The pharmacy industry in Taiwan has produced a succession of great changes in recent years, for example: The National Health Insurance, the division of medical treatment & medication, Join the World Trade Organization(WTO) which were putting into practice. However, the competent authority which protects the medicine price and nuclear price policying, bringing a large impact to the pharmaceutical factory. So the promotion sales are effected first. So, for be able to improve the sales¡¦ performance & understand the releationship of human resources management activities & sales¡¦ performance, this research attempts to take one pharmacy company for an example, and using Multiple Regression Anylysis to research the effect of Selection, Traing & Education, Salary & Welfare, Position Alternation & Employees Participating to Sales Amount, Achievement Rat of Sales, Leave Application & Customer Developing. Besides this, it also using the Analysis of Variance to understand the difference of sales¡¦ performance under the situation of Cost Reducing & Human Resource Promoting. So according to the Multiple Regression Anylysis & Analysis of Variance, this research get the results as follow: 1.Traing & Education, Salary & Welfare, Employees Participating, the three Human Resource Management Activities have the significance effect to Sales¡¦ Performance. So the hypothesis H-1, H1-3 & H1-5 are all supported. 2.Selection also has the significance effect to Sales¡¦ Performance, but not all of factors can anticipate the Sales¡¦ Performance. So in accordance with the hypothesis H1-1 just present a partical supported result. 3.Position Alternation just has the significance effect to Sales Amount, Leave Application & Customer Developing, but not has the significance effect to Achievement Rat of Sales. So in accordance with the hypothesis H1-4 just present a partical supported result. 4.Under the difference Type of Human Resource Management, The sales¡¦s performance not only has the significance effect to Sales Amount & Customer Developing, but also has the higher performance. However, there are no significance about this difference. So in accordance with the hypothesis H2-1 & H2-2 just present a partical supported result. According to above-mentioned results, if we want to enhance some of sales¡¦ performances, we should enforance some of the optional human resources activities. This research also discover that: The higher salary & welfare structure & The type of human resources promoting, which have the significance effect to increase the sales¡¦ performance.

In this thesis, we first show that the performance of ranking and selection (R&S) procedures in steady-state simulations depends highly on the quality of the variance estimates that are used. We study the performance of R&S procedures using three variance estimators --- overlapping area, overlapping Cramer--von Mises, and overlapping modified jackknifed Durbin--Watson estimators --- that show better long-run performance than other estimators previously used in conjunction with R&S procedures for steady-state simulations. We devote additional study to the development of the new overlapping modified jackknifed Durbin--Watson estimator and demonstrate some of its useful properties. Next, we consider the problem of finding the best simulated system under a primary performance measure, while also satisfying stochastic constraints on secondary performance measures, known as constrained ranking and selection. We first present a new framework that allows certain systems to become dormant, halting sampling for those systems as the procedure continues. We also develop general procedures for constrained R&S that guarantee a nominal probability of correct selection, under any number of constraints and correlation across systems. In addition, we address new topics critical to efficiency of the these procedures, namely the allocation of error between feasibility check and selection, the use of common random numbers, and the cost of switching between simulated systems.

This thesis consists of two parts. The first part reviews a Variable Search, a variable selection procedure for mean modeling. The second part deals with variance modeling for robust parameter design in computer experiments. In the first chapter of my thesis, Variable Search (VS) technique developed by Shainin (1988) is reviewed. VS has received quite a bit of attention from experimenters in industry. It uses the experimenters' knowledge about the process, in terms of good and bad settings and their importance. In this technique, a few experiments are conducted first at the best and worst settings of the variables to ascertain that they are indeed different from each other. Experiments are then conducted sequentially in two stages, namely swapping and capping, to determine the significance of variables, one at a time. Finally after all the significant variables have been identified, the model is fit and the best settings are determined. The VS technique has not been analyzed thoroughly. In this report, we analyze each stage of the method mathematically. Each stage is formulated as a hypothesis test, and its performance expressed in terms of the model parameters. The performance of the VS technique is expressed as a function of the performances in each stage. Based on this, it is possible to compare its performance with the traditional techniques. The second and third chapters of my thesis deal with variance modeling for robust parameter design in computer experiments. Computer experiments based on engineering models might be used to explore process behavior if physical experiments (e.g. fabrication of nanoparticles) are costly or time consuming. Robust parameter design (RPD) is a key technique to improve process repeatability. Absence of replicates in computer experiments (e.g. Space Filling Design (SFD)) is a challenge in locating RPD solution. Recently, there have been studies (e.g. Bates et al. (2005), Chen et al. (2006), Dellino et al. (2010 and 2011), Giovagnoli and Romano (2008)) of RPD issues on computer experiments. Transmitted variance model (TVM) proposed by Shoemaker and Tsui. (1993) for physical experiments can be applied in computer simulations. The approaches stated above rely heavily on the estimated mean model because they obtain expressions for variance directly from mean models or by using them for generating replicates. Variance modeling based on some kind of replicates relies on the estimated mean model to a lesser extent. To the best of our knowledge, there is no rigorous research on variance modeling needed for RPD in computer experiments. We develop procedures for identifying variance models. First, we explore procedures to decide groups of pseudo replicates for variance modeling. A formal variance change-point procedure is developed to rigorously determine the replicate groups. Next, variance model is identified and estimated through a three-step variable selection procedure. Properties of the proposed method are investigated under various conditions through analytical and empirical studies. In particular, impact of correlated response on the performance is discussed.

Thesis--University of Oklahoma. / Includes bibliographical references (leaves 64-65).

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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.