DSC 456 Quizzes 2023 with complete solution
Order the visual encodings (aka mappings) in terms of their effectiveness at allowing a
user to perceive a specific value or a close comparison between values.
1= Most Effective
4= Least Effective
1- Length/distance with common baseline
2- Length/distance with no common baseline
3-Area
4- Color
According to Weber's Law, perception is not simply on absolute terms, but
We perceive differences as a function of the magnitude of the original (i.e. we perceive
percentage changes)
Comparing values between Case 1, Item B and Case 3, Item E requires using which
channel?
Position with an unaligned baseline
What do we mean by pre-attentive attributes?
visual cues that are visually apparent very quickly (~200ms) and do not require
intentional processing to see
Gestalt psychology describes
how the human brain finds structure in visual stimuli
Based on Steven's Power Law we know the specific coefficients for the linearity of
perception of different stimuli like heat, brightness and visual length.
False
Which of the following is a reason to use data visualization?
1. Take advantage of human visual processing machinery for insight generation
2. Can get high information density in an image.
3. Conveys patterns that may be difficult to describe.
Which of the following describe(s) a difference between Exploratory and Explanatory
visualization?
1. Explanatory visualization should be carefully crafted through multiple drafts.
2. Exploratory visualizations are usually made quickly and many at a time.
In data science, visualization is used
at the beginning of the process to understand data, in the middle to debug results and at
the end to communicate.
Which of the following describes the guiding principle for making decisions about how to
visualize data?
Take into account the data, the people who will see the visualization, and what you want
them to understand when they see it (data, audience, message).
Major differences in the character of datasets can be invisible to summary statistics.
True
Tableau allows you to create new visualizations by:
1. Dragging and dropping variables onto the axes.
2.Selecting variables and choosing a recommended 'Show Me' graph.
, Good for categorical X values and cases where the Y value is ratio scaled.
Bar Chart
Can handle multiple Y values per X value
Scatter Plot
mplies some importance of the connection between the data points.
Line Graph
Forget about using color or multiple lines or different shapes or other marks or
adornment for the moment. In terms of how many data variables can be mapped to the
screen, bar charts, line graphs and scatter plots are the same.
True
Line graphs show a different type of pattern from bar graphs because
the lines imply there is a connection between the plotted points, often helping showcase
a trend.
As opposed to bar graphs or line graphs, scatter plots
1. can have more than one Y value per X value.
2. are used to show sampled data (each point is a sample taken of both X and Y axis
variables).
With both dot charts and bar charts, the axis order is critical.
True
Which of the following are true about stacked bar graphs?
1. Stacked bar graphs allow you to show information about subgroups of each bar of a
bar chart.
2. A proportional stacked bar graph can do the work of several individual pie charts, but
may not solve the difficulty reading that same data.
3. Stacked bar graphs can be very poor when comparing the size of one subcategory
across multiple bars, especially looking for fine distinctions.
An area chart
1. may help with difficulties perceiving changes to the size of a segment of a stacked
bar graph.
2. uses the area perception channel and thus requires caution (especially can
overemphasize the wrong information).
In our first lab, we looked at a data transformation called melting (or pivoting) that
changes the shape of data so that we could map variables to visual features. Which of
these describes how that process works.
1. Each data value in the pivoted columns gets its own row in the transformed data.
2. A new variable is created that tells which of the original columns a data value came
from.
One critical attribute of good data visualization is that data themselves are emphasized.
One way to make the data stand out is to:
Remove clutter
One criteria for making sure the data are the most prominent feature of a visualization is
the
Data to ink ratio
When graphical integrity is compromised, a graph can be misleading (intentionally or by
accident). Watch out for
Order the visual encodings (aka mappings) in terms of their effectiveness at allowing a
user to perceive a specific value or a close comparison between values.
1= Most Effective
4= Least Effective
1- Length/distance with common baseline
2- Length/distance with no common baseline
3-Area
4- Color
According to Weber's Law, perception is not simply on absolute terms, but
We perceive differences as a function of the magnitude of the original (i.e. we perceive
percentage changes)
Comparing values between Case 1, Item B and Case 3, Item E requires using which
channel?
Position with an unaligned baseline
What do we mean by pre-attentive attributes?
visual cues that are visually apparent very quickly (~200ms) and do not require
intentional processing to see
Gestalt psychology describes
how the human brain finds structure in visual stimuli
Based on Steven's Power Law we know the specific coefficients for the linearity of
perception of different stimuli like heat, brightness and visual length.
False
Which of the following is a reason to use data visualization?
1. Take advantage of human visual processing machinery for insight generation
2. Can get high information density in an image.
3. Conveys patterns that may be difficult to describe.
Which of the following describe(s) a difference between Exploratory and Explanatory
visualization?
1. Explanatory visualization should be carefully crafted through multiple drafts.
2. Exploratory visualizations are usually made quickly and many at a time.
In data science, visualization is used
at the beginning of the process to understand data, in the middle to debug results and at
the end to communicate.
Which of the following describes the guiding principle for making decisions about how to
visualize data?
Take into account the data, the people who will see the visualization, and what you want
them to understand when they see it (data, audience, message).
Major differences in the character of datasets can be invisible to summary statistics.
True
Tableau allows you to create new visualizations by:
1. Dragging and dropping variables onto the axes.
2.Selecting variables and choosing a recommended 'Show Me' graph.
, Good for categorical X values and cases where the Y value is ratio scaled.
Bar Chart
Can handle multiple Y values per X value
Scatter Plot
mplies some importance of the connection between the data points.
Line Graph
Forget about using color or multiple lines or different shapes or other marks or
adornment for the moment. In terms of how many data variables can be mapped to the
screen, bar charts, line graphs and scatter plots are the same.
True
Line graphs show a different type of pattern from bar graphs because
the lines imply there is a connection between the plotted points, often helping showcase
a trend.
As opposed to bar graphs or line graphs, scatter plots
1. can have more than one Y value per X value.
2. are used to show sampled data (each point is a sample taken of both X and Y axis
variables).
With both dot charts and bar charts, the axis order is critical.
True
Which of the following are true about stacked bar graphs?
1. Stacked bar graphs allow you to show information about subgroups of each bar of a
bar chart.
2. A proportional stacked bar graph can do the work of several individual pie charts, but
may not solve the difficulty reading that same data.
3. Stacked bar graphs can be very poor when comparing the size of one subcategory
across multiple bars, especially looking for fine distinctions.
An area chart
1. may help with difficulties perceiving changes to the size of a segment of a stacked
bar graph.
2. uses the area perception channel and thus requires caution (especially can
overemphasize the wrong information).
In our first lab, we looked at a data transformation called melting (or pivoting) that
changes the shape of data so that we could map variables to visual features. Which of
these describes how that process works.
1. Each data value in the pivoted columns gets its own row in the transformed data.
2. A new variable is created that tells which of the original columns a data value came
from.
One critical attribute of good data visualization is that data themselves are emphasized.
One way to make the data stand out is to:
Remove clutter
One criteria for making sure the data are the most prominent feature of a visualization is
the
Data to ink ratio
When graphical integrity is compromised, a graph can be misleading (intentionally or by
accident). Watch out for
