Susan Telke, Lynn E. Eberly · Journal of Wound Ostomy and Continence Nursing · 2011
Susan E. Telke, MS, Instructor, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis. Lynn E. Eberly, PhD, Associate Professor, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis. Correspondence: Lynn E. Eberly, PhD, Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St SE, MMC 303, Minneapolis, MN 55455 ([email protected]).
Cited by 5
Susan Telke, Lynn E. Eberly · Journal of Wound Ostomy and Continence Nursing · 2011
Susan E. Telke, MS, Instructor, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis. Lynn E. Eberly, PhD, Associate Professor, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis. Correspondence: Lynn E. Eberly, PhD, Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St SE, MMC 303, Minneapolis, MN 55455 ([email protected]).
Cited by 3
Lynn E. Eberly, Susan Telke · Journal of Wound Ostomy and Continence Nursing · 2011
Lynn E. Eberly, PhD, Associate Professor, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis. Susan E. Telke, MS, Instructor, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis. Correspondence: Lynn E. Eberly, PhD, Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St SE, MMC 303, Minneapolis, MN 55455 ([email protected]).
Cited by 8
Ralph J. Marino · Archives of Physical Medicine and Rehabilitation · 1995
Cited by 2
Mauro Naghettini · 2016
Cited by 5
Abbas F.M. Alkarkhi, Wasin A.A. Alqaraghuli · Elsevier eBooks · 2019
Cited by 2
Frank den Hollander · American Mathematical Society eBooks · 2008
Cited by 0Open access
Susan Telke, Lynn E. Eberly · Journal of Wound Ostomy and Continence Nursing · 2011
Susan E. Telke, MS, Instructor, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis. Lynn E. Eberly, PhD, Associate Professor, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis. Correspondence: Lynn E. Eberly, PhD, Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St SE, MMC 303, Minneapolis, MN 55455 ([email protected]).
Cited by 2
Susan Telke, Lynn E. Eberly · Journal of Wound Ostomy and Continence Nursing · 2011
Susan E. Telke, MS, Instructor, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis. Lynn E. Eberly, PhD, Associate Professor, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis. Correspondence: Lynn E. Eberly, PhD, Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St SE, MMC 303, Minneapolis, MN 55455 ([email protected]).
Cited by 3
Lynn E. Eberly, Susan Telke · Journal of Wound Ostomy and Continence Nursing · 2011
Lynn E. Eberly, PhD, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis. Susan E. Telke, MS, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis. Correspondence: Lynn E. Eberly, PhD, Division of Biostatistics, School of Public Health, University of Minnesota, 420 Delaware St SE, MMC 303, Minneapolis, MN 55455 ([email protected]).
Cited by 6
Nikola Štaffenová (21238199), Alžbeta Kucharčíková (21238202), Lukáš Falát (12667203) · Figshare · 2025
<div> Human capital management (HCM) helps manage and develop human capital (HC) in businesses through investments that increase the value of HC and contribute to improving the financial situation of companies. Industry 4.0 (4IR) presents new challenges especially for small and medium-sized enterprises (SMEs). The article aim is to find out how important SMEs perceive investments in HC, whether they evaluate the effectiveness of investments in HC and which of the selected HCM activities related to digitization they consider essential. It was used basic scientific methods, Cronbach’s alpha, descriptive statistics, and the chi-square test. It was found that in 2020 up to 49.56% and in 2021 up to 63.94% of respondents consider the funds spent on employees as investments in HC. In connection with digitization, it is striking that up to 76.98% of SMEs in 2020 and 68.34% of respondents in 2021 did not use a personnel information system (HRIS) and an even greater number of companies did not even prepare reports for the HC area. The originality of the article lies in the processing of a two-year questionnaire survey and in the intersection of HCM and Industry 4.0. This is a very current topic, which is also emphasized by the European Union. Future research could focus on employee training models (HC development), or on the culture of innovation and adaptability in companies that digitalize. </div>
Cited by 0Open access
P. Sedgwick · BMJ · 2010
A randomised controlled trial evaluated the cost and efficacy of community leg ulcer clinics that used four layer compression bandaging. The control treatment was provision of usual care by district nurses.1 Over the 12 months of follow-up, ulcers healed more quickly in the clinic group than in the control group (P=0.03). Statistical tests were two sided. Which of the following, if any, are true?
Cited by 27
Luis Fernando Ibarra · 2024
Cited by 1
F. Xavier Malcata · 2020
A statistical hypothesis is a hypothesis that is testable, on the basis of observing a process modeled by a set of random variables that follow a probability distribution known a priori. Hypothesis testing (or confirmatory data analysis) is a method of statistical inference that resorts to tests of significance to determine the probability that a statement is true, and at what likelihood such a statement may be accepted as true. This chapter explains basic process of hypothesis testing that consists of four sequential steps: formulation of the null hypothesis, H 0; identification of an appropriate test statistic that can be used to assess the truth of the null hypothesis (dependent on the nature of the data and of the test); computation of the P-value, or associated probability that a test statistic at least as significant as the one determined from the sample data would be obtained; and comparison of the P-value with an acceptable significance level.
Cited by 0
Wolfgang Karl Härdle, Vladimir Spokoiny, Vladimir Panov et al. · Springer texts in statistics · 2013
Cited by 0
J. E. R. Staddon · 2024
Cited by 0
ウオイ, トオル, 魚井, 徹 · OUKA (Osaka University Knowledge Archive) (Osaka University)
Cited by 0Open access
Ksenia Juravel (5102189), Luis Porras (4623364), Sebastian Höhna (506055) et al. · Figshare · 2023
<div><p>An accurate phylogeny of animals is needed to clarify their evolution, ecology, and impact on shaping the biosphere. Although datasets of several hundred thousand amino acids are nowadays routinely used to test phylogenetic hypotheses, key deep nodes in the metazoan tree remain unresolved: the root of animals, the root of Bilateria, and the monophyly of Deuterostomia. Instead of using the standard approach of amino acid datasets, we performed analyses of newly assembled genome gene content and morphological datasets to investigate these recalcitrant nodes in the phylogeny of animals. We explored extensively the choices for assembling the genome gene content dataset and model choices of morphological analyses. Our results are robust to these choices and provide additional insights into the early evolution of animals, they are consistent with sponges as the sister group of all the other animals, the worm-like bilaterian lineage Xenacoelomorpha as the sister group of the other Bilateria, and tentatively support monophyletic Deuterostomia.</p></div>
Cited by 0Open access
Dianxu Ren · Journal of Emergency Nursing · 2008
Cited by 24
Manuela Expósito Ruíz, Sabina Pérez-Vicente, Francisco Rivas‐Ruiz · Allergologia et Immunopathologia · 2010
The aim of statistical inference is to predict the parameters of a population, based on a sample of data. Inferential statistics encompasses the estimation of parameters and model predictions. The present article describes the hypothesis tests or statistical significance tests most commonly used in healthcare research.
Cited by 14Open access