Questions and Answers
A Chernoff Face glyph
The Gestalt laws
An example of 'focus and context' system in a visualisation is
Colour
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In relation to data encoding, which statement is correct?
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In the context of data collection, which statement is true?
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How many scatterplots are in a SPLOM, used to present 9-dimensional data?
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Which statement about large graphs is correct?
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What does a single-axis composition generally mean?
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Which representation would be most effective for certain wildlife park data?
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In the context of data collection, which statement is false?
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Roughly how long will each iteration of the spring model take if using 10,000 objects instead of 5,000?
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Stevens’ power law describes?
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With respect to the things a viewer might find interesting in a visualization, which statement is true?
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Successful methods of drawing graphs typically?
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When creating a declarative query using a map to select a rectangular area of data, what will happen in a linked bar chart?
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What does the Visualization Pipeline highlight?
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If you consider an additional data set option in your design, how much bigger will the design space be?
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In semiotic terms, is a grey cloud on a weather map a?
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An example of 'query relaxation' in general selection is?
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When depicting bivariate data, what is a good representation choice?
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Which description applies to data sets A and B according to Munzner’s classification?
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Which algorithm would not be a good choice for analyzing complex data with fine-grained local detail and high-level clusters?
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Data collected by questionnaire is easy to collect, but not always valuable?
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Is data collected by observation always subjective?
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Which database attributes are most appropriate for managing qualitative data from interviews?
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Summative evaluation focuses on which of the following?
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In justifying design, parallel coordinates visualizations are useful because?
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Using SQL to define queries over a large database allows the user to?
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Bertin's visual variables can be characterized as?
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Study Notes
Quiz Overview
- Duration: 90 minutes time limit
- Format: 30 multiple choice questions, each with three options
- Scoring: Each question worth up to 2 marks; -1 penalty for incorrect answers
- Total score scaled to 70% of overall mark for the course
Data Collection and Analysis
- Performance data is often quantitative; preference and perception data are generally qualitative.
- A single axis composition involves aligning or overlaying visualizations based on one shared dimension.
- SPLOM (Scatterplot Matrix) displays multiple scatterplots to analyze high-dimensional data.
Visual Representation Techniques
- Heat maps and scatterplots are effective for visualizing relationships between variables.
- Large graphs can be simplified using clustering techniques while maintaining structural details.
- Query relaxation in data visualizations allows for broadening selection criteria, enhancing interactivity.
Evaluation Methodologies
- Summative evaluation assesses whether specified criteria are met, while formative evaluation focuses on improvement suggestions.
- Database attributes for qualitative analysis should include participant ID, interview text, and numerical measures for tracking efficacy.
Visualization Concepts
- Gestalt laws explain how viewers perceive visual data through principles like proximity and connectedness.
- Focus and context systems like fisheye distortion enable detailed analysis of specific data segments while maintaining an overview.
- Bertin's visual variables underlie many aspects of effective data representation.
Data Representation Challenges
- Bivariate data can have various suitable representation methods depending on if the variables are qualitative, quantitative, or categorical.
- Spring models are less effective when high-level clusters need to be preserved alongside fine-grained details.
- Chernoff faces visualize low-dimensional datasets using distinct graphical elements to represent different dimensions.
Cognitive and Perceptual Elements
- Stevens’ power law describes how physical intensity impacts perceived sensation differently across individuals.
- Interesting aspects in visualizations often include paths correlating attributes, focusing on specific targets, and investigating outliers.
Interaction Techniques
- SQL interactions allow users to see results of queries with varying detail levels, enhancing data exploration capabilities.
- Visualizations must consider audience perception and the cognitive load associated with interpreting complex data structures.
Miscellaneous Concepts
- Data classification may vary, with aspects like dimensional tables or simple relational tables being relevant for organizing data sets.
- A focus on detail versus macro-level understanding is crucial in designing algorithms for data analysis.
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