Key words Flashcards

1
Q

Nominal Scale

A

Labels or names used to identify an attribute of the element

Ex: M/F, Married/Single, Eye Color: Blue/Brown/Grey/Etc.

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2
Q

Ordinal Scale

A

Data exhibiting properties of nominal data and the order or rank of data is also meaningful
Ex: Old age, Middle age, Young adulthood; High/Low

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3
Q

Interval Scale

A

Data has all the properties of ordinal data and the interval between values is expressed in terms of a fixed unit of measure. Interval data are always numerical, can be ranked.
} Ex: SAT scores (620, 550, 470), Credit history scores

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4
Q

Ratio Scale

A

Data exhibiting all properties of interval data and ratio of two values is also meaningful, i.e., there is a meaningful zero value.
} Ex: Distance, Height, Price (Sofa from West Elm costing $1600 is twice as expensive as sofa from IKEA costing $800.)

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5
Q

Data Measurement Levels

A
Ratio/Interval Data  (Highest Level Complete Analysis)
Ordinal Data   (Higher Level Mid-level Analysis)
Nominal Data   (Lowest Level Basic Analysis)
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6
Q

Data Types: Categorical and Numerical Data

A
Data
   Qualitative (Categorical)
Examples:
- Marital Status
- Political Party n Eye Color
(Defined categories)

Discrete
Examples:
- Number of Children n Defects per hour
(Counted items)

Continuous
Examples:
- Weight n Voltage (Measured
characteristics)

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7
Q

Cross Sectional Data

A

Data values observed at a

single point in time.

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8
Q

Time Series Data

A

Data collected over

several time periods.

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9
Q

Tabular summary

A

Frequency, Percent Frequency, etc.

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10
Q

Graphical Summary

A

Bar Charts, Histograms, etc.

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11
Q

Numerical

A

Mean, Median, Standard Deviation, etc.

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12
Q

Relative frequency

A

of a class equals the fraction or proportion of items belonging to a class.

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13
Q

A bar chart can be used to

A

summarize categorical data

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14
Q

A useful feature of bar charts is that they can display

A

multiple issues

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15
Q

A pie chart is

A

another graphical device for depicting relative frequency, or percent frequency for categorical data.

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16
Q

Line Charts are

A

effective tools to represent data that are measured over time (e.g., monthly, quarterly, annually)

17
Q

A Scatter diagram is

A

a graphical representation of the relationship between two quantitative variables.

18
Q

A histogram is constructed

A

by placing the bins on the horizontal axis and the frequency, relative frequency, or percent frequency on the vertical axis.

19
Q

Mean =

A

The most common measure of central tendency

Mean = sum of values divided by the number of values } Affected by extreme values (outliers)

20
Q

Weighted Mean

A

The mean of data values that have been weighted according to their relative importance
is useful in computing the expected value of a random variable

21
Q

A percentile provides information about

A

how the data are spread over the interval from the smallest value to the largest value.

22
Q

Basic Elements of Probability Theory

A

Experiment
Elementary outcome
Sample Space (S)

23
Q

Mutually Exclusive =

A

no overlap between events

24
Q

Cumulative distribution function (CDF):

A

The probability a random variable X takes on a value less than or equal to x.

25
The variance of a random variable X is
the weighted sum of its squared deviation from its mean
26
Standard deviation
the square root of the variance
27
Binomial distribution Definition
Used to count the number of successes (or failures) from a random sample of size n when each trial has a probability of success p.
28
Poisson Distribution Definition
Used to count the number of occurrences of a particular event during an interval of time.
29
Discrete random variable
when there are finitely many or countable number of values of a random variable
30
Continuous random variable
when a random variable can take any real-number value (in an open or closed range)
31
Uniform Distribution Definition
Simplest continuous distribution, used to model quantities which are “equally likely to take any value” in a given range.
32
Normal Distribution
Mostcommoncontinuous distribution used to model quantities which have a “central tendency” with concentration around the center.