Geography 200 Mid Term
Descriptive Statistics
summarize pertinent characteristic of a data set
Inferential Statistics
used to make generalization about a population on the basis of a sample--includes estimation
and hypothesis testing
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Discrete Data
1.only certain values are possible 2. exist only for particular points in space 3. exist only for
particular points in time (Ex. Number of people, number of tripes to work, total population)
Continuous Data
values exist for all points in space or time, and/or all values are possible (Ex. height of a person,
distance to work, life expectancy of population)
Qualitative Data
are data that cannot be readily represented using numbers--may include opinions, sights,
sounds, smells, feelings, emotions, etc.--these are equally valid, but require different modes of
interpretation and analysis
Quantitative Data
, are numerical measurements or representations
Precision
the "exactness" or the "specificity" of the measurement
Accuracy
the level of which measurements match the "actual" values--increasing levels of accuracy can
be achieved by better calibration, and a reduction in system-wide bias
Reliability
is defined by consistency and replicability of measurements
Validity
deals with appropriateness, usefulness, or meaning of a measurement.
Deterministic Process
can theoretically be specified with (practically) 100% certainty (eg gravity, solar radiation,
chemical reactions)
Probabilistic Process
cannot be specified with complete certainty (eg most facets of human behavior, cultural
processes, complex physical system functions)
The multiplication (AND) rule
events are statistically independent if the outcome of one does not influence the outcome of the
other (flipping a coin repeatedly)
- probability can be calculated through multiplication of individual probabilities
the Addition (OR) rule
when two outcomes within the same event space are mutually exclusive, the probability of one
OR the other happening can be calculated by adding the probabilities of each outcome
What does it mean for a classification system to be mutually exclusive and exhaustive? Provide
an example that illustrates both
Descriptive Statistics
summarize pertinent characteristic of a data set
Inferential Statistics
used to make generalization about a population on the basis of a sample--includes estimation
and hypothesis testing
Previous
Play
Next
Rewind 10 seconds
Move forward 10 seconds
Unmute
0:00
/
0:15
Full screen
Brainpower
Read More
Discrete Data
1.only certain values are possible 2. exist only for particular points in space 3. exist only for
particular points in time (Ex. Number of people, number of tripes to work, total population)
Continuous Data
values exist for all points in space or time, and/or all values are possible (Ex. height of a person,
distance to work, life expectancy of population)
Qualitative Data
are data that cannot be readily represented using numbers--may include opinions, sights,
sounds, smells, feelings, emotions, etc.--these are equally valid, but require different modes of
interpretation and analysis
Quantitative Data
, are numerical measurements or representations
Precision
the "exactness" or the "specificity" of the measurement
Accuracy
the level of which measurements match the "actual" values--increasing levels of accuracy can
be achieved by better calibration, and a reduction in system-wide bias
Reliability
is defined by consistency and replicability of measurements
Validity
deals with appropriateness, usefulness, or meaning of a measurement.
Deterministic Process
can theoretically be specified with (practically) 100% certainty (eg gravity, solar radiation,
chemical reactions)
Probabilistic Process
cannot be specified with complete certainty (eg most facets of human behavior, cultural
processes, complex physical system functions)
The multiplication (AND) rule
events are statistically independent if the outcome of one does not influence the outcome of the
other (flipping a coin repeatedly)
- probability can be calculated through multiplication of individual probabilities
the Addition (OR) rule
when two outcomes within the same event space are mutually exclusive, the probability of one
OR the other happening can be calculated by adding the probabilities of each outcome
What does it mean for a classification system to be mutually exclusive and exhaustive? Provide
an example that illustrates both
