Clemson Acct 3130 Final Exam Vocab Questions
And Complete Answers
Continuous data - ANSWER One way to categorize quantitative data , as opposed to discrete
data. [BLANK] can take on any value within a range. An example is height
Declarative Visualizations - ANSWER Made when the aim of your project is to "[Blank]" or
present your findings to an audience. Charts that are [BLANK] are typically made after the
data analysis has been complete and are meant to exhibit what was found in the analysis
steps.
Discrete Data - ANSWER One way to categorize quantitative data, as opposed continuous
data. Represented by whole numbers. An example is points in a basketball game.
Exploratory Visualizations - ANSWER Made when the lines between steps P (perform test
plan), A (address and refine results), and C (communicate results) are not as clearly divided as
they are in a declarative visualization project. Often when you are exploring the data with
visualizations, you are performing the test plan directly in visualization software such as
Tableau instead of creating the chart after the analysis has been done.
Interval data - ANSWER Third most sophiscticated tpe of data on the scale of nominal,
ordinal, [BLANK], and ratio; a type of quantitative data. [BLANK] can be counted and grouped
like qualitative data, and the differences between each data point are meaningful. However,
[BLANK] do not have a meaningful 0. 0 does not mean "the absence of" but is simply another
number. An example is tempurature.
, Nominal data - ANSWER The Least sophisticated type of data; a type of qualitative data. The
only thing you can do with [BLANK] is count, group and take a proportion. Examples are hair
color and gender.
Normal distribution - ANSWER A type of distribution in which the median, mean, and mode
are all equal, so half of all the observation fall below the mean and the other half fall above
the mean. This phenomenon is naturally occurring in many data sets in our world. Can be
standardized and compared for easier analysis.
Ordinal data - ANSWER Second most sophisticated type of data; a type of qualitative data.
[BLANK] can be counted and categorized like nominal data and the categories can also be
ranked. Examples are gold, silver, and bronze medals.
Proportion - ANSWER The primary statistic used with quantitative data. [BLANK] is calculated
by counting the number of items in a particular category, then dividing that number by the
total by the total number of observations.
Qualitative Data - ANSWER Categorical data. All you can do with these data are count and
group, and in some cases, you can rank thje data. [BLANK] can be further defined in two ways:
nominal data and ordinal data. There are not as many options for charting [BLANK] because
they are not as sophisticated as [BLANK]
Quantitative data - ANSWER More complex than its sister set of data. [BLANK] can be further
defined in two ways: interval and ratio. In all [BLANK] the intervals between data points are
And Complete Answers
Continuous data - ANSWER One way to categorize quantitative data , as opposed to discrete
data. [BLANK] can take on any value within a range. An example is height
Declarative Visualizations - ANSWER Made when the aim of your project is to "[Blank]" or
present your findings to an audience. Charts that are [BLANK] are typically made after the
data analysis has been complete and are meant to exhibit what was found in the analysis
steps.
Discrete Data - ANSWER One way to categorize quantitative data, as opposed continuous
data. Represented by whole numbers. An example is points in a basketball game.
Exploratory Visualizations - ANSWER Made when the lines between steps P (perform test
plan), A (address and refine results), and C (communicate results) are not as clearly divided as
they are in a declarative visualization project. Often when you are exploring the data with
visualizations, you are performing the test plan directly in visualization software such as
Tableau instead of creating the chart after the analysis has been done.
Interval data - ANSWER Third most sophiscticated tpe of data on the scale of nominal,
ordinal, [BLANK], and ratio; a type of quantitative data. [BLANK] can be counted and grouped
like qualitative data, and the differences between each data point are meaningful. However,
[BLANK] do not have a meaningful 0. 0 does not mean "the absence of" but is simply another
number. An example is tempurature.
, Nominal data - ANSWER The Least sophisticated type of data; a type of qualitative data. The
only thing you can do with [BLANK] is count, group and take a proportion. Examples are hair
color and gender.
Normal distribution - ANSWER A type of distribution in which the median, mean, and mode
are all equal, so half of all the observation fall below the mean and the other half fall above
the mean. This phenomenon is naturally occurring in many data sets in our world. Can be
standardized and compared for easier analysis.
Ordinal data - ANSWER Second most sophisticated type of data; a type of qualitative data.
[BLANK] can be counted and categorized like nominal data and the categories can also be
ranked. Examples are gold, silver, and bronze medals.
Proportion - ANSWER The primary statistic used with quantitative data. [BLANK] is calculated
by counting the number of items in a particular category, then dividing that number by the
total by the total number of observations.
Qualitative Data - ANSWER Categorical data. All you can do with these data are count and
group, and in some cases, you can rank thje data. [BLANK] can be further defined in two ways:
nominal data and ordinal data. There are not as many options for charting [BLANK] because
they are not as sophisticated as [BLANK]
Quantitative data - ANSWER More complex than its sister set of data. [BLANK] can be further
defined in two ways: interval and ratio. In all [BLANK] the intervals between data points are
