Smoking rates among senior students have been related to smoking initiation in younger students. Opportunities to select smoking friends may be one explanation, however our understanding of this process has been limited by cross-sectional designs. <br ><br /> The purpose of this longitudinal study was to determine whether senior student smoking rates a) predict smoking initiation in younger elementary school students, controlling for individual exposure to family and friends who smoke and b) are related to the selection of smoking friends, increasing risk of smoking initiation as a result. <br ><br /> This study involved secondary data analysis of 2798 students from 84 Ontario elementary schools involved with the Third Waterloo Smoking Prevention Project (WSPP3). Grade 8 students completed a questionnaire at baseline to obtain the percentage of senior students who smoke in each school. Students in grade 6 completed a similar questionnaire at baseline, and were surveyed again in grades 7 and 8. Multilevel regression analyses were used to examine school and individual characteristics simultaneously. <br ><br /> Each 5% increase in the senior student smoking rate at a school increased the risk that a non-smoking grade 6 student would try smoking more than once by grade 8 (OR 1. 05) and that a non-smoking grade 6 student with no smoking friends would gain a smoking friend by grade 7 (OR 1. 10). Students who remained non-smokers in grade 7 but gained a smoking friend were more likely to try smoking more than once by grade 8 (OR 4. 31). <br ><br /> In schools where a high proportion of senior students smoked, younger students were more likely to initiate smoking, and gain a smoking friend. Anti-smoking policies and interventions may be more urgently required in these schools to lower senior student smoking rates and reduce initiation among younger students. Tailoring the intensity and content of programs to match the needs of schools is one way to potentially maximize effectiveness.

This thesis examined the factors correlated with rapid and benign progression of disease in a group of 1452 Parkinsons disease (PD) patients. The data were collected in a movement disorders clinic at the Royal University Hospital, University of Saskatchewan run by Dr. Alex Rajput and Dr. Ali Rajput. This data is a clinical dataset of PD patients collected from 1970 through to February, 2005. This was a retrospective cases-only study, with anticipated analytical follow-up if any correlations were detected between progression type of PD and the many independent variables available in the dataset. <p>Rapid progression was defined as those subjects who reached Hoehn and Yahr stage 3 within three years or H&Y stage 4 or 5 within five years. Subjects who remained in Hoehn and Yahr stage 1 or 2, ten years after onset of disease, were defined as having benign progression. The study analyzed demographic and clinical findings at first visit to this clinic associated with rapid and benign progression of PD. <p> Analysis revealed that, at first clinic visit, benign progression was positively associated with disease duration (OR=1.41; 95% CI 1.27, 1.57), male sex (OR=3.23; 95% CI 1.70, 6.16), and current smoking habit (OR=2.33; 95% CI 0.67, 8.11). Benign progression was negatively associated with older age of onset (OR=0.36; 95% CI 0.25, 0.50), past history of smoking (OR=0.46; 95% CI 0.24, 0.89), current or past use of levodopa (OR=0.45; 95% CI 0.21, 0.98), and mild to severe rigidity (OR=0.43; 95% CI 0.23, 0.80). <p>Analysis also revealed that, at first clinic visit, rapid progression was positively associated with older age of onset (OR=2.45; 95% CI 1.80, 3.33) and mild to severe rigidity (OR=1.73; 95% CI 1.02, 2.94). Rapid progression was negatively associated with disease duration (OR=0.52; 95% CI 0.44, 0.62), male sex (OR=0.58; CI 0.35, 0.95), and mild to severe resting tremor (OR=0.47; CI 0.28, 0.77). <p>The results of this study indicate that age of onset, disease duration, male sex, and rigidity are good potential predictors of disease progression in PD because they have opposite associations with rapid and benign progression. History of levodopa use was negatively associated with benign progression and as such may be good indicator of non-benign progression. Although previous studies found no predictive value for smoking history, the current study reported a unique association between smoking history and benign progression. Past smoking history was negatively associated with benign progression. While there was a positive association with current smoking history, the result was not statistically significant. Resting tremor was negatively associated with rapid progression and as such may be a good indicator of non-rapid progression. <p> Disease characteristics collected at first clinic visit are useful in predicting the course of progression of PD. With more rapid progression of PD closer and more frequent follow-up of patients may be necessary.

A financial failure prediction model should be dynamic by adding latest information in an effort to improve the current predictive power, and this model also can be applied to different industries and periods. That is, it has prominent goodness of fit and stable parameter. In this study, I testify that if the modified independent variables, industry relative ratios, can improve the prediction rate by using logistic regression. My research is based on public information. This study constructs two kinds of model¡GModel I is constructed with original financial ratios and Model II with relative industry ratios. Both models incorporate additional variables related to corporate governance. My empirical results suggest that relative industry ratios enhance the predictive power of financial failure prediction within three partially overlapping periods. Further study focus on Model II, I isolated firms which are confronted with financial difficulties and they can¡¦t be discriminated from other normal firms by using the prediction model. My result demonstrates that the main difference between the former and the latter is debt/equity ratio. Those firms which can¡¦t be detected afford less liability. In addition, my studies also compare these undetected firms with their control group and find they still can be distinguished from their control group by using logit model. The accuracy rate of prediction can reach 92.42%. Last study we use event study to research the links between the default possibilities of firms and their stock prices. My results demonstrate that the default possibilities may cause abnormal returns.

To improve Taiwan's competitiveness, Taiwan's government actively encourages businesses to commit to innovative R&D activities by implementing R&D subsidies and incentives. They hope to accumulate intellectual capitals and nurture enough technical professionals to promote industrial upgrades and stimulate economic development. When applying for R&D subsidy, the application must go through the proper approval process. Approved applications will be awarded with substantial financial assistance to fuel further innovations such that R&D subsidy has become critical to many companies. This study used the "Conventional Industry Technology Development"(CITD) as an example and classifies the R&D subsidy applications for analysis based on the 5 variables: company's basic information, program type, commitment to R&D, past experience in applying for R&D subsidy, and strategic alliance with other institutions. Logistic regression is expected to sum up factors that are significant in obtaining R&D subsidies and formulate a predictive model. This enables the government to re-examine its policies and understand the conventional manufacturers' commitment to R&D. Furthermore, the study may assist the companies to assess their chances in obtaining R&D subsidies and serve as a reference for future endeavors. The study suggests that the factors most critical for companies to obtain R&D subsidies are the number of employees, capital size, number of government subsidies already obtained, number of companies non-R&D outsourced to, whether R&D intensity has increased for the past 2 years, and whether R&D funding has increased for the past 2 years. Although plan's duration and non-R&D outsourced dollar amount may have some influence, their impact was not obvious in the model.

The paper constructs recidivism prediction models for women drug prisoners, using the 10 factors evaluated in "drug recidivism risk assessment form" by correctional institutions and the 18 factors studied in the literature. With the new recidivism prediction model, I hope to help improving the prediction accuracy of women drug prisoners¡¦ recidivism. The sample in the paper includes 1,029 drug prisoners released from Kaohsiung Women's Prison between 2008 and 2011. All criminal records are traced until the end of 2011. Two sets of potential risk factors of recidivism are considered in the paper. The first set only contains the factors in the evaluation form, and the second set includes all relevant factors. Using Logistic Regression Analysis and Survival Analysis, the effects of potential risk factors on recidivism are examined. I also predict the probability and the time interval of recidivism. Using the Logistic regression model with the risk factors only in the evaluation form, 58.4% of recidivism can be correctly predicted. While extending the set of potential risk factors, the screening rate of recidivism can be enhanced to 73.3%. The median forecast results are far superior to the average forecast in Survival Analysis. With the potential risk factors in the evaluation form, the difference of predicted recidivism date and the actual date is less than 60 days and less than 180 days in 2.5% and 9.6% of sample respectively. With all relevant risk factors, prediction, the share of sample whose difference of predicted recidivism date and the actual date is less than 60 days and less than 180 days are significantly improved to 10.2% and 27.3% respectively.

The purpose of this research was to create measures and models for the evaluation of Open Source Software (OSS) projects. An exploratory analysis of the development and maintenance processes in OSS was conducted for this purpose. Data mining and text mining techniques were used to discover knowledge from transactional datasets maintained on OSS projects. Large and comprehensive datasets were used to formulate, test and validate the models. A new multidimensional measure of OSS project performance, called project viability was defined and validated. A theoretical and empirical measurement framework was used to evaluate the new measure. OSS project data from SourceForge.net was used to validate the new measure. Results indicated that project viability is a measure of the performance of OSS projects. Three models were then created for each dimension of project viability. Multiple data mining techniques were used to create the models. Variables identified from process, product, resource and end-user characteristics of the project were used. The use of new variables created through text mining improved the performance of the models. The first model was created for OSS projects in the development phase. The results indicated that end-user involvement could play a significant role in the development of OSS projects. It was also discovered that certain types of projects are more suitable for development in OSS communities. The second model was developed for OSS projects in their maintenance phase. A two-stage model for maintenance performance was selected. The results indicated that high project usage and usefulness could improve the maintenance performance of OSS projects. The third model was developed to investigate the affects of maintenance activities on the project internal structure. Maintenance data for Linux project was used to develop a new taxonomy for OSS maintenance patches. These results were then used to study the affects of various types of patches on the internal structure of the software. It was found that performing proactive maintenance on the software moderates its internal structure.

In order to reduce default risk, financial institutions have been investigating into credit ratings of companies, which they want to give credit to. This research tries to give a method for financial institutions to differentiate between default and normal company with financial ratios, which is already announced in their seasonal financial reports. The samples are abstracted from security markets, and restricted to building companies. With Discriminant analysis and Logistic regression models, financial institutions can estimate what company may become into default situation and others stay in good condition. According to this research, financial ratios that can be used to discriminate between default and normal companies are: net worth ratio and short-turn borrowing/liquid asset and asset turnover and gross profit margin. It can also be described with asset turnover and gross profit margin if default risk is been estimated.

The Company often enlarge its scale to maintain its competitive advantage by investing. When company lacks of internal funds, it will raise funds from outside. The purpose of this study is to explore how company chooses financial instruments and influence of the announcement effect on stock price. This study analyzes Taiwan listed company by the the sample period from 1993 to 2005. There are two parts of the thesis. The first is the factor of choosing certain financial instrument. We use logistic regression model, both binary and multinomial, to figure it out. The second is the influence of the announcement effect has on the stock price. We use event study to find whether abnormal return exists. Conclusion: 1. If the company¡¦s size is larger, it will choose debt to raise funds. 2. If R&D expense relative to net sales, debt ratio, the proportion of intangible asset are higher, the company will be tend to raise funds by choosing convertible bond 3. If the stock price is overvalued, the company will choose stock. 4. Taiwan listed company will experience negative stock return whatever it chooses stock, debt, or convertible bond.

This study aims to construct a predictive model that investigates patterning of settlement mounds by employing environmental variables. The results then will help to search for unknown sites of the same age. The methodology is applied to the Lake District of Anatolia for the period of 9000B.C. &amp / #8211 / 5500B.C. Four main sets of data are used in this study. The first set is the settlement data, which includes the names, coordinates, and periods of the sites. The sources of independent datasets are topography, lithology and soil. The study starts with the straightforward procedure of plotting the sites in the region. Then the layers (independent variables), populated with their sub-fields, are included in the model in the GIS to construct a predictive model by using logistic regression. Results reveal some high potential areas with no known occupation, as well as some zones which need more research. Also, hierarchy of environmental variables is detected, which affected the settlement patterning of the study area.

In this thesis, we study improvement of product quality in manufacturing industry by identifying and optimizing influential process variables that cause defects on the items produced. Real data provided by a manufacturing company from the metal casting industry were studied. Two well-known approaches, logistic regression and decision trees, were used to model the relationship between process variables and defect types. The approaches used were compared.