community (IC). Some intelligence and policy failures appear to be related to an imprecise use of estimative words.
A well-chosen WEP gives a decision maker a clear and unambiguous estimate upon which to base a decision. Ineffective WEPs are vague or misleading about the likelihood of an event. An ineffective WEP places the decision maker in the role of the analyst. The decision maker has to infer the prediction alone, thus increasing the likelihood of poor decision making or snap decision making.
In 1964 Sherman Kent. one of the first contributors to a formal discipline of intelligence analysis, addressed the problem of misleading expressions of odds in National Intelligence Estimate s (NIE). In "Words of Estimative Probability" Kent distinguished between ‘poets’ (those preferring wordy probabilistic statements) from ‘mathematicians’ (those preferring quantitative odds). To bridge the gap between them and decision makers, Kent developed a paradigm relating estimative terms to odds. His goal was to "… set forth the community's findings in such a way as to make clear to the reader what is certain knowledge and what is reasoned judgment, and within this large realm of judgment what varying degrees of certitude lie behind each key judgment." citation
title = Sherman Kent and the Profession of Intelligence Analysis
publisher = Center for the Study of Intelligence, Central Intelligence Agency
date = November 2002
page =50 ] Kent’s initiative was not adopted although the idea was well received and remains compelling today.
However, the NIC’s own discussion of this paradigm seems to undercut its chances of being effective: Intelligence judgments pertaining to likelihood are intended to reflect the Community’s sense of the probability of a development or event. […] We do not intend the term “unlikely” to imply an event will not happen. We use “probably” and “likely” to indicate there is a greater than even chance. We use words such as “we cannot dismiss,” “we cannot rule out,” and “we cannot discount” to reflect an unlikely—or even remote—event whose consequences are such it warrants mentioning. Words such as “may be” and “suggest” are used to reflect situations in which we are unable to assess the likelihood generally because relevant information is nonexistent, sketchy, or fragmented. [ National Intelligence Council, 2007, p5 ]
This explanation is ‘a half-step forward, half-step backward.’ An agency-sponsored WEP paradigm "is" progress. However--an estimative statement that uses “maybe”. “suggest”. or other weasel word s is vague and symptomatic of the problem at hand--not its solution. In 1964 Kent railed against the “restort to expressions of avoidance…which convey a definite meaning but at the same time either absolves us completely of the responsibility or makes the estimate enough removed … as not to implicate ourselves.” citation
title = Sherman Kent and the Profession of Intelligence Analysis
publisher = Center for the Study of Intelligence, Central Intelligence Agency
date = November 2002
page =63 ]
Mercyhurst experience with standardized WEPs
The Mercyhurst College Institute for Intelligence Studies conducted several experiments investigating [http://sourcesandmethods.blogspot.com/2008/03/what-do-words-of-estimative-probability.html the IC’s interpretation of WEPs ] (results varied Citation
last = Wheaton
first = K.
title = What Do Words Of Estimative Probability Mean? An Exercise In Analyst Education
date = 24 March 2008
year = 2008
url = http://sourcesandmethods.blogspot.com/2008/03/what-do-words-of-estimative-probability.html
accessdate = 2008-04-24 ] ) and citation
last = Kesselman | first = Rachel
contribution = Estimative Words of Probability Trends in National Intelligence Estimates
title = International Studies Association 49th Annual Convention: Bridging Multiple Divides
date= 26 March 2008
url = http://www.allacademic.com/meta/p251711_index.html ] their use of WEPs in NIEs over the past three decades] to determine the significant changes in the ways the NIC has been articulating its intelligence judgments over time. See Analysis of Competing Hypotheses
Mercyhurst’s WEP paradigm reduces Kent’s schema to its least ambiguous words:
Analytic Confidence and Source Reliability are expressed on a 1 to 10 scale, with 10 high.
Table 4 contains a non-exhaustive list of common terms that are especially vague, known pejoratively as Weasel Words. Their use in estimative statements is almost certain to cause confusion; they should be avoided at all costs.
last = Blair
first = Bruce
title = The Logic of Intelligence Hype and Blindness
publisher = [http://www.cdi.org Center for Defense Intelligence ]
date = 2003
url = http://www.cdi.org/blair/logic-updated.pdf
format = PDF
accessdate = 2008-04-21|format=PDF
last = Chido
first = Diane E.
last2 = Wheaton
first2 = Kristan J.
first4= Kristina M.
author5=James J. Kelly
title = Structured Analysis of Competing Hypotheses: Theory And Application
place = Erie, PA
publisher = Mercyhurst College Institute of Intelligence Studies Press
year = 2006
url = http://www.mciis.org/
isbn =0-9773881-0-7 .
authorlink = Department Of Defense
title = Joint Intelligence 2.0
publisher = Department of Defense
date = 2007
url = http://www.fas.org/irp/doddir/dod/jp2_0.pdf
format = PDF
accessdate = 2008-04-21|format=PDF
first = Richards J. | last = Heuer
title = The Psychology of Intelligence Analysis
place = Langley, VA
publisher = [https://www.cia.gov/library/center-for-the-study-of-intelligence/index.html CIA Center for the Study of Intelligence]
year = 1999
isbn = 1 929667-00-0
last = Ward
first = David L.
authorlink = David L. Ward
title = Definition of Some Estimative Expressions
publisher = https://www.cia.gov/library/center-for-the-study-of-intelligence/index.html CIA Center For The Study Of Intelligence
date = 1964
accessdate = 2008-04-23
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The OLS linear regression analysis is a crucial statistics tool to estimate the relationship between variables. Usually, the estimator indicates the causality between one variable and the other (A Sykes, 1993) (e.g the product price and its demand quantity). This report will analyzes the product ‘Supa-clean’, a new cleaning agent in Cleano-max PLC, though two model: a demand function and a multivariate demand function. After analysing the estimator, the weakness and the room of improvement of this statistics tool will be discussed.
Ⅱ Constant-price elasticity demand estimation
ⅰ.The tables below demonstrate the calculation of the key values about the estimation of the demand function for Supa-clean.
ⅱ. When Explain how fundamental the OLS linear regression analysis plays in the own-price elasticity estimator, three aspects needed to be taken into consideration: the analysis of two objects, the relationship between them, and the diagnostic methods used for consequences.
Generally speaking, elasticity measures how a dependent variable varies with n independent variable (n = 1 in demand function). Therefore, the elasticity of demand measures the change in quantity with respect to the change in price. The formula of it is:
η= (percentage change in price / percentage change in price) (2.4)
Percentage changes of Q or P means that the proportion of change in Q or change in P occupied total Q or P. and stand for change in quantity and price, respectively, which could be calculated as:
η= = * (2.5)
= the slope of demand function
The equation (2.5) indicates that is the slope coefficient of the demand function. In own-price elasticity, this formula (2.5) would on.
. middle of paper.
. e plausible than model
Generally speaking, this report uses OLS linear regression to estimate two models: a demand function and a multivariate demand function of ‘Supa-clean’. In addition to this, several weaknesses of two models as well as this methodology also are indicated, such as: the omitted variables problem or the multicollinearity problem. Finally, the plausibility of the model of multivariate demand function has been proven better than the demand function’s.
A Sykes, 1993, An introduction to regression analysis
BR Beattie, CR Taylor, MJ Watts – 1985, The economics of production
DR Anderson, DJ Sweeney, TA Williams, 2011, Statistics for business and economics
SN Goodman,1999, Toward Evidence-Based Medical Statistics
I Dobbs, 2000, Managerial economics
M Shalev, 2007, Limits and alternatives to multiple regression in comparative research
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Sept. 29 (Bloomberg) -- Hennes & Mauritz AB, Europe’s second-largest clothing retailer, reported a drop in third- quarter earnings that beat analysts’ estimates as the company contained the impact of higher cotton prices and wages in Asia.
Operating profit fell 17 percent to 4.71 billion kronor ($700 million) in the three months ended Aug. 31, Stockholm- based H&M said today in a statement. The average of 17 analyst estimates compiled by Bloomberg was 4.35 billion kronor. The gross margin narrowed to 58.6 percent from 60.5 percent a year earlier, beating the 57.8 percent average estimate.
H&M shares rose to the highest in more than a month. In addition to a near doubling in the price of cotton last year, the retailer has also had to contend with higher labor expenses in Asia, where it gets about 75 percent of its clothes. Future earnings may benefit from a 54 percent decline in cotton prices since they surged to a record in March.
“Gross margin is a relief,” Jan Meijer, an analyst at ING Commercial Banking in Amsterdam, said in an e-mail. “The market was worried that H&M margins would be impacted heavily by higher cotton prices and wages in Asia.”
H&M gained as much as 5.7 percent in Stockholm trading and was up 9.2 kronor, or 4.7 percent, at 205 kronor as of 1 p.m.
“The third quarter could have been a lot worse from an input costs perspective, given higher cotton costs still filtering through to the company,” Societe Generale analyst Anne Critchlow said in an.
i2]], the vector of estimative for specific random regression coefficients for animal (i), and [Z'.
Working individuals nearing their 50's and 60' cannot rely solely on estimative guesses, they need to confidently know if their savings will be able to deliver adequate income to sustain the retirement lifestyle that they want and that is precisely the reason the VxPI indexes were created," commented Frank Votaw, Chief Operating Officer of Voth Nixon Group.
Sherman Kent, "Words of Estimative Probability," 1964, Central Intelligence Agency, https://www.
d) Estimative of the extension of the doping process.
Finally, the UN spokesperson added "on the humanitarian front, UNRWA is now housing up to 80,000 Palestinians in its various shelters and relief centers and UNRWA's schools; and that we estimative to be about the 5 percent of the entire population.
Given their inherent estimative nature, constant challenge and adjustment of the methodologies are mandatory steps to ensure valid, accurate, and reliable abortion estimates.
Estimative of Podisus nigrispinus (Dallas) (Heteroptera: Pentatomidae) development time with non linear models.
Trait Number of markers considered Estimative 214 150 100 50 [L.
Later, the assessment uses much stronger estimative language, stating that, "the USSR would almost certainly not intervene directly with its own forces" were Castro's regime overthrown by internal or external forces and that although the Soviets would respond to an overthrow with strong political action, the USSR "would almost certainly never intend to hazard its own safety for the sake of Cuba" (all emphases mine).
Sherman Kent, the father of intelligence analysis, captured these risks in his classic essay "Words of Estimative Probability," showing how leaders can be misled by ambiguous expressions of uncertainty.
Values of Pearson's correlation coefficient r and standard error of estimative SEE for equations used to calculate forward slip S from strain degree [epsilon] or strain degree [epsilon] and work roll peripherical speed v Rolling S = S = stand a[[epsilon].
PROJECT ESTIMATING TECHNIQUES
Experienced project managers (PMs ) appreciate the importance of producing accurate estimates although it consumes time and effort. PMs select among a selection of estimating schemes to get the precision they call for. An impressive amount of literature has been written on the subject. Of late. computer-based estimating softwares with proprietary algorithms are already available in the market. but are very expensive to purchase. This cannot make an extensive discussion of all the estimating techniques available instead. it presents the dynamics of precise estimating by discussing two established estimating
methods - the fundamental building blocks utilized by all accomplished and expert project estimators
Bottom up estimates are used when the project baselines are required or a definitive type of estimate is needed. These types of estimates are called bottom up ' because they begin by estimating the details of the project and then summarizing the details into summary levels. The work breakdown structure (WBS ) can be used for this roll up. The advantage of this kind of estimate is that it will produce accurate results. The accuracy of the bottom up estimate depends on the level of detail that is considered. Nicholas and Steyn (2008 ) argue that statistically. convergence takes place as more and more detail is added The disadvantage of this type of estimate is that the cost of doing detailed estimating is higher. and the time to produce the estimate is considerably longer
Bottom-up estimating starts from zero. accounts for each component of the WBS. and arrives at a sum for the project. It is completed with the project team and can be one of the most time-consuming methods to predict project costs. It relies on estimating the cost of individual work items. then adding them up to obtain a project Typically. an in-depth analysis of all project tasks. components. and processes is performed to estimate requirements for the items including labor and materials. The application of labor rates. material prices and overhead to the requirements turns the estimate into monetary units. A fringe benefit to completing a bottom-up estimate is the project team may buy into the project work as they see the cost and value of cost within the project
Another technique used in project estimation is called apportioning. or top-down. estimating. With this method. a given and then a percentage of the the phases and tasks of the project. The WBS can provide a good solid breakdown for using this estimation technique. In for this method to be as accurate as possible. however. Lock (2007 ) asserts that it is critical that. first. the overall project estimate is correct otherwise the project estimates for the smaller pieces won 't be accurate. Second apportioning is based on a formula derived from historical /data experience of other similar projects. Because of this. it is critical that the previous subjects be very similar to the project of hand. This technique is rarely as accurate as the bottom-up approach. but can be very valuable when.
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