Application of Shewhart Control Chart to Analyze the Effect of Income on Body Mass Index and Blood Pressure

This study investigates whether the level of income influences body mass index and blood pressure and we also determine whether there is a positive or negative correlation between the study variables (income, body mass index, and blood pressure). The Shewhart control chart procedure was applied to determine the effect of income on body mass index and blood pressure on subgroup categories. The Pearson correlation procedure was used to determine the relationship between the study variables. The findings showed that the percentage of high-income earners is higher with respect to normal body mass index (normal weight) than middle and low-income earners, respectively. The result also revealed that the percentage of low-income earners with normal blood pressure is higher compared to middle and high-income earners. The analysis indicates that the percentage of elevated blood pressure and hypertension is higher for middle and high-income earners than low-income earners. The result showed a weak negative correlation (r =-0.33) between income and BMI and a very strong positive correlation (r = 0.88) between income and blood pressure. This study concludes that the level of income influences body mass index and blood pressure based on working-class categories and lifestyl

Graphical Summaries of Circular Data with Outliers Using Python Programming Language

Graph in statistics is used to summarise and visualise the data in pictorial form. Graphical summary enables us to visualise the data in a more simple and meaningful way so that the interpretation will be easier to understand. The graphical summaries of circular data with outliers is discussed in this study. Most of the time, people use linear data in real life applications. Other than linear data, there is another data type that has a direction which refers to circular data and it is different from linear data in many aspects such as in descriptive statistics and statistical modeling. Unfortunately, the availability of statistical software specialises in analysing circular data is very limited. In this study, the graphical summaries of circular data are plotted using the in-demand programming language, Python. The Python code for generating graphical summaries of circular data such as circular dot plot and rose diagram is proposed. The historical circular data is used to illustrate the graphical summaries with the existence of outliers. This study will be helpful for those who are started exploring circular data and choose Python as an analysis tool

Structural translation with synchronous tree adjoining grammars in VERBMOBIL

The VERBMOBIL project is developing a translation system that can assist a face-to-face dialogue between two non-native english speakers. Instead of having continiously speak english, the dialogue partners have the option to switch to their respective mother tongues (currently german or japanese) in cases where they can\u27t find the required word, phrase or sentence. In such situations, the users activate VERBMOBIL to translate their utterances into english. A very important requirement for such a system is realtime processing. Realtime processing is essentially necessary, if such a system is to be smoothly integrated into an ongoing communication. This can be achieved by the use of anytime processing, which always provides a result. The quality of the result however, depends on the computation time given to the system. Early interruptions can only produce shallow results. Aiming at such a processing mode, methods for fast but preliminary translation must be integrated into the system assisted by others that refine these results. In this case we suggest structural translation with Synchronous Tree Adjoining Grammars (S-TAGs), which can serve as a fast and shallow realisation of all steps necessary during translation, i.e. analysis, transfer and generation, in a system capable of running anytime methods. This mode is especially adequate for standardized speech acts and simple sentences. Furthermore, it provides a result for early interruptions of the translation process. By building an explicit linguistic structure, methods for refining the result can rearrange the structure in order to increase the quality of the translation given extended execution time. This paper describes the formalism of S-TAGs and the parsing algorithm implemented in VERBMOBIL. Furthermore the language covered by the german grammar is described. Finally we list examples together with the execution time required for their processing

Modelling life trajectories and mode choice using Bayesian belief networks

Traditionally, transport mode choice was primarily examined as a stand alone problem. Given a purpose and destination, the choice of transport mode was modelled as a function of the various attributes of the transport mode alternatives. Later, transport mode choice decisions were modelled as part of more comprehensive models (activity-based approach). There is a need in the transport research community to explore and model dynamics in activity-travel patterns along various time horizons. This will lead to dynamic models of behavioural change. In this thesis, it is argued that a life course perspective offers some potential advantages in understanding and modelling activity-travel decisions, including transport mode choice. Central concepts in the life course approach are life trajectories, transitions and events. An individual life course is composed of multiple, interdependent careers (i.e. housing, household, education, occupational career) which develop over time in parallel. Earlier life transitions may have a cumulative effect on later life. The concepts of timing, sequencing, duration and spacing are used to describe life events, transitions and trajectories. The assumed effect of events on activity-travel decisions is captured in terms of a theory of learning and adaptation. Individuals develop and continuously adapt choice rules while interacting with their environment. The context is nonstationary, uncertain and highly dynamic and therefore it is assumed that individuals adapt their behaviour. Under stationary conditions, individuals will show habitual behaviour after some period of time. A life course event is seen as a trigger that may induce individuals and households to reorganise their activities in time and space. A particular event may also lead to other life course events. Thus, life course events may have direct and indirect effects on activity-travel patterns. An event does not necessarily lead to immediate changes in particular facets of activity-travel patterns. Behavioural change may also occur in anticipation of life course events. Bayesian Belief Networks is the approach adopted in this thesis to model the direct and indirect effects of life course effects on transport mode choice. More complex causation patterns can be included and results can be directly interpreted in terms of the classified events. Such networks need as input empirical data to learn the structure of the network and the conditional probability tables of the variables that are identified to be relevant. Data was collected using a retrospective Internet-based survey. Retrospective surveys, especially when administered through the Internet, are a good alternative for (quasi-)longitudinal data collection methods, like panel surveys, repeated cross sectional surveys, and cohort pseudo-panel surveys. One would expect that the quality of data coming from a retrospective survey depends on the nature of the event about which information is collected and on the time elapsed between the occurring of the events and the time of the retrospective survey. The quality of the data was tested and the results were positive. In case of life course events memory lapses are less of a problem. Life course events can be better recollected than other events. In this study, the results of the reliability and validity tests of the collected data showed that item nonresponse in general was relatively low, especially for those life course events that serve as markers unfolding one’s life. A statistical analysis suggested that memory / cohort effects were not found for the more salient life course events, such as housing, work and study related events. Memory may have an effect in reporting of events in case of income and transport mode related events (car availability and PT pass). The study illustrated that certain details of events, such as housing type and housing state are more difficult to recall. The time effect of an influence of life course events on mode choice was tested with a simple multinomial logit model. The results support the conclusion that a certain time influence exists in the response to events. The data of the retrospective Internet-based survey was used as input for two Bayesian Belief Networks, a life trajectory and a mode choice network. A year is chosen as the unit of analysis for these networks. Both networks were successfully learned from the data. The first network can be used to simulate a person’s life trajectory and the second network can be used to predict mode choice for an individual at a certain time given the individual life trajectory. The goodness-of-fit of the learned Bayesian Belief Networks was assessed on the basis of the log likelihood statistic. The values indicated that both networks perform relatively well. It was also investigated whether the life trajectory network was capable of reproducing observed characteristics of complete life trajectories. The observed and predicted life trajectories were compared in terms of the following criteria: the number of occurrences, interval times between occurrences of events, simultaneous occurrences of events and sequence of occurrences of events. The life trajectory network reproduced the number of occurrences in the life trajectories quite well. In general, the network predicted more or less the same means of interval times for the events, except for the PT pass event. The network was less successful in predicting correctly the observed incidence of synchronic events. The results of the sequence alignment analysis indicate that the network predicts the sequence of the occurrences in the life trajectories relatively good. The modal split (car, public transport and slow transport) of the predicted mode choice was compared with the observed mode choice. Results indicated a relatively small over prediction of public transport and under prediction of car and slow transport. This suggests that the mode choice network is able to simulate more or less the same mode choice as registered in the data. The learned networks were used to study direct and indirect effects of one variable on other variables in the network. The described effects seem logical. A simulation illustrated the dynamics of the lives of ten inhabitants of a newly build neighbourhood. It showed that, insight in dynamics of life trajectory events and mode choice can lead to a better understanding which can support the development of better or different policy measures

Simple low cost causal discovery using mutual information and domain knowledge

PhDThis thesis examines causal discovery within datasets, in particular observational datasets where normal experimental manipulation is not possible. A number of machine learning techniques are examined in relation to their use of knowledge and the insights they can provide regarding the situation under study. Their use of prior knowledge and the causal knowledge produced by the learners are examined. Current causal learning algorithms are discussed in terms of their strengths and limitations. The main contribution of the thesis is a new causal learner LUMIN that operates with a polynomial time complexity in both the number of variables and records examined. It makes no prior assumptions about the form of the relationships and is capable of making extensive use of available domain information. This learner is compared to a number of current learning algorithms and it is shown to be competitive with them

Fuzzy Sets Use in Cluster Analysis with a Special Attention to a Fuzzy C-means Clustering Method

Táto práca sa zaoberá zhlukovou analýzou, a podrobnejšie zhlukovacími metódami, ktoré používajú fuzzy množiny. V teoretickej časti sú popísané zhlukovacie metódy a transformácie potrebné na zhlukovú analýzu. V praktickej časti aplikujeme na reálne dáta. Tieto dáta predstavujú vstupné dáta z chemicko-transportného modelu CMAQ, ktorý sa používa na získanie výpočtu koncentrácii znečisťujúcich látok v atmosfére. Na tieto dáta aplikujeme dve rôzne metódy, metódu k-means a fuzzy c-means. Pre metódu fuzzy c-means porovnáva dva rôzne prístupy k zvoleniu optimálneho váhového exponentu. Porovnali sme takto vytvorené 3 zhlukovacie štruktúry. Výsledné zhluky si boli podobné a však metóda fuzzy c- means s vyššiu hodnotou váhového exponentu vytvorila zhluky, ktoré nemali žiadnu podobnosť so zhlukovanými veličinami. V závere sme vytvorili regresný model na nájdenie vzťahu medzi vstupnými a výstupnými dátami modelu CMAQ.This master thesis deals with cluster analysis, more specifically with clustering methods that use fuzzy sets. Basic clustering algorithms and necessary multivariate transformations are described in the first chapter. In the practical part, which is in the third chapter we apply fuzzy c-means clustering and k-means clustering on real data. Data used for clustering are the inputs of chemical transport model CMAQ. Model CMAQ is used to approximate concentration of air pollutants in the atmosphere. To the data we will apply two different clustering methods. We have used two different methods to select optimal weighting exponent to find data structure in our data. We have compared all 3 created data structures. The structures resembled each other but with fuzzy c-means clustering, one of the clusters did not resemble any of the clustering inputs. The end of the third chapter is dedicated to an attempt to find a regression model that finds the relationship between inputs and outputs of model CMAQ.

Konzept zur interaktiven Auswertung multidimensionaler biologischer Bilddaten

Das Thema der Arbeit beinhaltet die Konzeptionierung der Analyse großer multidimensionaler biologischer Bilddatenmengen. Erstellt werden Bausteine für die Filterung, Segmentierung und Merkmalsextraktion. Weiterhin werden Programmpakete bereitgestellt. Dabei wird stets der Spagat zwischen Qualität, Quantität und Berechnungszeit gehalten