This research focuses on characterizing a potato with extra sugar content and identifying the location and depth of the extra sugar content using the active thermography imaging technique. The extra sugar content of the potato is an important problem for potato growers and potato chip manufacturers. Extra sugar content could result in diseases or wounds in the potato tuber. In general, potato tubers with low sugar content are considered as having a higher quality. The inspection system and general methodologies characterizing extra sugar content will be presented in this study. The average heating rate obtained from the thermal image analysis is the major factor in characterization procedures. Using information on the average heating rate, the probability of achieving a potato with extra sugar content may be predicted using the logistic regression model. In addition, neural networks are also used to identify the potato with extra sugar contents. The correct rate for identifying a potato with extra sugar content in it can reach 85%. The location of extra sugar content can also be found using the logistic regression model. Results show the overall correct rate predicting the extra sugar content location with a resolution of 20 by 20 pixels is 91%. In predicting the extra sugar content depth, amounts exceeds 2/3 inches are not detectable by analyzing thermal images. The depth of extra sugar content can be discriminated in 0.3 inch increments with a high rate of accuracy (87.5%).

The relationship between precipitation and large-flow is important to understand and characterize in the climate system. We examine statistical relationships between the Tropical Rainfall Measurement Mission (TRMM) 3B42 gridded precipitation and large-scale ow variables in the Tropics for 2000{2007. These variables include NCEP/NCAR Re-analysis sea surface temperatures (SSTs), vertical temperature pro files, omega, and moist static energy, as well as Atmospheric Infrared Sounder (AIRS) vertical temperatures and QuikSCAT surface divergence. We perform correlation analysis, empirical orthogonal function analysis, and logistic regression analysis on monthly, pentad, daily and near-instantaneous time scales. Logistic regression analysis is able to incorporate the non-linear nature of precipitation in the relation- ship. Flow variables are interpolated to the 0.25 degrees TRMM 3B42 grid and examined separately for each month to o set the effects of the seasonal cycle. January correlations of NCEP/NCAR Re-analysis SSTs and TRMM 3B42 precipitation have a coherent area of positive correlations in the Western and Central Tropical Pacific on all time scales. These areas correspond with the South Pacific Convergence Zone (SPCZ) and the Inter Tropical Convergence Zone (ITCZ). 500mb omega is negatively correlated with TRMM 3B42 precipitation across the Tropics on all time scales. QuikSCAT divergence correlations with precipitation have a band of weak and noisy correlations along the ITCZ on monthly time scales in January. Moist static energy, calculated from NCEP/NCAR Re-analysis has a large area of negative correlations with precipitation in the Central Tropical Pacific on all four time scales. The first few Empirical Orthogonal Functions (EOFs) of vertical temperature profiles in the Tropical Pacific have similar structure on monthly, pentad, and daily timescales. Logistic regression fit coefficients are large for SST and precipitation in four regions located across the Tropical Pacific. These areas show clear thresholded behavior. Logistic regression results for other variables and precipitation are less clear. The results from SST and precipitation logistic regression analysis indicate the potential usefulness of logistic regression as a non-linear statistic relating precipitation and certain ow variables.

This study includes six dimensions: article¡¦s attributions, author¡¦s traits, the identities of censors, the opinions of censors, the results of judgment and the processes of publication; uses content analysis to describe the present editorial situation of Journal of Human Resource Management (JHRM) and constructs the regression model by the logistic regression using four dimensions except the results of judgment and the processes of publication. The findings indicate two sides, the first one is on the content analysis: it has a significant difference between the year of contribution and references, article¡¦s issue, total pages, number of authors, days from submission to acceptance, days from submission to publication; and different academic title also affect the article¡¦s research method. The second one is on the effect factor of the article is published or not: article¡¦s attributions and author¡¦s traits, including 16 variables, have no any effects on the acceptance of an article, but the identities of censors and the opinions of censors, including 8 variables, do have significant effects on it. The important factors to the article are the number of censors, the application of entirety, the innovation of deliverance and the completeness of viewpoint and as the order for influencing the acceptance of an article.

In the thesis, we study the data collected from VICU of Kaohsiung Veterans General Hospital.The data consists of the following two kinds. The data of the first kind is ten minute ECG data with sampling frequency 500Hz. The data of the second kind is long term one minute average heart rate. There are two main topics studies in this thesis. In the first part, we consider the standard deviations which be taked log and medians of RR intervals, PR intervals and RT intervals, respectively derived from first kind data. By considering the odds ratio¡Bsensitivity and specificity, and we select the heart classification vectors. The object is to classify among normal and ventricular heart disease (VSD), normal and ventricular heart disease with congestive heart failure (VSD+CHF). We proposed a method of both variables and two-step classification procedure. Furthermore, we built the logistic regression models. In the second part, we derive the distribution of the spectrum power ratio of low frequency to high frequency. Shewhart control chart and Tabular cusum chart are constructed to monitor RG. Also, we discover the periodicity of RG. Finally, there exists positive correlation between RA¡]or RG¡^ and autocorrelation of lag one of RR interval.

Sample size calculation methods for ordinal logistic regression are proposed to test statistical hypotheses. The author was motivated to do this work by the need for statistical analysis of the red imported fire ants data. The proposed methods use the concept of approximation by the moment-generating function. Some correction methods are also suggested. When a prior data set is available, an empirical method is explored. Application of the proposed methodology to the fire ant mating flight data is demonstrated. The proposed sample size and power calculation methods are applied in the hypothesis testing problems. Simulation studies are also conducted to illustrate their performance and to compare them with existing methods.

<p>Today's young people are well-educated ever but in a poor employment situation. At the beginning of this paper, I first state the situation both in the world and in China, revealing the poor employment situation of youth. Then I introduce systems related to youth employment in China and measures the government taken to help graduate students to find a job. The purpose of this paper is to analyze employment of youth people in China especially among the medium and highly educated people and find which and how the factors contribute to it. By using the Logistic Regression by STATA, I find that the main factors are gender, age, living area, and political status, major and educational level. The result reveals that the discrimination and gap between rural and urban area are severe issues in China. Last but not least, I give some suggestions both to the society and the individual to improve the youth employment.</p>

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: FDR; logistic regression; microarray. Includes bibliographical references (leaf 26).

Theses (Ed. D.)--Marshall University, 2005. / Title from document title page. Includes abstract. Document formatted into pages: contains ix, 180 p. Bibliography: p. 121-129.

Genome-wide association studies have been used extensively to study hundreds of phenotypes and have determined thousands of associated SNPs whose underlying biology and causation is as yet largely unknown. Many previous studies attempted to clarify the causal biology by investigating overlaps of trait-associated variants with functional annotations, but lacked statistical rigor and examined incomplete subsets of available functional annotations. Additionally, it has been difficult to disentangle the relative contributions of different annotations that may show strong correlations with one another. In this thesis, we address these shortcomings and strengthen and extend the obtained results. Two methods, permutations and logistic regression, are applied in statistically rigorous analyses of genomic annotations and their observed enrichment or depletion of trait-associated SNPs. The genomic annotations range from genic regions and regulatory features to measures of conservation and aspects of chromatin structure. Logistic regressions in a number of trait-specific subsets identify genomic annotations influencing SNPs associated with both normal variation (e.g., eye or hair colour) and diseases, suggesting some generalities in the biological underpinnings of phenotypes. SNPs associated with phenotypes of the immune system are investigated and the results highlight the distinct aetiology for this subset. Despite the heterogeneity of the studied cancers, SNPs associated to different cancers are particularly enriched for conserved regions, unlike all other trait-subsets. Nonetheless, chromatin states are, perhaps surprisingly, among the most influential genomic annotations in all trait-subsets. Evolutionary conserved regions are rarely within the top genomic annotations despite their widespread use in prioritisation methods for follow-up studies. We identify a common set of enriched or depleted genomic annotations that significantly influence all traits, but also highlight trait-­‐specific differences. These annotations may be used for the computational prioritisation of variants implicated in phenotypes of interest. The approaches developed for this thesis are further applied to studies of a specific human complex trait (height) and gene expression in atherosclerosis.

This work investigates the factors that contribute to financial crises. We first study the Dow Jones index performance by grouping the daily adjusted closing value into a two-month window and finding several critical quantiles in each window. Then, we identify severe downturn in these quantiles and find that the 5th quantile is the best to identify financial crises. We then matched these quantiles with historical financial crises and gave a basic explanation about them. Next, we introduced all exogenous factors that could be related to the crises. Then, we applied a rapid Bayesian variable selection technique - Stochastic Search Variable Selection (SSVS) using a Bayesian logistic regression model. Finally, we analyzed the result of SSVS, leading to the conclusion that that the dummy variable we created for disastrous hurricane, crude oil price and gold price (GOLD) should be included in the model. / text