The financial crises and bank runs in the past decade increased attention to the financial systems. In Turkey as in Europe banks are main financial intermediaries and financial crises occur mostly due to realization of risks in banks. Although liquidity risk is embedded into daily operations of banks unless controlled it may take banks into insolvency and even bankruptcy. This thesis aims to examine liquidity risk structure of Turkish banking sector. As a sample the domestic commercial banks in Turkey is chosen. The risk profile of the sector is examined by using a ratio analysis. The accounting figures in balance sheets and income statements of banks are employed for statistical analysis about liquidity risk of the sector. The means of liquidity ratios among different groups of banks are compared via analysis of variance. Moreover relation between liquidity risk and return in the sector is analysed by using panel data regressions.

Recent changes in the insurance regulations and laws in Turkey lead insurance industry to gain further importance and turn the insurance industry one of the rising sectors in financial markets. However, while these favorable events for the insurance industry takes place in Turkish markets, on the global markets the economic crisis initiated with the collapse of US sub-prime mortgage markets deepened and the credit crunch arose in the aftermath. In the times of credit stress having good liquidity base is important for the firms. In recent economic crisis, liquidity related troubles resulted in bailouts or takeovers of giant financial companies. In order to prevent negative consequences of inadequate liquidity and to sustain financial stability, having appropriate level of liquidity is especially crucial for financial companies like insurers. In this thesis it is aimed to analyze the Turkish insurance sector&rsquo / s liquidity condition for the period between 2002 and 2008 with the help of liquidity ratios. Considering the nature of the business, a distinction is made between &ldquo / non-life&rdquo / and &ldquo / life&rdquo / insurance companies while assessing their liquidity ratios. Furthermore, panel data regression analysis is conducted to determine the firm specific factors affecting the liquidity decisions of non-life insurers operating in Turkey.

Noncompliance to treatment allocation is likely to complicate estimation of causal effects in clinical trials. The ubiquitous nonrandom phenomenon of noncompliance renders per-protocol and as- treated analyses or even simple regression adjustments for noncompliance inadequate for causal inference. For survival data, several specialist methods have been developed when noncompliance is related to risk. The Causal Accelerated Life Model (CALM) allows time-dependent departures from randomized treatment in either arm and relates each observed event time to a potential event time that would have been observed if the control treatment had been given throughout the trial. Alternatively, the structural Proportional Hazards (C-Prophet) model accounts for all-or-nothing noncompliance in the treatment arm only while the CHARM estimator allows time-dependent departures from randomized treatment by considering survival outcome as a sequence of binary outcomes to provide an 'approximate' overall hazard ratio estimate which is adjusted for compliance. The problem of efficacy estimation is compounded for two-active treatment trials (additional noncompliance) where the ITT estimate provides a biased estimator for the true hazard ratio even under homogeneous treatment effects assumption. Using plausible arm-specific predictors of compliance, principal stratification methods can be applied to obtain principal effects for each stratum. The present work applies the above methods to data from the Esprit trials study which was conducted to ascertain whether or not unopposed oestrogen (hormone replacement therapy - HRT) reduced the risk of further cardiac events in postmenopausal women who survive a first myocardial infarction. We use statistically designed simulation studies to evaluate the performance of these methods in terms of bias and 95% confidence interval coverage. We also apply a principal stratification method to adjust for noncompliance in two treatment arms trial originally developed for binary data for survival analysis in terms of causal risk ratio. In a Bayesian framework, we apply the method to Esprit data to account for noncompliance in both treatment arms and estimate principal effects. We apply statistically designed simulation studies to evaluate the performance of the method in terms of bias in the causal effect estimates for each stratum. ITT analysis of the Esprit data showed the effects of taking HRT tablets was not statistically significantly different from placebo for both all cause mortality and myocardial reinfarction outcomes. Average compliance rate for HRT treatment was 43% and compliance rate decreased as the study progressed. CHARM and C-Prophet methods produced similar results but CALM performed best for Esprit: suggesting HRT would reduce risk of death by 50%. Simulation studies comparing the methods suggested that while both C-Prophet and CHARM methods performed equally well in terms of bias, the CALM method performed best in terms of both bias and 95% confidence interval coverage albeit with the largest RMSE. The principal stratification method failed for the Esprit study possibly due to the strong distribution assumption implicit in the method and lack of adequate compliance information in the data which produced large 95% credible intervals for the principal effect estimates. For moderate value of sensitivity parameter, principal stratification results suggested compliance with HRT tablets relative to placebo would reduce risk of mortality by 43% among the most compliant. Simulation studies on performance of this method showed narrower corresponding mean 95% credible intervals corresponding to the the causal risk ratio estimates for this subgroup compared to other strata. However, the results were sensitive to the unknown sensitivity parameter.

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no / Medical statistics is a vast and ever-growing field of academic endeavour, with direct application to developing the robustness of the evidence base in all areas of medicine. Although the complexity of available statistical techniques has continued to increase, fuelled by the rapid data processing capabilities of even desktop/laptop computers, medical practitioners can go a long way towards creating, critically evaluating and assimilating this evidence with an understanding of just a few key statistical concepts. While the concepts of statistics and ethics are not common bedfellows, it should be emphasised that a statistically flawed study is also an unethical study.[1] This review will outline some of these key concepts and explain how to interpret the output of some commonly used statistical analyses. Examples will be confined to two-group tests on independent samples, using both a continuous and a dichotomous/binary outcome measure.

Although there are strong opinions both for and against identifying exceptional children, it is important school divisions as well as schools ensure that student sub groups are not overrepresented in special education. The purpose of this study was to determine the representation of students in special education by category (SWD, ED, SLD, and ID) and by demographic identifier (Ethnicity, Gender, SES) for 2008-2009 and to determine changes in representation between 2008-2009 and 2013-2014 in the 132 school divisions in the Commonwealth of Virginia. The 2013-2014 data reported in the Casey (2017) study were used in this study. In addition, this study examined specific disability categories by race, gender, and socioeconomic status from all 132 Virginia public school divisions and compared data from 2008-2009 to data from 2013-2014. Data for the study were retrieved from the Virginia Department of Education (VDOE) related to specific categories. Findings included a comparison of the data for 2008-2009 and 2013-2014. In 2008-2009 some data were missing, which may have contributed to the limited findings. Overrepresentation was determined through the use of the standard 10 formula for overrepresentation. Findings for 2008-2009 revealed that Black students were identified as over represented in the area of Intellectual Disabilities (ID) in 2% of the school divisions in the Commonwealth of Virginia. In the school year 2008-2009, 0% of school divisions identified Hispanic students as disproportionate in the areas of ID, Students with Disabilities (SWD), Specific Learning Disabilities (SLD), and Emotionally Disturbed (ED). In the 2008-2009 school year, 2% of the school divisions in Virginia identified economically disadvantaged students as disproportionate in the disability category SWD, and 20% of the school divisions identified economically disadvantaged students as disproportionate in the disability category SLD. Additionally, 17% of the school divisions identified economically disadvantaged students as disproportionate in the disability category ED, and 38% percent of the school divisions in Virginia identified economically disadvantaged students as disproportionate in the disability category ID. The 2008-2009 data, when compared to the 2013-2014 data, were less disproportionate. / EDD / The accurate identification of students for special education is necessary both for school divisions and for students. Thus, it is important that school divisions as well as schools ensure student sub groups are not overrepresented in special education. The purpose of this study was to determine representation of students in special education by category (SWD, ED, SLD, and ID) and by demographic variables (Ethnicity, Gender, SES) for 2008-2009, and to determine changes in representation between 2008-2009 and 2013-2014 in the 132 school divisions in the Commonwealth of Virginia. The study utilized 2013-2014 data reported in the Casey (2017) study. Data from the Virginia Department of Education (VDOE) were used for the study. Analysis of the data revealed less disproportionate representation in each of the groups examined in 2008-2009 when compared to the data reported by Casey (2017). In 2008-2009 some data were missing, which may have contributed to a lack of over representation in several areas. While Blacks were overidentifed in ID in a few divisions in 2008-2009, Economically disadvantaged students were overidentified in a larger number of school divisions in SLD, ED, and ID. However, the 2008-2009 data, when compared to the 2013-2014 data, indicated less disproportionality.

For more than three decades, research has shown that the special education referral, identification, and placement processes can be discriminatory (Artiles, Rueda, Salazar, and Higareda, 2005). Proportionately, there are more minority students of low socio-economic status than are White students in special education categories that are the high incidence special education categories. Students with high-incidence disabilities make up 80% of all students with disabilities. High-Incidence disabilities share these characteristics, (1) often hard to distinguish from students without disabilities, especially in non-school settings, (2) often display a combination of academic, and (3) behavioral and social problems (Friend and Bursuck, 2012). Special Education licensure and endorsement varies from state to state, as does the terminology used in describing the knowledge that is required and the students to whom it applies. In most states students identified for special education services for the categories of Intellectual Disability (ID), Specific Learning Disability (SLD) and students with Emotional Disturbance (ED) are identified as high- incidence disabilities. The purpose of this study was to investigate the status of relative representation of male and female, minority, and low socioeconomic students with disabilities served in special education programs across 132 public school divisions within eight Superintendent']s regions in the 2013-14 school year, as published by the Virginia Department of Education. Data from the Virginia Department Education (VDOE) were analyzed to present an analysis of three categories of the special education populations (ID, SLD and ED), racial, (Black and Hispanic) and socio-economic status. Findings indicated that fewer than 50% of the school divisions in the Commonwealth of Virginia showed disproportionality, however when examined by Superintendent's regions there were some geographical areas that showed some concentrations of disproportionality. / Ed. D.

Problem When choosing topic for this study the economy was on the brink of a recession. Many experts made varying statements regarding this fact, and further readings in this area led us to question: can an in- clusion of gold enhance the performance in an index portfolio dur- ing recessions? And if so, how much should be allocated to gold? Purpose The purpose of this thesis is to look back at the historical price de- velopment of gold and DJIA during recessions in order to find out whether an inclusion of gold can improve a DJIA index portfolio held in today’s recession. In addition, by analyzing the risks and pos- sibilities with gold, the optimal allocation of gold in a DJIA portfolio will be investigated in.   Method The methodological approach will be of a quantitative data analysis approach. By using historical data, new empirical findings will be found by using the deductive approach. This method has been cho- sen due to the nature of the purpose and in order to best give a gen- eral answer to our research questions. Conclusion The gold price is strongly influenced by uncertainty, and even though an optimal allocation of gold in each recession could be found, no general optimal allocation applicable in today’s recession could be found. Gold has higher risk (higher variance) than DJIA, but is compensated with higher return as well.

<p><strong>Problem</strong></p><p>When choosing topic for this study the economy was on the brink of a recession. Many experts made varying statements regarding this fact, and further readings in this area led us to question: can an in- clusion of gold enhance the performance in an index portfolio dur- ing recessions? And if so, how much should be allocated to gold?</p><p><strong>Purpose</strong></p><p>The purpose of this thesis is to look back at the historical price de- velopment of gold and DJIA during recessions in order to find out whether an inclusion of gold can improve a DJIA index portfolio held in today’s recession. In addition, by analyzing the risks and pos- sibilities with gold, the optimal allocation of gold in a DJIA portfolio will be investigated in.</p><p> </p><p><strong>Method</strong></p><p>The methodological approach will be of a quantitative data analysis approach. By using historical data, new empirical findings will be found by using the deductive approach. This method has been cho- sen due to the nature of the purpose and in order to best give a gen- eral answer to our research questions.</p><p><strong>Conclusion</strong></p><p>The gold price is strongly influenced by uncertainty, and even though an optimal allocation of gold in each recession could be found, no general optimal allocation applicable in today’s recession could be found. Gold has higher risk (higher variance) than DJIA, but is compensated with higher return as well.</p>

Historically, students from ethnically diverse backgrounds in grades K-12 have been over-represented in special education, yet little research on disproportionate representation has been conducted with preschool-aged children. This study examined if 72,525 preschool-aged children with disabilities from ethnically diverse backgrounds were disproportionately represented in special education within and across five southern states. Data were gathered from the 2006 December 1st Child Count reported by each State Department of Education to the U.S. Department of Education. Chosen states offered state-funded pre-kindergarten programs, which should have provided equal opportunities for inclusion across states. Analyses compared children with disabilities for disproportionate representation across state of residence, across special education eligibilities, across educational placements, and amount of inclusion provided. Data were analyzed for child and placement characteristics. Due to data suppression by individual states, analyses were conducted using children from Black and White backgrounds, and children from Hispanic backgrounds were used when reported by individual states. Child characteristics considered included the child’s: (a) type of disability eligibility category, (b) age, and (c) ethnicity. Placement characteristics included: (a) type of educational placement, (b) state in which child resided, and (c) amount of inclusion received. Indices of disproportionate representation were calculated using: (a) composition index, (b) risk index, (c) odds ratio, and (d) relative risk ratio. A 3 x 5 ANOVA was used to calculate placement differences between states. Factorial analysis was used to calculate determinants of placement status for preschool-aged children with disabilities. Results revealed disproportionate representation does occur at the preschool level, although between state variability was great, and patterns differed from the K-12 literature. Children from American Indian backgrounds were over-represented due to high proportions in states of Alabama and North Carolina, while children from Asian and Hispanic backgrounds were under-represented. Children from Black and White backgrounds were represented in special education at expected rates. The most common eligibility categories were speech/language impairments and developmental delay. Placement results revealed over-representation for White preschoolers and males, although type of state-funded pre-k program was a non-significant factor. Inclusion analyses favored Whites and males. Child demographic factors explained the majority of variability in inclusion status.