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Comm 337 > Chapter 7 > Flashcards

Chapter 7 Flashcards

1
Q

4 categories of data analytics

A

descriptive
diagnostic
predictive
prescriptive

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2
Q

computing profit margins and leverage ratios to examine if business risk changed during a period or to identify possible fraud is an example of

A

descriptive analytic

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3
Q

answers “what happened” questions

A

descriptive analytics

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4
Q

used to understand how the business is performing

A

descriptive analytics

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5
Q

descriptive analytics use __________ analysis techniques

A

exploratory data

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6
Q

goes beyond examining what happened to answering “why did this happen”

A

diagnostic analytics

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7
Q

using logic and basic tests to try and reveal relationships in the data that explain why something happened

A

diagnostic analytics

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8
Q

sometimes with diagnostic analytics we test a

A

hypothesis

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9
Q

the incorrect rejection of a true null hypothesis

A

Type 1 error

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10
Q

failure to reject the null hypothesis

A

Type 2 error

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11
Q

answer the question “what is likely to happen in the future?”

A

predictive analytcis

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12
Q

predictive analytics use __________ to find patterns likely to manifest themselves in the future

A

historical data

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13
Q

to be successful, predictive analytics require that future events are predictable based on

A

past data

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14
Q

used to create the model for future predictions

A

training dataset

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15
Q

used to access how well the model predicts the target outcome

A

test dataset

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16
Q

occurs when a model fits a training dataset well but doesn’t predict well when applied to other datasets

A

data overfitting

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17
Q

balance the accuracy of predicting the target outcome correctly with overfitting the data by examining the performance of the model on the test dataset

A

validation test

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18
Q

data items that take on a limited number of assigned values to represent different groups

A

categorical data

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19
Q

1) answer the question “which one”

2) answer the question “how much”

A

1) categorical data

2) numeric data

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20
Q

when the target outcome is a categorical value, use

A

classification analysis

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21
Q

various techniques that identify characteristics of groups and then tries to use those characteristics to classify new observations into 1 of those groups

A

classification analysis

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22
Q

answer the question “what should be done”

A

prescriptive analytics

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23
Q

can be either recommendations or programmed actions a system can take based on results

A

prescriptive analytics

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24
Q

UPS drivers using system to update and optimize delivery routes to save time is an example of a

A

prescriptive analytic

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25
Q

prescriptive analytics use techniques like _______ and _______ to generate predictions

A
  • artificial intelligence

- machine learning

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26
Q

data is of no value if the underlying data is not of

A

high quality

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27
Q

process of estimating a value that is beyond the data used to create the model

A

extrapolation beyond the range

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28
Q

often ppl will misuse data analysis to report a single number as a prediction and believe that the outcome will be that number

A

failing to consider the variation

29
Q

the spread of the data about a prediction

A

variation

30
Q

2 examples of misusing a well-designed model

A

1) extrapolation beyond the range

3) failing to consider the variation

31
Q

is processed faster than written data

A

visualized data

32
Q

are easier to use and support the dominant learning style

A

visualizations

33
Q

choosing the right _________ strengthens the ability to communicate effectively

A

visualization

34
Q

5 main purposes for visualizations

A

1) comparison
2) correlation
3) distribution
4) trend evaluation
5) part-to-whole

35
Q

the most common reason to create visualizations in business

A

comparison

36
Q

most commonly used charts for comparison reasons (2)

A
  • bar charts

- column charts

37
Q

is a variation of a bar chart with a bullet or small line by each bar that indicates an important benchmark

A

bullet chart

38
Q

this chart allows the graph to show more info (vs a bar or column chart)

A

bullet chart

39
Q

comparing how 2 numeric variables fluctuate with eachother

A

correlation

40
Q

2 examples of charts used for correlational reasons

A
  • scatterplot with regression line

- heatmap

41
Q

looks like a data table but shows colours that relate to magnitude of the different entries

A

heatmap

42
Q

allows a representation of correlation between numeric and non-numeric fields

A

heatmap

43
Q

shows the spread of numeric data values

A

distribution

44
Q

helps develop a deeper understanding of data by examining simple descriptive statistics

A

distribution

45
Q

2 types of charts used for distribution

A

1) histograms

2) boxplot

46
Q

single numeric value divided into equal-sized bins, and bin sizes are listed on the x-axis

A

histogram

47
Q

shows changes over an ordered variable (often a measurement of time)

A

trend evaluation

48
Q

difference between visualizations showing trends or correlations is that the axis in a trend visualization is

A

ordered

49
Q

2 types of charts used for trend evaluations

A

1) line chart

2) area chart

50
Q

area charts are same as a line chart except the area between the lines and the x-axis are

A

filled in

51
Q

type of chart that is useful when trying to show a progression overtime

A

area chart

52
Q

shows which items make up the parts of a total

A

part-to-whole

53
Q

2 types of charts used for part-to-whole

A

1) pie chart

2) tree maps

54
Q

most overused and misused visualization

A

pie chart

55
Q

are most appropriate when showing percentages that sum up to 100% and data only has few categories

A

pie charts

56
Q

uses nested rectangles to show the amount that each group contributes

A

tree maps

57
Q

high-quality visualizations follow 3 important design principles

A

1) simplification
2) emphasis
3) ethical presentation

58
Q

ensuring the most important message is easily identifiable

A

emphasis

59
Q

clearly and concisely communicated the object of the visualization

A

simplification

60
Q

visualizations can be simplified by considering 3 important techniques

A

1) quantity
2) distance
3) orientation

61
Q

visualizations are most impactful when the contain not too much, not too, little, but just the right amount of data, also called the

A

Goldilocks principle

62
Q

removing _______ aids in understanding and also removes unnecessary info

A

distance

63
Q

data and text is easier to understand if oriented or rotated to be

A

horizontal

64
Q

most visualizations are created to help improve

A

decision-making quality

65
Q

3 different techniques used to emphasize data more effectively

A

1) highlighting
2) weighting
3) ordering

66
Q

refer to the amount of attention an element attracts

A

visual weights

67
Q

the intentional arranging of visualization items to produce emphasis

A

data ordering

68
Q

a graphical depiction of info, designed with/without intent to deceive

A

data deception

69
Q

graphical depiction that may create a belief about the message which varies from the actual message

A

data deception

Comm 337 (12 decks)

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