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Categorical Data Analysis
Published in William M. Mendenhall, Terry L. Sincich, Statistics for Engineering and the Sciences, 2016
William M. Mendenhall, Terry L. Sincich
In Chapters 7 and 8, we discussed how to make inferences about a proportion from a single population. Recall that the population proportion p is the probability of “success” in a binomial experiment—an experiment that results in one of two possible outcomes on any one trial. In this chapter, we are interested in making inferences about the unknown probabilities (or proportions) from a multinomial experiment with k possible outcomes. That is, we want to make inferences aboutp1, p2, pk, where pi is the probability of the ith outcome and p1 + p2 + · · · + pk = 1. (See Section 4.7 for a detailed discussion of multinomial experiments.)
Simple random sampling
Published in Mark Stamp, Introduction to Machine Learning with Applications in Information Security, 2023
Ideally, a confidence interval for a population proportion is based on the binomial distribution of the number of sampling units satisfying a condition (the number of successes). The binomial distribution is a discrete distribution. There are various methods for computing coverage probabilities of confidence intervals for a binomial proportion, see Brown et al. (2001) for a discussion. A common method for computing the confidence interval of a proportion is the Clopper-Pearson method. Function BinomCI of package DescTools can be used to compute confidence intervals for proportions (Signorell, 2021).
Statistics
Published in Dušan Teodorović, Miloš Nikolić, Quantitative Methods in Transportation, 2020
Dušan Teodorović, Miloš Nikolić
We could be interested, for example, to determine the proportion of drivers who do not wear seat belts, or the proportion of air passengers that have a fear of flying. In situations like these, we try to estimate a population proportion based on a sample proportion. As with the population mean estimation procedure, we should include in the analysis a margin of error.
The Movingo integrated ticket: seamless connections across the mälardalen region of Sweden
Published in Transportation Planning and Technology, 2020
Ilyas B. Alhassan, B. Matthews, Jeremy P. Toner, Yusak O. Susilo
Given that the sample proportion of car commuters that used rail services after the project is an unbiased point estimator of the population proportion, the proportion of car commuters using rail due to the integrated ticketing was estimated at the 95% confidence level. The estimate of the population proportion (p) whose estimator is (p^) is approximately normally distributed if n is sufficiently large (np>5 and nq>5, where q = 1 – p). The mean of the sampling distribution is the population proportion p with standard deviation . The (1-α) 100% confidence interval, CI, for the population proportion is where p^, the estimated sample proportion, is equal to the number of successes in the sample divided by the sample size, n (Washington, Karlaftis, and Mannering 2011).
Architects’ ranking of professional design services
Published in Architectural Engineering and Design Management, 2022
Bryan Lyndon Waters, Manish Kumar Dixit, Fatemeh Pariafsai
After a survey was created through Qualtrics, a study description and survey link were posted on 13 online knowledge community announcement pages accessible via the AIA official website. These knowledge communities served as platforms for professional discourse and information sharing, with each community dedicated to respective aspects of the profession. Members of the following knowledge communities were invited to participate in the study: Academy of Architecture for Health, Building Performance, Committee on Architecture for Education, Committee on Design, Committee on the Environment, Construction Contract Administration, Corporate Architects and Facility Management, Custom Residential Architects Network, Design for Aging, Interior Architecture Knowledge Community, Practice Management Knowledge Community, Small Project Design, and Technology in Architectural Practice. In addition, architects connected with the research team on LinkedIn were sent a study description and survey link to participate. Only the insights of licensed architects were used in this study to aid confidence in the credibility of the survey participants. In 2020, the year this study was conducted, the United States had 121,997 licensed architects. To compute the minimum sample size to meet the desired statistical constraints, an online sample size calculator (Calculator, 2022) was used. Assuming a 95% confidence level, 5% margin of error, 50% population proportion, and 121,997 population size, the minimum required sample size is 383. The survey received 435 participants, which surpasses the minimum sample size. However, only 85 licensed architects (about 22% of the minimum sample size) correctly followed the instructions to answer all the questions, which may be a limitation.
Application of the gold standard direct observation tool to estimate hand hygiene compliance among healthcare providers in Dessie referral hospital, Northeast Ethiopia
Published in International Journal of Environmental Health Research, 2022
Gashaw Tesfaye, Mesfin Gebrehiwot, Haileyesus Girma, Asmamaw Malede, Kefelegn Bayu, Metadel Adane
The sample size was determined using a single population proportion formula (see below) by considering 95% confidence level (Zα/2 = 1.96), 5% margin of error (d), and 22% sample proportion (p). The proportion value was taken from the percentage of hand hygiene compliance in Debre Berhan referral hospital, Ethiopia (Kolola and Gezahegn 2017). Accordingly, the sample size (n) was calculated to be 209. After adding a 10% non-response rate, the final sample size was determined to be 230.