This study uses a standard education production function in order to relate student grades in mathematics and statistics to three factors. The first factor includes teaching practice measures and classroom learning environment. The second factor comprises teacher characteristics and class size. The third factor represents student control variables. The statistical analysis which is based on mixed effect modeling of student marks in mathematics and statistics courses shows that incoming skills, classroom learning environment, support to the students and students attitude toward mathematics and statistics are the most significant predictors of achievement in mathematics. However, teaching practices were not found to be crucial for improving mathematics grades.
Cigarette smoking is the predominant risk factor for bladder cancer in males and females. The tobacco carcinogens are metabolized by various xenobiotic metabolizing enzymes such as N-acetyltransferases (NAT) and glutathione S-transferases (GST). Polymorphisms in NAT and GST genes alter the ability of these enzymes to metabolize carcinogens. In this paper, we conduct a statistical analysis based on logistic regressions to assess the impact of smoking and metabolizing enzyme genotypes on the risk to develop bladder cancer using a case-control study from Tunisia. We also use the generalized ordered logistic model to investigate whether these factors do have an impact on the progression of bladder tumors.
Historical temperature data from the Canadian Atlantic province of Prince Edward Island is analyzed over the period from April 1913 to July 2013 in order to develop time series models that have the ability to produce accurate forecasting of minimum and maximum monthly average temperatures. In this paper, we study the statistical properties of these temperature series and we use alternative statistical testing approaches to identify the type of seasonal pattern in the data. A parsimonious seasonal ARIMA model is determined for each series and the predicted future temperature records for the region are presented.
The limit theory of the seasonal KPSS test is established under the null hypothesis using seasonal dummy variables. Taking these variables into account can result in improved finite sample performance of the test. The seasonal KPSS test can be interpreted as a test of deterministic seasonality and it may be used in addition to seasonal unit root tests to analyze the dynamic properties of time series. The seasonal indicator variables provide the test with an explicit model-based regression that in itself constitutes a support for its limit theory.
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