This thesis investigates the effect of aspect ratio on visual perception in scatterplots. Four tasks explored how the aspect ratio affects participants' perception of distance, amount, and correlation in scatterplots. The results showed that square aspect ratio scatterplots are more suitable for detecting length and number, while rectangular aspect ratio scatterplots are better for detecting correlation. In addition, the JND (Just Noticeable Difference) was used in evaluating the visual perception of scatterplots in this experiment. The findings of this study have important implications for the design of scatterplots in data visualization, as well as for future research on visual perception in data visualization. / Master of Science / This thesis focuses on understanding how the aspect ratio of scatterplots affects how people perceive data. Scatterplots are graphs that display data points on two variables, allowing researchers to identify relationships between variables visually. The aspect ratio of a scatterplot refers to the percentage of its width to height. This study found that people are better at detecting distances and amounts in scatterplots with a square aspect ratio. In contrast, they better see scatterplot correlations with a rectangular aspect ratio. The results suggest that the aspect ratio of a scatterplot plays a critical role in how people perceive and interpret data. These findings are essential for researchers, analysts, and designers who want to create compelling and accurate data visualizations.

Visualisering är ett kraftfullt verktyg vid dataanalys, särskilt för att identifiera avvikelser. Att effektivt kunna identifiera felaktiga komponenter i elektronik kan förbättra och utveckla produktionsprocesserna avsevärd. Genom att tydligt visa korrelationen mellan felaktiga och fungerande komponenter kan analytiker identifiera nyckelkomponenter som orsakar defekta produkter.  Multivariata data och multivariata tidsseriedata ställer höga krav på visualiseringar på grund av deras komplexitet. Den höga dimensionaliteten kan leda till problem som överlappning och dolda mönster beroende på vilken visualiseringsteknik som används. För att uppnå effektiv visualisering av multivariata data och multivariata tidsseriedata krävs det att både trender över tid och korrelationer mellan olika variabler visas. Studien genomfördes i samarbete med konsultföretaget Syntronic AB för att identifiera lämpliga visualiseringstekniker för data som samlats in vid testning av kretskort. Metoden som användes är design science, vilket omfattar en litteraturstudie, utveckling av prototyp och utvärdering av prototypen. Prototypen består av tre visualiseringstekniker som är: Kategorisk heatmap, Parallella koordinater och Scatterplot. Dessa tekniker jämfördes systematiskt för att bedöma deras effektivitet. Utvärderingen består av kvantitativa metoder såsom mätningar och enkäter, samt den kvalitativa metoden intervju. Resultatet av studien presenterar den utvecklade prototypen och analysen av utvärderingen.  Resultatet av studien visar att kategoriska heatmaps är effektiv för att identifiera samband mellan avvikelser i multivariat data. Även om alla användare upplevde visualiseringen svårtolkad vid en första anblick uttryckte de att visualiseringen var effektiv på att visa korrelationer mellan avvikelser. Parallella koordinater upplevdes svårtolkad och ineffektiv på grund av den höga dimensionaliteten där alla dimensioner inte kan visas samtidigt. Förbättringsförslag för att öka användarvänlighet och användarupplevelse lyftes där tree view förslogs som ett alternativ för att välja de dimensioner som ska visas i stället för reglaget. Scatterplots visade sig vara användbar för att analysera enskilda testpunkter och visade generella trender på ett tydligt och begripligt sätt. Studien har även visat att interaktiviteten påverkar upplevelsen av visualisering, där begränsad interaktivitet medför att tekniken upplevds mindre användbar för att identifiera relationer mellan avvikelser. / Visualization is of great importance when analyzing data, especially when distinguishing anomalies. Identifying faulty components of electronics could evolve and improve the production processes tremendously. By effectively displaying the correlation between faulty and working components, analytics can identify key components causing faulty products.Multivariate data and multivariate time series data place high demands on visualizations due to their complexity. The high dimensionality can lead to issues such as overlapping and hidden patterns, depending on the visualization technique used. To achieve effective visualization of multivariate data and multivariate time series data, it is necessary to show both trends over time and correlations between different variables. This study was conducted in cooperation with Syntronic AB, a consulting company, to help identify suitable visualization techniques for data gathered by testing circuit boards. The methodology used is design research which includes research gathering, development of a prototype and evaluation of the prototype. The prototype consists of three visualization techniques: Categorical heatmap, Parallel Coordinates, and Scatterplot. These techniques were systematically compared to assess their effectiveness. The evaluation consists of quantitative methods such as time measurement and survey, and the qualitative method interview. The result of the study shows the developed prototype and the analysis of the evaluation.  As a result, the study found categorical heatmaps effective in distinguishing correlation between anomalies in multivariate data. Although all users found the visualization difficult to grasp at first glance, expressed their beliefs regarding the effectiveness of displaying correlation. Parallel Coordinates were perceived as difficult to interpret and ineffective for high-dimensional datasets where all dimensions can´t be displayed simultaneously. Interactive options such as tree view to select test pointsto visualize were suggested to further improve the usefulness of Parallel Coordinates. Scatterplot proved useful for analyzing individual test points and showed general trends in a user-friendly way. Furthermore, the study also showed that interactivity affect the perception of visualizations. Limited interactivity resulted in users finding the visualizations less effective in distinguishing anomalies and were perceived as less user-friendly.

Traditional visualization techniques for multidimensional data sets, such as parallel coordinates, star glyphs, and scatterplot matrices, do not scale well to high dimensional data sets. A common approach to solve this problem is dimensionality reduction. Existing dimensionality reduction techniques, such as Principal Component Analysis, Multidimensional Scaling, and Self Organizing Maps, have serious drawbacks in that the generated low dimensional subspace has no intuitive meaning to users. In addition, little user interaction is allowed in those highly automatic processes. In this thesis, we propose a new methodology to dimensionality reduction that combines automation and user interaction for the generation of meaningful subspaces, called the visual hierarchical dimension reduction (VHDR) framework. Firstly, VHDR groups all dimensions of a data set into a dimension hierarchy. This hierarchy is then visualized using a radial space-filling hierarchy visualization tool called Sunburst. Thus users are allowed to interactively explore and modify the dimension hierarchy, and select clusters at different levels of detail for the data display. VHDR then assigns a representative dimension to each dimension cluster selected by the users. Finally, VHDR maps the high-dimensional data set into the subspace composed of these representative dimensions and displays the projected subspace. To accomplish the latter, we have designed several extensions to existing popular multidimensional display techniques, such as parallel coordinates, star glyphs, and scatterplot matrices. These displays have been enhanced to express semantics of the selected subspace, such as the context of the dimensions and dissimilarity among the individual dimensions in a cluster. We have implemented all these features and incorporated them into the XmdvTool software package, which will be released as XmdvTool Version 6.0. Lastly, we developed two case studies to show how we apply VHDR to visualize and interactively explore a high dimensional data set.

The aim of this diploma thesis is to find suitable regression models between the chosen statistical data of transport and GDP per capita and determine whether these models are statistically significant. The first part is a theoretical introduction to the problems of transport in terms of economics, as well as a description of methods of regression analysis, which is used in the practical part. The practical part draws data from Eurostat's website that provide for this thesis sufficient statistical basis. The data are graphically processed as scatter charts, based on these are determined mathematical equation of regression. The choice of suitable regression analysis is based on coefficient of determination and significance level is alpha = 0,05.

In the last two decades the study of changes in the genome function that are not induced by changes in DNA has consolidated a strong research field called ”epigenetics”. Chromatin state changes play an essential role in the regulation of transcription of many genes, thus controlling cell differentiation. A large part of these changes is due to histone modifications that alter the accessibility of the DNA. Current state of the art visualization methods for the analysis of epigenetic data sets are not suited to represent the relationship between the combinatorial pattern of histone modifications and their regulatory effects.

This work presents a novel method for enhancing multi-class scatterplots with animated patterns. Each class in a scatterplot is assigned its own pattern that changes over time. This pattern determines the coloring of the class’s points, representing motion over time to make the points be perceived as connected to each other. As such it enables the task of identifying or perceiving the differences between different classes, even in greyscale. A user study was conducted to evaluate the efficiency of greyscale scatterplot patterns at identifying classes compared to other scatterplot methods. The results show slightly worse effectiveness than the baseline of colored multi-class scatterplots, while also showing greater effectiveness than greyscale scatterplots. / Det här arbetet presenterar en ny metod för att utöka flerklassade scatterplots med animerade mönster. Varje klass i en scatterplot tilldelas ett mönster som ändras med tiden. Detta mönster bestämmer färgläggningen av klassens punkter och representerar rörelse över tid vilket får punkterna att tillsammans uppfattas som sammanhängande. Denna metod möjliggör uppgiften att identifiera eller åskådliggöra skillnaderna mellan olika klasser, även i gråskala. En användarstudie genomfördes för att utvärdera effektiviteten av gråskaliga scatterplotmönster vid identifiering av klasser i jämförelse med andra metoder. Resultaten visar något sämre effektivitet än baslinjen av färgade flerklassade scatterplots, men visar också på bättre effektivitet än gråskaliga scatterplots.

This diploma thesis evaluates and processes data from final device checks. All the devices are similar types of thermal overcurrent relays by the ABB company. For appropriate statistical data processing, the Minitab 14 statistical software was used and various statistical methods were applied. Results are always listed for each device type and each method used. The diploma thesis is divided into two parts. The first one analyzes the methods used and the second part states the method results. There is also an overall evaluation of the processed data.