Indiana University-Purdue University Indianapolis (IUPUI) / Various methods exist to measure physical activity (PA). Subjective methods, such as diaries and surveys are relatively inexpensive ways of measuring one’s PA; how ever, they are riddled with measurement error and bias due to self-report. Wearable accelerometers offer a noninvasive and objective measure of subjects’ PA and are now widely used in observational and clinical studies. Accelerometers record high frequency data and produce an unlabeled time series at the sub-second level. An important activity to identify from such data is walking, since it is often the only form of exercise for certain populations. While much work has been done to advance the use of accelerometers in public health research, methodology is needed for quan tifying the physical characteristics of different types of PA from the raw signal. In my dissertation, I advance the accelerometry research methodology in a three-paper sequence. The first paper is a novel application of functional linear models to model the physical characteristics of walking. We emphasize the signal processing used to prepare the data for analyses, and we apply the methods to a motivating dataset collected in an elder population. The second paper addresses the classification of PA. We designed an experiment and collected the data with the purpose of extracting useful and interpretable features for differentiating among walking, descending stairs, and ascending stairs. We build subject-specific classification models utilizing a tree based classifier. We evaluate the effects of sensor location and tuning parameters on the classification rate of these models. The third paper addresses the classification of walking types at the population level. We propose a robust normalization of features extracted for each subject and compare the model classification results to evaluate the effect of feature normalization. In summary, this work provides a framework for better use of accelerometers in the study of physical activity. / 2 years

Metodologie Six Sigma se dnes používá v mnoha firmách a společnostech ke zlepšování kvality procesů a výrobků. Využívá k tomu různé statistické nástroje a jedním z hlavních je lineární regrese. Cílem této práce je stručný popis metodologie Six Sigma. V dalším kroku pak pomocí lineární regrese ale i jiných statistických nástojů eliminovat jednu výrobní operaci v konkrétním technickém procesu.

The study aimed to developed a logistic model based on antibody proteins, vaccinations and demographic factors that predicts previous infection in Covid-19. The data set comprised of 2750 individuals from eldercare homes in Sweden, with four test dates executed between October of 2021 and August of 2022.  Exploratory data analysis revealed bimodal patterns in the antibodies against nucleocapsid protein within the non-infected group, raising suspicions of false negatives in the data. Due to the binary nature of the response and to be interpretable for further research, logistic regressions were used to model the relation between predictors and the logit of the response. Because of low performance scores and high probability for the presence of false negatives, K-means clustering algorithm was performed on the data. As a clustering variable, the logarithm of base 2 of the nucleocapsid protein was used, because of its theoretical relationship with previous infection in Covid-19.  Observations were reclassified using the clustering technique, and two new logistic models were fitted to the data. The final model contained polynomial terms to handle the non-linear relationship between the logit of the response and the predictors. We found a significant relationship between the logarithm of 2 of nucleocapsid protein and previous Covid-19 infection in the final model, with high prediction results. We reached an F1-score of 0.94, indicating a well-performing model.  Additionally, an algorithm was created to predict the days since infection, involving the change in nucleocapsid protein from one test date to the next, and a GAM model for fitting a smooth line to the data between nucleocapsid protein as response against the days since infection. Using this algorithm, we reached an absolute mean error between predicted results and actual days since infection of 23 days. This algorithm was later applied to observations reclassified in the clustering process.  In conclusion, the study successfully reclassified false negative observations with previous Covid-19 infection, and fitted a logistic model with high prediction score with F1-score of 0.94. Finally, an algorithm was created that estimated the days since infection with an absolute mean error of 23 days. / Syftet med studien var att utveckla en logistisk modell baserad på antikroppsproteiner, vaccinationer och demografiska faktorer som förutsäger tidigare infektion i Covid-19. Datamängden bestod av 2750 individer från äldreboenden i Sverige, med fyra testdatum utförda mellan oktober 2021 och augusti 2022.  Utforskande dataanalys visade på bimodala mönster i antikroppar mot nukleokapsidprotein inom den icke- infekterade gruppen, vilket gav upphov till misstankar om falskt negativa resultat i datamaterialet. På grund av svarets binära karaktär och för att vara tolkningsbara för vidare forskning användes logistiska regressioner för att modellera förhållandet mellan prediktorer och responsvariabeln. På grund av låga prediktionsresultat och hög sannolikhet av förekomsten av falskt negativa svar utfördes K-means-klusteralgoritmen på datat. Som klustervariabel användes logaritmen av bas 2 för nukleokapsidproteinet, på grund av dess teoretiska samband med tidigare infektion i Covid-19.  Observationerna omklassificerades med hjälp av klustertekniken, och två nya logistiska modeller anpassades till datat. Den slutliga modellen innehöll polynomiala termer för att hantera det icke-linjära förhållandet mellan responsens logit och prediktorerna. Vi fann ett signifikant samband mellan logaritmen av 2 av nuk- leokapsidprotein och tidigare Covid-19-infektion i den slutliga modellen, med ett högt prediktionsresultat. Vi nådde en F1-score på 0.94.  Dessutom skapades en algoritm som predicerade dagar sedan infektion med hjälp av förändringen i nukleokap- sidprotein från ett testdatum till nästa, och en GAM-modell för att anpassa ett glidande medelvärdeslinje till datat mellan nukleokapsidprotein som response mot dagarna sedan infektionen. Med hjälp av denna algoritm nåddes ett absolut medelfel på 23 dagar mellan prediktion och faktiskt tid sedan infektionen. Denna algoritm tillämpades senare på observationer som omklassificerats i klusterprocessen.  Sammanfattningsvis lyckades studien framgångsrikt omklassificera falskt negativa observationer med tidigare Covid-19-infektion och anpassade en logistisk modell med hög prediktionspoäng med en F1-score på 0.94. Slutligen skapades en algoritm som uppskattade dagarna sedan infektionen med ett absolut medelfel på 23 dagar.

Data generated by complex, computational models can provide highly accurate predictions of hydrological and hydrodynamic data in multiple dimensions. Unfortunately, however, for large data sets, running these models is often timeconsuming and computationally expensive. Thus, finding a way to reduce the running time of these models, while still producing comparable results, is of notable interest. The Inference Engine is a proposed system for doing just this. It takes previously generated model data and uses them to predict additional data. Its performance, both accuracy and running time, has been compared to the performance of the actual models, in increasingly difficult data prediction tasks, and it is able, with sufficient accuracy, to quickly predict unknown model data.

Age estimation, a component of the biological profile, contributes significantly to the creation of a post-mortem profile of an unknown set of human remains. This goal of this study is to: (1) refine the juvenile age estimation method of cranial vault thickness (CVT) through MARS modeling, (2) test the method on known age samples, and (3) compare CVT and dental development age estimations. Data for this study comes from computed tomography (CT) scans, radiographic images, and dry bone. CVT was measured at seven cranial landmarks (nasion, glabella, bregma, vertex, vertex radius, lambda and opisthocranion). Results indicate that CVT models vary in their predictive ability; vertex and lambda produce the best results. Predicted fit values and prediction intervals for CVT are larger, and less accurate than dental development age estimates. Aging by CVT could benefit from a larger known age sample composed of individuals older than 6 years old.

Past studies concerning the effects of economic conditions on voter perceptions have tended to generalize their findings to the entire national electorate. Such generalizations fail to account for the different ideologies, lifestyles, and economic conditions that exist from state to state. In the current study, I compare the effects of subjective financial evaluations with the effects of objective economic indicators on voter perceptions and turnout in the state of Mississippi. The purpose is to determine the extent to which past findings on the national level hold up on the state level, with Mississippi as the subject of analysis. Using data from the Mississippi Poll and employing a logistic regression method, the findings show that Mississippian‟s perceptions of political figures are more strongly influenced by subjective financial evaluations. Voter turnout, on the other hand, was more strongly influenced by objective economic indicators than personal financial satisfaction.

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The existing research in the field of tourism has exhibited a clearer understanding of how residents perceive the dynamic and complex phenomena of tourism. Since the goals of tourism planning and development are to seek maximization of benefits and minimization of the costs of tourism, it is apparent that the effective evaluation of tourism impacts will be valuable information in successful strategies for tourism product development and operation. With these perspectives, this study attempted to investigate the underlying dimensions explaining residents' perceived tourism impacts and to identify relationships between determinants and residents' perceived tourism impacts. The social exchange theory provided a fundamental framework for this study. The dimensions of the tourism impacts were addressed by explicating economic, social/cultural, environmental/physical impacts of tourism development from literature review. Ten determinants which affect residents' perception were identified from past research on tourism impacts: birthplace, length of residency, community attachment, tourism related jobs, recreational activity, tourist contacts, tourism policy participation, travel experience, levels of tourism development, and growth of community. Norfolk/Virginia Beach/Newport News MSAs areas were selected as the study area because these areas provide fine multifaceted tourism attractions, generates many tourists, and influences the host community' life. A total of 316 useful respondents (13.2%) were analyzed by using the SPSS program. Two research questions were proposed. Factor analysis, multiple regression analysis, and multivariate analysis of variance (MANAOVA) were performed. From the findings of this study, residents perceived the impacts of tourism as five different dimensions embodying economic benefits, social costs, cultural enrichment, environmental deterioration, and physical enhancement. Their perceptions were affected by eight out of ten determinants. Generally, a higher level of tourism development and growth of community affects residents' perceptions of tourism impacts. Residents who were natives, who have higher community attachment, and who had been living in the research area for a shorter time period had more concerns about the perceived impacts of tourism. In addition, perceived tourism impacts were significantly differed across household incomes and ethnic groups. For future study, it is suggested that a further investigation of determinants affecting residents' perceptions is needed for better understanding and explanation of the impacts of tourism. It is believed that this study would help tourism planners and developers formulate and implement better strategies. / Master of Science

Colleges and universities are under mounting pressure to meet enrollment goals in the face of declining college attendance. Insight into student-level probability of enrollment, as well as the identification of features relevant in student enrollment decisions, would assist in the allocation of marketing and recruitment resources and the development of future yield programs. A logistic regression model was fit to predict which applicants will ultimately matriculate (enroll) at Concordia College. Demographic, geodemographic and behavioral features were used to build a logistic regression model to assign probability of enrollment to each applicant. Behaviors indicating interest (campus visits, submitting a deposit) and residing in a zip code with high alumni density were found to be strong predictors of matriculation. The model was fit to minimize false negative rate, which was limited to 18.1 percent, compared to 50-60 percent reported by comparable studies. Overall, the model was 80.13 percent accurate.