Event program

UNLV Undergraduates from all departments, programs and colleges participated in a campus-wide symposium on April 16, 2011. Undergraduate posters from all disciplines and also oral presentations of research activities, readings and other creative endeavors were exhibited throughout the festival

Event program

UNLV Undergraduates from all departments, programs and colleges participated in a campus-wide symposium on April 16, 2011. Undergraduate posters from all disciplines and also oral presentations of research activities, readings and other creative endeavors were exhibited throughout the festival

Causal Relations via Econometrics

Applied econometric work takes a superficial approach to causality. Understanding economic affairs, making good policy decisions, and progress in the economic discipline depend on our ability to infer causal relations from data. We review the dominant approaches to causality in econometrics, and suggest why they fail to give good results. We feel the problem cannot be solved by traditional tools, and requires some out-of-the-box thinking. Potentially promising approaches to solutions are discussed.causality, regression, Granger Causality, Exogeneity, Cowles Commission, Hendry Methodology, Natural Experiments

Efficient Text Classification with Linear Regression Using a Combination of Predictors for Flu Outbreak Detection

Early prediction of disease outbreaks and seasonal epidemics such as Influenza may reduce their impact on daily lives. Today, the web can be used for surveillance of diseases.Search engines and Social Networking Sites can be used to track trends of different diseases more quickly than government agencies such as Center of Disease Control and Prevention(CDC). Today, Social Networking Sites (SNS) are widely used by diverse demographic populations. Thus, SNS data can be used effectively to track disease outbreaks and provide necessary warnings. Although the generated data of microblogging sites is valuable for real time analysis and outbreak predictions, the volume is huge. Therefore, one of the main challenges in analyzing this huge volume of data is to find the best approach for accurate analysis in an efficient time. Regardless of the analysis time, many studies show only the accuracy of applying different machine learning approaches. Current SNS-based flu detection and prediction frameworks apply conventional machine learning approaches that require lengthy training and testing, which is not the optimal solution for new outbreaks with new signs and symptoms. The aim of this study is to propose an efficient and accurate framework that uses SNS data to track disease outbreaks and provide early warnings, even for newest outbreaks accurately. The presented framework of outbreak prediction consists of three main modules: text classification, mapping, and linear regression for weekly flu rate predictions. The text classification module utilizes the features of sentiment analysis and predefined keyword occurrences. Various classifiers, including FastText and six conventional machine learning algorithms, are evaluated to identify the most efficient and accurate one for the proposed framework. The text classifiers have been trained and tested using a pre-labeled dataset of flu-related and unrelated Twitter postings. The selected text classifier is then used to classify over 8,400,000 tweet documents. The flu-related documents are then mapped ona weekly basis using a mapping module. Lastly, the mapped results are passed together with historical Center for Disease Control and Prevention (CDC) data to a linear regression module for weekly flu rate predictions. The evaluation of flu tweet classification shows that FastText together with the extracted features, has achieved accurate results with anF-measure value of 89.9% in addition to its efficiency. Therefore, FastText has been chosen to be the classification module to work together with the other modules in the proposed framework, including the linear regression module, for flu trend predictions. The prediction results are compared with the available recent data from CDC as the ground truth and show a strong correlation of 96.2%

Intangible assets and national income accounting

In this paper I focus on three related and difficult areas of the measurement of national income. I argue that the economic theory underlying measurement of these items is currently controversial and incomplete.National income

The Impact of Exercise During Radiation Therapy for Breast Cancer Patients

Breast cancer is one of the leading causes of death among women. In 2017, breast, lung and bronchus, prostate, and colorectal cancers accounted for almost 50% of all new cancer cases in the United States. Breast conservation therapy with lumpectomy (i.e., surgery) and adjuvant radiation therapy is commonly used as treatment for early stage breast cancer. However, side effects such as pain and poor sleep quality can affect quality of life for breast cancer patients undergoing radiation treatment. The main purpose of this quantitative study, using the health belief model (HBM) theoretical framework, was to investigate the correlations between the independent variable of exercise and the dependent variables of pain and sleep quality during radiation treatment. To examine these possible relationships, secondary data from another study were used, Self-Reported Exercise Behavior and Short-Term Patients Outcomes in Women Undergoing Radiation Treatment for Operable Breast Cancer by principal investigator Janet K. Horton of the Duke University Health System. The secondary data were analyzed using logistic regression and multiple linear regression statistical models. The findings from this study indicate that mild exercise is positively associated with reduced pain level and improved sleep quality and that vigorous exercise does not have a positive association with improved sleep quality. This study provides health practitioners with resources to encourage physical activity in breast cancer patients while undergoing and after radiation treatment. In this way, the study may serve to promote positive social change not only for breast cancer patients, but also for patients with other types of cancer to reduce side effects from radiation treatment