<p>This study investigates the most important reasons behind the rst-year students' decision to select University of the Western Cape (UWC) as higher education institution.<br /> These reasons were organized into a few factors for easy interpretation. The data to be analyzed for this project is a subsection of the data collected during the orientation period of 2008. During the orientation week of 2008, the questionnaires were completed on a voluntary basis by new rst-year students. All questionnaires were anonymously completed and therefore the data does not contain any information that could be linked to any individual. For the purpose of this study, only the black African and coloured students were considered. The other racial groups were not analyzed due to too small sample sizes. Questionnaires with missing information on the reasons for selecting UWC were not&nbsp / nalyzed. We ended up with a sample of size 600. The data were statistically analyzed, using descriptive statistics, bivariate analyses, factor analysis, coefficient of congruence and bootstrap factor analysis. The results indicated that the most important reasons aecting students to choose UWC were identied as good academic reputation, family member's advice, UWC graduates are successful and UWC graduates get good jobs. The least important reasons were found to be not accepted anywhere, parents / family members graduated from UWC, recruited by UWC and wanted to study near to home. The results also indicated that there were significant differences among students according to population groups, parent's monthly income and grade 12 average. Factor analysis of 12 variables yielded three extracted factors upon which student decisions were based. Similarities of these three factors were tested, and a high similarity among demographic characteristics and grade 12 average were found. Additional analyses were conducted to measure the accuracy of factor analyses models constructed using Spearman and Polychoric correlation matrices. The results indicated that both correlation matrices were&nbsp / nbiased, with higher variance and higher loadings when the Polychoric correlation matrix was used to construct a factor analysis model for categorical data.</p>

<p>This study investigates the most important reasons behind the rst-year students' decision to select University of the Western Cape (UWC) as higher education institution.<br /> These reasons were organized into a few factors for easy interpretation. The data to be analyzed for this project is a subsection of the data collected during the orientation period of 2008. During the orientation week of 2008, the questionnaires were completed on a voluntary basis by new rst-year students. All questionnaires were anonymously completed and therefore the data does not contain any information that could be linked to any individual. For the purpose of this study, only the black African and coloured students were considered. The other racial groups were not analyzed due to too small sample sizes. Questionnaires with missing information on the reasons for selecting UWC were not&nbsp / nalyzed. We ended up with a sample of size 600. The data were statistically analyzed, using descriptive statistics, bivariate analyses, factor analysis, coefficient of congruence and bootstrap factor analysis. The results indicated that the most important reasons aecting students to choose UWC were identied as good academic reputation, family member's advice, UWC graduates are successful and UWC graduates get good jobs. The least important reasons were found to be not accepted anywhere, parents / family members graduated from UWC, recruited by UWC and wanted to study near to home. The results also indicated that there were significant differences among students according to population groups, parent's monthly income and grade 12 average. Factor analysis of 12 variables yielded three extracted factors upon which student decisions were based. Similarities of these three factors were tested, and a high similarity among demographic characteristics and grade 12 average were found. Additional analyses were conducted to measure the accuracy of factor analyses models constructed using Spearman and Polychoric correlation matrices. The results indicated that both correlation matrices were&nbsp / nbiased, with higher variance and higher loadings when the Polychoric correlation matrix was used to construct a factor analysis model for categorical data.</p>

In this thesis, an analysis of Bitcoin, Monero price and volatility is conducted with respect to S&amp;P500 and the VIX index. Moreover using Python, we computed correlation coefficients of nine cryptocurrencies with two different approaches: Pearson and Spearman from July 2016 -July 2018. Moreover the Pearson correlation coefficient was computed for each year from July2016 - July 2017 - July 2018. It has been concluded that in 2016 the correlation between the selected cryptocurrencies was very weak - almost none, but in 2017 the correlation increased and became moderate positive. In 2018, almost all of the cryptocurrencies were highly correlated. For example, from January until July of 2018, the Bitcoin - Monero correlation was 0.86 and Bitcoin - Ethereum was 0.82.

Magister Scientiae - MSc / This study investigates the most important reasons behind the rst-year students' decision to select University of the Western Cape (UWC) as higher education institution. These reasons were organized into a few factors for easy interpretation. The data to be analyzed for this project is a subsection of the data collected during the orientation period of 2008. During the orientation week of 2008, the questionnaires were completed on a voluntary basis by new rst-year students. All questionnaires were anonymously completed and therefore the data does not contain any information that could be linked to any individual. For the purpose of this study, only the black African and coloured students were considered. The other racial groups were not analyzed due to too small sample sizes. Questionnaires with missing information on the reasons for selecting UWC were not nalyzed. We ended up with a sample of size 600. The data were statistically analyzed, using descriptive statistics, bivariate analyses, factor analysis, coefficient of congruence and bootstrap factor analysis. The results indicated that the most important reasons a ecting students to choose UWC were identi ed as good academic reputation, family member's advice, UWC graduates are successful and UWC graduates get good jobs. The least important reasons were found to be not accepted anywhere, parents / family members graduated from UWC, recruited by UWC and wanted to study near to home. The results also indicated that there were significant differences among students according to population groups, parent's monthly income and grade 12 average. Factor analysis of 12 variables yielded three extracted factors upon which student decisions were based. Similarities of these three factors were tested, and a high similarity among demographic characteristics and grade 12 average were found. Additional analyses were conducted to measure the accuracy of factor analyses models constructed using Spearman and Polychoric correlation matrices. The results indicated that both correlation matrices were&nbsp; nbiased, with higher variance and higher loadings when the Polychoric correlation matrix was used to construct a factor analysis model for categorical data. / South Africa

The research is dealing with the problem of choosing right statistical criteria in order to analyze the concrete educological research information and to interpret the received results. The practical value of the research’s results could be described as: 1- the exact evaluation of the real situation, by researching the effectiveness of use of educational computer means at the lessons of mathematics, with the help of hypothesises; 2- the statistical criteria received at the time of the research can be used at the concrete future researches.

Conselho Nacional de Desenvolvimento Científico e Tecnológico / The study of adaptability and yield stability allows to identify cultivars adapted to specific conditions and wide, stable and with high grain yield, and can be realized as the different methodologies employed. Thus, the aim of this study was to investigate the association between adaptability and stability methods for grain yield in common bean cultivars and identify the most promising for cultivation in State of Rio Grande do Sul. For this, were used the Eberhart and Russell (1966), the Cruz et al. (1989), the additive main effects and multiplicative interaction analysis (AMMI), the Lin and Binns (1988) modified and the Annicchiarico (1992) methods, and, in the first three methods, the average grain yield was considered as a parameter of stability and adaptability. The competition trials of common bean cultivars were conducted in randomized block experimental design with three replications, during the growing seasons from 2000 to 2008, in ten different places of Rio Grande do Sul State, in different crop cultivation and harvesting period, totaling 24 experiments. We evaluated 12 common bean cultivars of different commercial groups (black, carioca and cranberry): Diamante Negro, Guapo Brilhante, Guateian 6662, IAPAR 44, Macanudo, Macotaço, Minuano, Tio Tibagi, TPS Nobre, Carioca, Pérola and Iraí. Significative cultivars x environments interaction were observed for grain yield. The TPS Nobre cultivar was the one closest to praised as ideal behavior, except for the AMMI analysis. While the Iraí cultivar was unstable and had low grain yield in Rio Grande do Sul State. Significative correlation was observed among Eberhart and Russell (1966), Cruz et al. (1989), AMMI, Lin and Binns (1988) modified and Annicchiarico (1992) methods, demonstrating that they are associated. Lin and Binns (1988) modified (rs = 0.95) and Annicchiarico (1992) (rs = 0.97) methodologies were highly correlated with the average grain yield and allowed the identification of stable and adapted cultivars among the most productive. Thus, in analysis of adaptability and stability for common bean crop, when the average grain yield is considered as a parameter of adaptability and stability, not recommended to use together of Eberhart and Russell (1966), Cruz et al. (1989), AMMI, Lin and Binns (1988) modified and Annicchiarico (1992) methods. Choosing the most appropriate method is at the discretion of the researcher. / O estudo da adaptabilidade e da estabilidade para produtividade de grãos permite a identificação de cultivares adaptadas às condições específicas ou amplas, estáveis e de elevada produtividade de grãos e pode ser realizado com o emprego de diferentes metodologias. Assim, o objetivo deste trabalho foi verificar a associação entre métodos de adaptabilidade e de estabilidade para a produtividade de grãos em feijão e identificar cultivares mais promissoras para o cultivo no Estado do Rio Grande do Sul. Para tanto, foram utilizados os métodos de Eberhart e Russell (1966), de Cruz et al. (1989), da análise dos efeitos principais aditivos e da interação multiplicativa (AMMI), de Lin e Binns (1988) modificado e de Annicchiarico (1992), sendo que, nas três primeiras metodologias, a média de produtividade de grãos foi considerada como um parâmetro de adaptabilidade e estabilidade. Os experimentos de competição de cultivares de feijão foram realizados no delineamento experimental de blocos ao acaso, com três repetições, durante os anos agrícolas de 2000 a 2008, em nove municípios do Estado do Rio Grande do Sul, em cultivo de safra e de safrinha, totalizando 24 experimentos. Foram avaliadas 12 cultivares de feijão de diferentes grupos comerciais (preto, carioca e manteigão): Diamante Negro, Guapo Brilhante, Guateian 6662, IAPAR 44, Macanudo, Macotaço, Minuano, Tio Tibagi, TPS Nobre, Carioca, Pérola e Iraí. Houve interação cultivares com ambientes significativa para a produtividade de grãos. A cultivar TPS Nobre foi a que mais se aproximou do comportamento preconizado como ideal, exceto pela análise AMMI. Já a cultivar Iraí foi instável e apresentou baixa produtividade de grãos no Estado do Rio Grande do Sul. Correlação significativa foi observada entres os métodos de Eberhart e Russell (1966), de Cruz et al. (1989), AMMI, de Lin e Binns (1988) modificado e de Annicchiarico (1992), demonstrando que estes estão associados. As metodologias de Lin e Binns (1988) modificado (rs = 0,95) e de Annicchiarico (1992) (rs = 0,97) foram altamente correlacionadas com as médias de produtividade de grãos e permitiram a identificação de cultivares estáveis e adaptadas entre as mais produtivas. Deste modo, em análise de adaptabilidade e de estabilidade na cultura do feijão, quando a média da produtividade de grãos é considerada como um parâmetro de adaptabilidade e de estabilidade, não se recomenda o uso conjunto dos métodos de Eberhart e Russell (1966), de Cruz et al. (1989), AMMI, de Lin e Binns (1988) modificado e de Annicchiarico (1992). A escolha do método mais adequado fica a critério do pesquisador.

Title: Influence of selected conditional factors on performance in white water slalom. Aims: The aim of the study was to investigate the relationship of selected specific movement abilities being examined modified test battery with the performance of athletes in the water slalom. Methods: It was used field measurements where the applied modified test battery. Using GPS module to determine the distance partial tests from batery. For measuring was used manual measurement. To determine the statistical correlation between the modified battery and performance ability of competitors was used two different coefficients of correlation and regression analysis. According to the order of the test and the race was used nonparametric correlation study - Spearman correlation coefficient. Determining the statistical significance of the relationship of measured times in tests and final time in the nomination races have used the Pearson correlation coefficient. Results: For a statistically significant relationship was determined value when r ≥ 0.8. Spearman's correlation coefficient: In the test at 40 m were obtained these correlation coefficients: Nomination races rs = 0,380952, Czech cup rs = 0,595238. In the test at 80 meters they were obtained these correlation coefficients: nomination races rs = 0,857143,...

<p>  </p> <p>Available literature indicated that construction projects have been experiencing significant time overruns from their planned duration. In many cases, the primary reasons for project delays were ineffective planning and scheduling methods, poor communication and collaboration between the key stakeholders, and the construction operations and task requirements have been overlooked.</p> <p>Construction project planning and scheduling are extensively studied topics, and several techniques have been developed to solve construction project scheduling problems. Traditional production planning and scheduling techniques are based on the push planning strategy, such as linear and network scheduling techniques. In the traditional method, the project scheduler calculates activity durations and then sequences them to determine when to complete the work. These techniques and planning methods have been criticized for lacking collaboration between project workers and realistic integration of the project time, location, and other essential resources to create a reliable work schedule. Furthermore, the inability to account for site operations, tasks, and workflow leads to waste and delay. </p> <p>Consequently, Ballard and Howell (1990) proposed the last planner system, and then Ballard et al. (2000) further developed the method. In the last planner system, all key stakeholders and the project management team actively communicate and coordinate to accomplish the project’s planned milestones. The last planner system and pull planning scheduling objective is improving workflow and increasing plan reliability. However, the pull planning scheduling method has some limitations. For instance, this method cannot be used to determine the available work capacity in each working space and show how much work can be completed at a given time. In addition, the pull planning and LPS system are highly descriptive and experienced-based, relying on the decision and experiences of the site supervisors. </p> <p>Available literature concerning construction project delay also indicated that ineffective planning and scheduling, slow decision-making, and poor communication and coordination had been the top critical causes of construction project delay.</p> <p>Therefore, this research was conducted to minimize construction project time and cost overrun due to poor scheduling and production planning. The study has been conducted in two main parts. In the first part of this study, critical causes of project delay have been analyzed, and the contribution of poor planning and scheduling to construction project delays in different environments has been assessed. The relative importance index and Spearman’s coefficient techniques have been utilized to analyze the collected data.  The second section of this research work was conducted to investigate the construction scheduling reliability and production efficiency and developed the enhanced production planning and scheduling method to improve schedule reliability and production plan efficiency.  The reason for developing the enhanced production planning and scheduling method was to find the best work option to optimize work duration and efficiently plan required resources per category of the identified activities. In addition, this study has automated the scheduling input data capturing from the project BIM model by utilizing the developed visual program.</p> <p>The study finding in the first section indicated that the top ten critical causes of identified causes of project delay in specified environments were significantly different. However, Ineffective project planning and scheduling had been among the most critical causes in all three conditions. Ineffective planning and scheduling were ranked number one in developed environment conditions, second in developing countries, and fifth in high-risk environments.</p> <p>Study results in the second section have shown that the case study's executed schedule had experienced significant changes in the planned dates of individual tasks, project milestones, and resource allocation. The project schedule critical path and critical activities were changed repeatedly after each update, and the project structure work was delayed for 30 days from its initial plan, as illustrated in figures 33 to 35. Subsequently, the enhanced planning methodology has been applied in the selected case study to validate the developed method and evaluate the result of the case study. The case study implemented work plan has been compared with the enhanced planning-based developed schedule. The total duration of the enhanced planning-based method has been calculated to be 240 work days, which shows 30 days less time from implemented case study plan and 50 days from the project base plan in the construction document, as illustrated in figures 39 to 41. </p> <p>Furthermore, the production schedule sensitivity analysis has shown that the production schedule and the case study base plan tasks duration have not been significantly different since both schedules were created based on the similar size of the work crew, but in terms of the number of the planned task, the production schedule had been created based on the most suitable work option. Another advantage of the production schedule is that it is more reliable because the schedule is created for a shorter duration, not long before the project work starts, and it is created after multiple collaborations and assessment steps. In addition, the developed program in this study using Revit dynamo automated the extraction of input data from the BIM model to create the project schedule. </p> <p>In conclusion, based on the case study results, the enhanced production planning methodology and developed metrics and indices can be applied to various building construction projects to find the most suitable work option and create a reliable and resource-efficient work schedule. </p>