El presente trabajo de investigación se desarrolló con la finalidad de ejercer la profesión de Administración de Negocios Globales, es por ello que se trabajó en las variables propuestas y su factibilidad en la ejecución en base a un estudio de mercado, un estudio organizacional sobre los aspectos legales y el cálculo de la inversión. El método de investigación utilizado fue el hipotético - deductivo y se aplicaron los diseños experimentales, longitudinales, prospectivos. Se consideró como muestra a 323 personas, todos de nacionalidad estadounidense. The present research work was developed in order to practice the profession of Global Business Administration, that is why we worked on the proposed variables and their feasibility in the execution based on a market study, an organizational study on the aspects legal and calculation of the investment. The research method used was the hypothetico - deductive and the experimental, longitudinal, prospective designs were applied. A sample of 323 people was considered, all of them of American nationality.

Singular Spectrum Analysis (SSA) is a method for decomposing and forecasting time series that recently has had major developments but it is not yet routinely included in introductory time series courses. An international conference on the topic was held in Beijing in 2012. The basic SSA method decomposes a time series into trend, seasonal component and noise. However there are other more advanced extensions and applications of the method such as change-point detection or the treatment of multivariate time series. The purpose of this work is to understand the basic SSA method through its application to the monthly average sea temperature in a point of the coast of South America, near where “EI Ni˜no” phenomenon originates, and to artificial time series simulated using harmonic functions. The output of the basic SSA method is then compared with that of other decomposition methods such as classic seasonal decomposition, X-11 decomposition using moving averages and seasonal decomposition by Loess (STL) that are included in some time series courses.

Patterns of articles published in The Journal for Specialists in Group Work (JSGW) from 1981–2010 were reviewed in this meta-study. Author (e.g., sex, employment setting, nation of domicile) and article (e.g., topic, design, sample, sample size, participant type, statistical procedures, and sophistication) characteristics were analyzed for trends over time.

No description available.

Trend scoring is often used in large-scale assessments to monitor for rater drift when the same constructed response items are administered in multiple test administrations. In trend scoring, a set of responses from Time A are rescored by raters at Time B. The purpose of this study is to examine the ability of trend-monitoring statistics to detect rater effects in the context of trend scoring. The present study examines the percent of exact agreement and Cohen’s kappa as interrater agreement measures, and the paired t-test and Stuart’s Q as marginal homogeneity measures. Data that contains specific rater effects is simulated under two frameworks: the generalized partial credit model and the latent-class signal detection theory model. The findings indicate that the percent of exact agreement, the paired t-test, and Stuart’s Q showed high Type I error rates under a rescore design in which half of the rescore papers have a uniform score distribution and the other half have a score distribution proportional to the population papers at Time A. All these Type I errors were reduced when using a rescore design in which all rescore papers have a score distribution proportional to the population papers at Time A. For the second rescore design, results indicate that the ability of the percent of exact agreement, Cohen’s kappa, and the paired t-test in detecting various effects varied across items, sample sizes, and type of rater effect. The only statistic that always detected every level of rater effect across items and frameworks was Stuart’s Q. Although advances have been made in the automated scoring field, the fact is that many testing programs require humans to score constructed response items. Previous research indicates that rater effects are common in constructed response scoring. In testing programs that keep trends in data across time, changes in scoring across time confound the measurement of change in student performance. Therefore, the study of methods to ensure rating consistency across time, such as trend scoring, is important and needed to ensure fairness and validity.

This study is the first in a series of planned studies on the application of multidimensional visualization of business information and data within the context of accounting. The study's research question is: When is multidimensional visualization of information a better problem representation, improving both the effectiveness and efficiency of a spatial accounting judgment? To examine when multidimensional visualization can assist auditors in configural cue pattern recognition, the study employs the traditional DuPont analysis as the three pieces of key information to be represented on the X, Y, and Z axes of a single 3-D perspective display. To help determine when use of 3-D perspective display is beneficial in combining pieces of information, I rely on Vessey's (1991) Cognitive Fit Theory, and the Proximity Compatibility Principle (PCP) proposed by Wickens and Carswell (1995). The study has two hypotheses. Hypothesis H1 predicted that participants viewing a set of 2-D displays will be the most effective or most efficient in generating hypotheses for what caused the changes in the trend of accounting data or in estimating values. Hypothesis H2 predicted that participants viewing a single 3-D perspective display will be the most effective or most efficient in recognizing patterns of accounting data or in generating hypotheses for what caused the emerged pattern. To test the hypotheses of the study a 3 x 2 between-subjects design (display format x task) is used. The independent variables are display types and task types. Graphical display was manipulated at three levels: no graphical display (table only), 2-D display, and 3-D perspective display. Task was manipulated at two levels: trend analysis and pattern recognition task. The need for a fit between different types of spatial tasks and display formats is demonstrated by the findings of this study: 1) that 2-D displays appear to be more suitable for spatial tasks involving the generation of hypotheses for causes of trends in accounting data, while 2) 3-D perspective displays appear to be more suitable for spatial tasks involving pattern recognition in accounting data.

Sea level rise (SLR) varies depending on location. It is therefore important to local residents, businesses and government to analyze SLR locally. Further, because of increasing ice melt and other effects of climate change, rates of SLR may change. It is therefore also important to evaluate rates of change of SLR, which we call sea level acceleration (SLA) or deceleration. The present thesis will review the annual average sea level data compiled at the Key West tidal gauge in Key West, Florida. We use a multi-model approach that compares the results of various models on that data set. The goal is to determine if there is a consistent result that can be ascertained from the various models. Generally, all the models reveal a clear upward trend of SLR. Further, the models provide evidence that the trend has increased over the last 8-10 years, i.e., that there is SLA.

Every day, traditional statistical methodology are used world wide to study a variety of topics and provides insight regarding countless subjects. Each technique is based on a distinct set of assumptions to ensure valid results. Additionally, many statistical approaches rely on large sample behavior and may collapse or degenerate in the presence of small, spare, or correlated data. This dissertation details several advancements to detect these conditions, avoid their consequences, and analyze data in a different way to yield trustworthy results. One of the most commonly used modeling techniques for outcomes with only two possible categorical values (eg. live/die, pass/fail, better/worse, ect.) is logistic regression. While some potential complications with this approach are widely known, many investigators are unaware that their particular data does not meet the foundational assumptions, since they are not easy to verify. We have developed a routine for determining if a researcher should be concerned about potential bias in logistic regression results, so they can take steps to mitigate the bias or use a different procedure altogether to model the data. Correlated data may arise from common situations such as multi-site medical studies, research on family units, or investigations on student achievement within classrooms. In these circumstance the associations between cluster members must be included in any statistical analysis testing the hypothesis of a connection be-tween two variables in order for results to be valid. Previously investigators had to choose between using a method intended for small or sparse data while assuming independence between observations or a method that allowed for correlation between observations, while requiring large samples to be reliable. We present a new method that allows for small, clustered samples to be assessed for a relationship between a two-level predictor (eg. treatment/control) and a categorical outcome (eg. low/medium/high).

The increased concentration of greenhouse gases is associated with the global warming in the lower troposphere. For over twenty years, the space physics community has studied a hypothesis of global cooling in the thermosphere, attributable to greenhouse gases. While the global temperature increase in the lower troposphere has been relatively well established, the existence of global changes in the thermosphere is still under investigation. A central difficulty in reaching definite conclusions is the absence of data with sufficiently long temporal and sufficiently broad spatial coverage. Time series of data that cover several decades exist only in a few separated regions. The space physics community has struggled to combine the information contained in these data, and often contradictory conclusions have been reported based on the analyses relying on one or a few locations. To detect global changes in the ionosphere, we present a novel statistical methodology that uses all data, even those with incomplete temporal coverage. It is based on a new functional regression approach that can handle unevenly spaced, partially observed curves. While this research makes a solid contribution to the space physics community, our statistical methodology is very flexible and can be useful in other applied problems.

<p>Syftet med denna studie är att undersöka huruvida de informanter som intervjuades till studien anser att den ekologiska medvetenheten är en trend eller en bestående aspekt i klädbranschen. Vidare undersöks hur informanternas ekologiska tänkande yttrar sig i arbetslivet och i privatlivet. Intervjuerna berörde deras tankar kring miljömedvetenhet, etiska aspekter vid ekologisk produktion. Likaså analyseras om informanterna kan kategoriseras som en nyligen introducerad kulturell typ, BoBos.</p><p>Den slutsats jag åstadkom var att informanterna besitter en ekologisk medvetenhet som yttrar sig i deras arbets- och privatliv. Deras åsikter och medvetenhet kan karakteriseras som boboitiska och anses vara tidsenliga för dagens samhälle.</p> / <p> </p><p>The purpose of this study is to examine whether the informants interviewed for the study believe that ecological awareness is a trend or a permanent aspect of the clothing industry and whether ecological thinking manifests itself in the work and private life of the informants.  The interviews investigated their views on environmental issues and the ethical aspects of organic production. Similarly, analysis of informants can be categorized as an analysis of a newly introduced cultural type, Bobo.</p><p>The conclusion I achieved was that the informants have an ecological awareness which manifests itself in their work and personal life. Their views and awareness can be characterized as boboitian and is considered to be timely for today's society.</p>