The practice of ranking objects, events, and people to determine relevance, importance, or competitive edge is ancient. Recently, the use of rankings has permeated into daily usage, especially in the fields of business and education. When determining the association among those creating the ranks (herein called sources), the traditional assumption is that all sources compare a list of the same items (herein called elements). In the twenty-first century, it is rare that any two sources choose identical elements to rank. Adding to this difficulty, the number of credible sources creating and releasing rankings is increasing. In statistical literature, there is no current methodology that adequately assesses the association among multiple sources. We introduce rank-adapted singular value decomposition (R-A SVD), a new method that uses Kendall's tau as the underlying correlation method. We begin with (P), a matrix of data ranks. The first step is to factor the covariance matrix (K) as follows: K = cov(P) = V D^2 V Here, (V) is an orthonormal basis for the rows that is useful in identifying when sources agree as to the rank order and specifically which sources. D is a diagonal of eigenvalues. By analogy with singular value decomposition (SVD), we define U^* as U^* = PVD^(-1) The diagonal matrix, D, provides the factored eigenvalues in decreasing order. The largest eigenvalue is used to assess the overall association among the sources and is a conservative unbiased method comparable to Kendall's W. Anderson's test determines whether this association is significant and also identifies other significant eigenvalues produced by the covariance matrix.. Using Anderson's test (1963) we identify the a significantly large eigenvalues from D. When one or more eigenvalues is significant, there is evidence that the association among the sources is significant. Focusing on the a corresponding vectors of V specifically identifies which sources agree. In cases where more than one eigenvalue is significant, the $a$ significant vectors of V provide insight into factions. When more than one set of sources is in agreement, each group of agreeing sources is considered a faction. In many cases, more than one set of sources will be in agreement with one another but not necessarily with another set of sources; each group that is in agreement would be considered a faction. Using the a significant vectors of U^* provides different but equally important results. In many cases, the elements that are being ranked can be subdivided into equivalence classes. An equivalence class is defined as subpopulations of ranked elements that are similar to one another but dissimilar from other classes. When these classes exist, U^* provides insight as to how many classes and which elements belong in each class. In summary, the R-A SVD method gives the user the ability to assess whether there is any underlying association among multiple rank sources. It then identifies when sources agree and allows for more useful and careful interpretation when analyzing rank data. / Statistics

No description available.

The role of streams in the global carbon budget was for a long time neglected, since they were considered passive transporters of carbon from land to sea. However, studies have shown that streams are often supersaturated in carbon dioxide (CO2), making them sources of carbon to the atmosphere. The main sources of stream CO2 are in-stream mineralization of organic matter and transport of carbon from the catchment. The catchment derived CO2 could both be of biogenic (respiration) or geogenic (weathering) origin. Most studies regarding the topic rely on measurements carried out in forest-dominated catchments, while agricultural streams are under-represented. The objective of this study was to examine partial pressure of CO2 (pCO2) in streams in catchments dominated by agriculture. This was done to increase the knowledge about agricultural influence on stream pCO2, and to provide a basis for planning mitigation strategies for reducing CO2 emissions from the agriculture sector. Sampling was performed in ten streams draining agriculture-dominated catchments around Uppsala, Sweden, from June to November 2017. Measurements of pCO2 were carried out with floating chambers, equipped with CO2 sensors. Nutrients, organic carbon, discharge and different chemical variables were also measured. For correlation tests, the method Kendall’s Tau was used. Catchments were delineated in a geographic information system (GIS) and the CORINE Land Cover dataset was used to examine land use. Stream specific median pCO2 varied from 3000 to 10 000 μatm. In some streams, pCO2 exceeded 10 000 μatm, which was outside of the sensor’s measurement range. Values of pCO2 were high compared to similar studies in forested catchments, which could indicate that occurrence of agriculture in the catchment increases stream CO2. Correlation was found between pCO2 and discharge, with negative correlation in five streams and positive correlation in two. Negative correlation was found between pCO2 and pH and percentage of dissolved oxygen, respectively. No significant correlation was found between pCO2 and fraction of agricultural land use, nutrients or organic carbon. Further studies are needed to examine the sources of CO2, since it is possible that a large part of the CO2 has a geogenic origin. The floating chamber method should be revised to reduce the sensor’s sensitivity to condensation and cold temperatures, and to increase the measuring range.

No description available.

It is of great importance to find an analytical copula that will represent the empirical lower tail dependence. In this study, the pairwise empirical copula are estimated using data of the S&P 500 stocks during the period 2007-2010.Different optimization methods and measures of dependence have been used to fit Gaussian, t and Clayton copula to the empirical copulas, in order to represent the empirical lower tail dependence. These different measures of dependence and optimization methods with their restrictions, point at different analytical copulas being optimal. In this study the t copula with 5 degrees of freedom is giving the most fulfilling result, when it comes to representing lower tail dependence. The t copula with 5 degrees of freedom gives the best representation of empirical lower tail dependence, whether one uses the 'Empirical maximum likelihood estimator', or 'Equal Ƭ' as an approach.

Research shows that, when unintentional harm to patients in outpatient and hospital settings occurs, root cause analysis (RCA) investigations should be conducted to identify and implement corrective actions to prevent future patient harm. Executives at a small healthcare consulting company that employs patient safety officers (PSOs) responsible for conducting RCAs were concerned with the low quality of RCA outcomes, prompting this postinvestigation assessment of PSOs' RCA training and experiences. Guided by adult learning theory, the purpose of this study was to assess PSOs' RCA training and investigation experiences by examining self-reported benefits, attitudes, barriers, and time since training, and the relationship between time since training and the number of barriers encountered during RCA investigations. This quantitative study used a preestablished survey with a purposeful sample of 89 PSOs located at 75 military health care facilities in the United States and abroad. Data analysis included descriptive statistics and Kendall's tau-b correlations. Results indicated that PSOs had positive training experiences, valued RCA investigations, varied on the time since RCA training, and encountered barriers conducting RCAs. Kendall's tau-b correlation analysis showed that the time since training was not significantly associated with the frequency of barriers they encountered. Findings suggest that the transfer of technical RCA knowledge was applied during actual RCA investigations regardless of time since training, and barriers contributed to subpar quality RCA outcomes. RCA professional development was designed to enhance nontechnical, soft competency skills as a best practice to overcome encountered barriers and promote social change in the field.

This master's thesis is focused on problem of orders of ingredients in McDonald's. It's an analysis of usage changes depending on outside temperature. Thesis includes theoretical background for correct analysis of the problem and possibilities to figuring it out. There is also an algorithmus for more efficient solution of problem with needs or excess of ingredients. There is also a program written in VBA language, that makes more simple usage of this algorithm on restaurants.