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The work for data analysis of the main methods, fullfilled in Computer mathematic systems (CMS), analysis. Also analysing and comparison of the data analysis of CMS – MAPLE, MATLAB and MATHCAD. There‘s briefly described enter and reading of data, characteristics of statistic data, analysis of variance, regression, interpolation and correlation. In the last section of data system analysis possibilities according to its sophistication, comfortable usage and variety of data function fullfillment. You will finde the examples of solved tasks with CMS after each description of data analysis method.

The current work deals with modelling longitudinal or repeated non-Gaussian measurements for a respiratory disease. The analysis of longitudinal data for non-Gaussian binary disease outcome data can broadly be modeled using three different approaches; the marginal, random effects and transition models. The marginal type model is used if one is interested in estimating population averaged effects such as whether a treatment works or not on an average individual. On the other hand random effects models are important if apart from measuring population averaged effects a researcher is also interested in subject specific effects. In this case to get marginal effects from the subject-specific model we integrate out the random effects. Transition models are also called conditional models as a general term. Thus all the three types of models are important in understanding the effects of covariates and disease progression and distribution of outcomes in a population. In the current work the three models have been researched on and fitted to data. The random effects or subject-specific model is further modified to relax the assumption that the random effects should be strictly normal. This leads to the so called hierarchical generalized linear model (HGLM) based on the h-likelihood formulation suggested by Lee and Nelder (1996). The marginal model was fitted using generalized estimating equations (GEE) using PROC GENMOD in SAS. The random effects model was fitted using PROC GLIMMIX and PROC NLMIXED in SAS (generalized linear mixed model). The latter approach was found to be more flexible except for the need of specifying initial parameter values. The transition model was used to capture the dependence between outcomes in particular the dependence of the current response or outcome on the previous response and fitted using PROC GENMOD. The HGLM was fitted using the GENSTAT software. Longitudinal disease outcome data can provide real and reliable data to model disease progression in the sense that it can be used to estimate important disease i parameters such as prevalence, incidence and others such as the force of infection. Problem associated with longitudinal data include loss of information due to loss to follow up such as dropout and missing data in general. In some cases cross-sectional data can be used to find the required estimates but longitudinal data is more efficient but may require more time, effort and cost to collect. However the successful estimation of a given parameter or function depends on the availability of the relevant data for it. It is sometimes impossible to estimate a parameter of interest if the data cannot its estimation. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2011.

National cultures play a critical role in effective leadership and organisational success in international businesses. Contemporary organisations are therefore challenged by cross-cultural leadership needs to address increasing diversity. In the past decades, there has been a growing interest in studies of non-Western leadership. Recent papers have focused particularly on leadership in the Asia-Pacific region. This paper compares leadership in China and New Zealand, based on the data collected by using the GLOBE (Global Leadership and Organisational Behaviour Effectiveness) project leadership questionnaire. In addition, the paper reviews the main findings of previous research in order to investigate the similarities and differences in preferred leadership behaviours/characteristics in both cultures, while attempting to examine the consistency of current findings against the previous GLOBE studies. Significant differences of leadership behaviours between China and New Zealand have been identified. This paper generally supports the findings of previous GLOBE studies that charismatic/value-based leadership is the universally contributing factor towards outstanding leadership and self-protective leadership is the universally negative factor against leadership excellence. Findings in this paper may contribute to further understanding of leadership practice in these two countries. Future research should be undertaken to examine the effects of cultural differences on organisational practice by a more comprehensive research method.

Thesis (Ph.D.)--McMaster University (Canada), 1995. / Source: Dissertation Abstracts International, Volume: 56-12, Section: B, page: 6848. Adviser: M. L. Tiku.

Thesis (M.Ed.)--Texas State University-San Marcos, 2007. / Vita. Appendices: leaves 126-137. Includes bibliographical references (leaves 138-145).

Thesis (M.A.)--Carleton University, 2004. / Includes bibliographical references (p. 207-271). Also available in electronic format on the Internet.

Thesis (M.A.)--Carleton University, 2004. / Includes bibliographical references (p. 73-77). Also available in electronic format on the Internet.