Current research suggests that a high level of confidence and a low level of anxiety are predictive of higher math achievement. Compared to students from other provinces, previous research has found that Saskatchewan students have a higher level of confidence and a lower level of anxiety for learning math, but still tend to achieve lower math scores compared to students in other provinces. The data suggest that there may be unique factors effecting math learning for students in Saskatchewan. The purpose of the study is to determine the factors that may affect Saskatchewan students math achievement. Exploratory factor analyses and regression methods were employed to investigate possible traits that aid students in achieving higher math scores. Results from a 2007 math assessment administered to grade 5 students in Saskatchewan were used for the current study. The goal of the study was to provide a better understanding of the factors and trends unique to students for mathematic achievements in Saskatchewan.<p> Using results from a province-wide math assessment and an accompanying questionnaire administered to students in grade five across public school in Saskatchewan (n=11,279), the present study found statistical significance in three factors that have been supported by previous studies to influence math achievement differences, specifically in (1) confidence in math, (2) parental involvement in math and (3) extracurricular participation in math. The three aforementioned factors were found to be related to math achievement as predicted by the Assessment for Learning (AFL) program in Saskatchewan, although there were reservations to the findings due to a weak amount of variances accounted for in the regression model (r2 =.084). Furthermore, a multivariate analysis of variance indicated gender and locations of schools to have effects on students math achievement scores. Although a high amount of measurement errors in the questionnaire (and subsequently a low variance accounted for by the regression model) limited the scope and implications of the model, future implications and improvements are discussed

This thesis proposes a scientific model to explain the data analysis process. I argue that data analysis is primarily a procedure to build un- derstanding and as such, it dovetails with the cognitive processes of the human mind. Data analysis tasks closely resemble the cognitive process known as sensemaking. I demonstrate how data analysis is a sensemaking task adapted to use quantitative data. This identification highlights a uni- versal structure within data analysis activities and provides a foundation for a theory of data analysis. The model identifies two competing chal- lenges within data analysis: the need to make sense of information that we cannot know and the need to make sense of information that we can- not attend to. Classical statistics provides solutions to the first challenge, but has little to say about the second. However, managing attention is the primary obstacle when analyzing big data. I introduce three tools for managing attention during data analysis. Each tool is built upon a different method for managing attention. ggsubplot creates embedded plots, which transform data into a format that can be easily processed by the human mind. lubridate helps users automate sensemaking out- side of the mind by improving the way computers handle date-time data. Visual Inference Tools develop expertise in young statisticians that can later be used to efficiently direct attention. The insights of this thesis are especially helpful for consultants, applied statisticians, and teachers of data analysis.

Lung cancer is the cancer form that has the highest mortality worldwide and inaddition the survival of lung cancer is very low. Only 15% of the patients are alivefive years from set diagnosis. More research is needed to understand the biologyof lung cancer and thus make it possible to discover the disease at an early stage.Early diagnosis leads to an increased chance of survival. In this thesis 179 lungcancer- and 116 control samples of blood serum were analyzed for identificationof metabolomic biomarkers. The control samples were derived from patients withbenign lung diseases.Data was gained from GC/TOF-MS analysis and analyzed with the help ofthe multivariate analysis methods PCA and OPLS/OPLS-DA. In this thesis it isinvestigated how to pre-treat and analyze the data in the best way in order todiscover biomarkers. One part of the aim was to give directions for how to selectsamples from a biobank for further biological validation of suspected biomarkers.Models for different stages of lung cancer versus control samples were computedand validated. The most influencing metabolites in the models were selected andconfoundings with other clinical characteristics like gender and hemoglobin levelswere studied. 13 lung cancer biomakers were identified and validated by raw dataand new OPLS models based solely upon the biomarkers.In summary the identified biomarkers are able to separate fairly good betweencontrol samples and late lung cancer, but are poor for separation of early lungcancer from control samples. The recommendation is to select controls and latelung cancer samples from the biobank for further confirmation of the biomarkers.NyckelordLung cancer is the cancer form that has the highest mortality worldwide and inaddition the survival of lung cancer is very low. Only 15% of the patients are alivefive years from set diagnosis. More research is needed to understand the biologyof lung cancer and thus make it possible to discover the disease at an early stage.Early diagnosis leads to an increased chance of survival. In this thesis 179 lungcancer- and 116 control samples of blood serum were analyzed for identificationof metabolomic biomarkers. The control samples were derived from patients withbenign lung diseases.Data was gained from GC/TOF-MS analysis and analyzed with the help ofthe multivariate analysis methods PCA and OPLS/OPLS-DA. In this thesis it isinvestigated how to pre-treat and analyze the data in the best way in order todiscover biomarkers. One part of the aim was to give directions for how to selectsamples from a biobank for further biological validation of suspected biomarkers.Models for different stages of lung cancer versus control samples were computedand validated. The most influencing metabolites in the models were selected andconfoundings with other clinical characteristics like gender and hemoglobin levelswere studied. 13 lung cancer biomakers were identified and validated by raw dataand new OPLS models based solely upon the biomarkers.In summary the identified biomarkers are able to separate fairly good betweencontrol samples and late lung cancer, but are poor for separation of early lungcancer from control samples. The recommendation is to select controls and latelung cancer samples from the biobank for further confirmation of the biomarkers.Nyckelord

The aim of this work was to design a set of rules for levodopa infusion dose adjustment in Parkinson’s disease based on a simulation experiments. Using this simulator, optimal infusions dose in different conditions were calculated. There are seven conditions (-3 to +3)appearing in a rating scale for Parkinson’s disease patients. By finding mean of the differences between conditions and optimal dose, two sets of rules were designed. The set of rules was optimized by several testing. Usefulness for optimizing the titration procedure of new infusion patients based on rule-based reasoning was investigated. Results show that both of the number of the steps and the errors for finding optimal dose was shorten by new rules. At last, the dose predicted with new rules well on each single occasion of majority of patients in simulation experiments.

Current research suggests that a high level of confidence and a low level of anxiety are predictive of higher math achievement. Compared to students from other provinces, previous research has found that Saskatchewan students have a higher level of confidence and a lower level of anxiety for learning math, but still tend to achieve lower math scores compared to students in other provinces. The data suggest that there may be unique factors effecting math learning for students in Saskatchewan. The purpose of the study is to determine the factors that may affect Saskatchewan students math achievement. Exploratory factor analyses and regression methods were employed to investigate possible traits that aid students in achieving higher math scores. Results from a 2007 math assessment administered to grade 5 students in Saskatchewan were used for the current study. The goal of the study was to provide a better understanding of the factors and trends unique to students for mathematic achievements in Saskatchewan.<p> Using results from a province-wide math assessment and an accompanying questionnaire administered to students in grade five across public school in Saskatchewan (n=11,279), the present study found statistical significance in three factors that have been supported by previous studies to influence math achievement differences, specifically in (1) confidence in math, (2) parental involvement in math and (3) extracurricular participation in math. The three aforementioned factors were found to be related to math achievement as predicted by the Assessment for Learning (AFL) program in Saskatchewan, although there were reservations to the findings due to a weak amount of variances accounted for in the regression model (r2 =.084). Furthermore, a multivariate analysis of variance indicated gender and locations of schools to have effects on students math achievement scores. Although a high amount of measurement errors in the questionnaire (and subsequently a low variance accounted for by the regression model) limited the scope and implications of the model, future implications and improvements are discussed

GDP and GDP per capita are widely used to gauge for living standards across countries. However, they have originally not been constructed for this purpose and are therefore subject to significant limitations. This paper aims at developing a better and non-monetary development index with which cross-country living standards can be assessed. This index, the BDI, can then be utilized for policy making. When constructing the BDI, this study utilizes time series analysis and panel unit root tests. A major finding of this study is that the BDI does indeed produce statistically significantly different results/ rankings for a special set of countries, compared to GDP and GDP per capita.

In this thesis, we have proposed a novel microarray gene selection algorithm consisting of five processes for solving gene expression classification problem. A normalization process is first used to remove the differences among different scales of genes. Second, an efficient gene ranking process is proposed to filter out the unrelated genes. Then, the genetic algorithm is adopted to find the informative gene subsets for each class. For each class, these informative gene subsets are adopted to classify the testing dataset separately. Finally, the separated classification results are fused to one final classification result. In the first experiment, 4 microarray datasets are used to verify the performance of the proposed algorithm. The experiment is conducted using the leave-one-out-cross-validation (LOOCV) resampling method. We compared the proposed algorithm with twenty one existing methods. The proposed algorithm obtains three wins in four datasets, and the accuracies of three datasets all reach 100%. In the second experiment, 9 microarray datasets are used to verify the proposed algorithm. The experiment is conducted using 50% VS 50% resampling method. Our proposed algorithm obtains eight wins among nine datasets for all competing methods.

Unconventional reservoir systems can best be described as petroleum (oil and/or gas) accumulations which are difficult to be characterized and produced by conventional technologies. In this work we present the development of a systematic procedure to evaluate well performance in unconventional (i.e., low to ultra-low permeability) reservoir systems. The specific tasks achieved in this work include the following: ● Integrated Diagnostics and Analysis of Production Data in Unconventional Reservoirs: We identify the challenges and common pitfalls of production analysis and provide guidelines for the analysis of production data. We provide a comprehensive workflow which consists of model-based production analysis (i.e., rate-transient or model matching approaches) complemented by traditional decline curve analysis to estimate reserves in unconventional reservoirs. In particular, we use analytical solutions (e.g., elliptical flow, horizontal well with multiple fractures solution, etc.) which are applicable to wells produced in unconventional reservoirs. ● Deconvolution: We propose to use deconvolution to identify the correlation between pressure and rate data. For our purposes we modify the B-spline deconvolution algorithm to obtain the constantpressure rate solution using cumulative production and bottomhole pressure data in real time domain. It is shown that constant-pressure rate and constant-rate pressure solutions obtained by deconvolution could identify the correlation between measured rate and pressure data when used in conjunction. ● Series of Rate-Time Relations: We develop three new main rate-time relations and five supplementary rate-time relations which utilize power-law, hyperbolic, stretched exponential, and exponential components to properly model the behavior of a given set of rate-time data. These relations are well-suited for the estimation of ultimate recovery as well as for extrapolating production into the future. While our proposed models can be used for any system, we provide application almost exclusively for wells completed in unconventional reservoirs as a means of providing estimates of time-dependent reserves. We attempt to correlate the rate-time relation model parameters versus model-based production analysis results. As example applications, we present a variety of field examples using production data acquired from tight gas, shale gas reservoir systems.

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 112-115). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 112-115). Also available on the Internet.