Population Error Rate
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Tolerable Error Definition
Auditors choose from several types of sampling when performing an audit. Attribute sampling means that an item being sampled either will or won't possess certain qualities, or attributes. An auditor
Tolerable Error Rate
selects a certain number of records to estimate how many times a certain feature will show up in a population. When using attribute sampling, the sampling unit is a single record or document. Auditors typically use attribute sampling to test internal controls. An example of an attribute sampling feature may be that per the client's internal control procedures, all purchases upper deviation rate definition over $50 are supposed to be authorized by a purchase order. So every purchase over $50 either will or won't be authorized by a purchase order -- attribute sampling has no gray area. Here's how you'd use attribute sampling to see whether the client's internal control is working properly: Your population consists of all vendor invoices for purchases over $50, and the number of records you sample from that population is set at 75 records. Looking through your sample, you see that 3 of the 75 records aren't supported by a purchase order. That gives you a population error rate of 4 percent (3/75). How do you decide if this population error rate is okay? That decision is based on what figures you set for tolerable error, expected error, sampling risk, and confidence level. For example, let's say that The tolerable error rate is 7 percent. The expected error rate is 5 percent. The sampling risk is 2 percent. The confidence level is 98 percent. (Remember that the confidence level plus the sampling risk always equal 100 pe
engineering, see Tolerance (engineering). For the eponymous movie, see Margin for error (film). The top portion charts probability density against actual percentage, showing the relative probability that
Variable Sampling
the actual percentage is realised, based on the sampled percentage. In the tolerable error and expected error bottom portion, each line segment shows the 95% confidence interval of a sampling (with the margin of error tolerable error rate definition on the left, and unbiased samples on the right). Note the greater the unbiased samples, the smaller the margin of error. The margin of error is a statistic expressing the amount http://www.dummies.com/business/accounting/auditing/how-does-attribute-sampling-work/ of random sampling error in a survey's results. It asserts a likelihood (not a certainty) that the result from a sample is close to the number one would get if the whole population had been queried. The likelihood of a result being "within the margin of error" is itself a probability, commonly 95%, though other values are sometimes used. The larger the https://en.wikipedia.org/wiki/Margin_of_error margin of error, the less confidence one should have that the poll's reported results are close to the true figures; that is, the figures for the whole population. Margin of error applies whenever a population is incompletely sampled. Margin of error is often used in non-survey contexts to indicate observational error in reporting measured quantities. In astronomy, for example, the convention is to report the margin of error as, for example, 4.2421(16) light-years (the distance to Proxima Centauri), with the number in parentheses indicating the expected range of values in the matching digits preceding; in this case, 4.2421(16) is equivalent to 4.2421 ± 0.0016.[1] The latter notation, with the "±", is more commonly seen in most other science and engineering fields. Contents 1 Explanation 2 Concept 2.1 Basic concept 2.2 Calculations assuming random sampling 2.3 Definition 2.4 Different confidence levels 2.5 Maximum and specific margins of error 2.6 Effect of population size 2.7 Other statistics 3 Comparing percentages 4 See also 5 Notes 6 References 7 External links Explanation[edit] The margin of error is usually defined as the "radius" (or half the wi
εμάς.Μάθετε περισσότερα Το κατάλαβαΟ λογαριασμός μουΑναζήτησηΧάρτεςYouTubePlayΕιδήσειςGmailDriveΗμερολόγιοGoogle+ΜετάφρασηΦωτογραφίεςΠερισσότεραΈγγραφαBloggerΕπαφέςHangoutsΑκόμη περισσότερα από την GoogleΕίσοδοςΚρυφά πεδίαΒιβλίαbooks.google.gr - When it comes to https://books.google.gr/books?id=Q7eGUaFKmuAC&pg=PT204&lpg=PT204&dq=population+error+rate&source=bl&ots=Ece9WRit8S&sig=q8wtttlDfR-OZkQ6e5TPkB8d0-8&hl=en&sa=X&ved=0ahUKEwi1u4zH4-fPAhWCESwKHRicA7kQ6AEIcTAR computer security, the role of auditors today has never been more crucial. Auditors must ensure that all computers, https://www.jstor.org/stable/3151530 in particular those dealing with e-business, are secure. The only source for information on the combined areas of error rate computer audit, control, and security, the IT Audit, Control,...https://books.google.gr/books/about/IT_Audit_Control_and_Security.html?hl=el&id=Q7eGUaFKmuAC&utm_source=gb-gplus-shareIT Audit, Control, and SecurityΗ βιβλιοθήκη μουΒοήθειαΣύνθετη Αναζήτηση ΒιβλίωνΑγορά eBook - 85,99 €Λήψη αυτού του βιβλίου σε έντυπη μορφήWiley.comΕλευθερουδάκηςΠαπασωτηρίουΕύρεση σε κάποια βιβλιοθήκηΌλοι οι πωλητές»IT Audit, Control, and SecurityRobert R. MoellerJohn error rate definition Wiley & Sons, 12 Οκτ 2010 - 696 σελίδες 0 Κριτικέςhttps://books.google.gr/books/about/IT_Audit_Control_and_Security.html?hl=el&id=Q7eGUaFKmuACWhen it comes to computer security, the role of auditors today has never been more crucial. Auditors must ensure that all computers, in particular those dealing with e-business, are secure. The only source for information on the combined areas of computer audit, control, and security, the IT Audit, Control, and Security describes the types of internal controls, security, and integrity procedures that management must build into its automated systems. This very timely book provides auditors with the guidance they need to ensure that their systems are secure from both internal and external threats. Προεπισκόπηση αυτού του βιβλίου »
Login Help Contact Us About Access You are not currently logged in. Access your personal account or get JSTOR access through your library or other institution: login Log in to your personal account or through your institution. If You Use a Screen ReaderThis content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader. Journal of Marketing Research Vol. 19, No. 1, Feb., 1982 Estimation of Error ... Estimation of Error Rates in Several-Population Discriminant Analysis Stephen C. Hora and James B. Wilcox Journal of Marketing Research Vol. 19, No. 1 (Feb., 1982), pp. 57-61 Published by: American Marketing Association DOI: 10.2307/3151530 Stable URL: http://www.jstor.org/stable/3151530 Page Count: 5 Read Online (Free) Download ($24.00) Subscribe ($19.50) Cite this Item Cite This Item Copy Citation Export Citation Export to RefWorks Export a RIS file (For EndNote, ProCite, Reference Manager, Zotero…) Export a Text file (For BibTex) Note: Always review your references and make any necessary corrections before using. Pay attention to names, capitalization, and dates. × Close Overlay Journal Info Journal of Marketing Research Description: Journal of Marketing Research (JMR) concentrates on the subject of marketing research, from its philosophy, concepts, and theories to its methods, techniques, and applications. This quarterly, peer-reviewed journal is published for technically-oriented research analysts, educators, and statisticians. JMR addresses concepts, methods, and applications of marketing research that present new techniques, contribute to knowledge based on experimental or descriptive methods, and review developments and concepts in related fields that have bearing on the research industry and practice. Articles pertain to applications of new and current marketing research methods and techniques, clarifications of marketing research methodology and practice, and analysis of the state of the art marketing research. JMR publishes articles representing the entire spectrum of research in marketing, ranging from analytical models of marketing phenomena to descriptive studies using ethnographic methodologies. Most research published in JMR fits into two categories: (1) empirical research that tests a theory or describes and evaluates