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CH2
:
Context
: who,what,when,where,why,how.
Case
: An individual we have data for*
Catagorical
: categories(words/numerical)*
Quantiative
:
Numerical w/ units*
WCGW*
Numbers can be categorical (area codes)
*CH3*
Frequency table
: Table that lists the categories in a categorical
variable and gives the count (or %) of observations for each category.*
Distribution
: Gives possible values and relative frequency of each
value*
Area principle
: In a stats display, each data value should be represented by the same amount of area*
Categorical data condition
:
Categorical graphs aren’t good for quantitative variables*
Contingency table
: Table that displays counts and sometimes % for individuals falling
into named categories on 2 or more variables.
The table categorizes the individual on all variables at once, revealing patterns.*
Marginal
Distribution
: In a contingency table, the distribution of either variable alone is called the MD.
The counts or %s are the totals found in the
margins (last row) of the table*
Conditional distribution
: Distrubution of a variable restricting the who to consider only a smaller group of
individuals*
Independence
: Variables are independent if the conditional distribution of one variable is the same for each category of the
other*
Segmented bar chart
: Displays the conditional distribution of a categorical variable within each category of another variable*
Simpson’s
paradox
:When averages are taken across different groups, they can appear to contradict the overall averages*
WCGW
*Don’t violate the area
principle!*Make sure percentages add up and units match*Don’t confuse similar sounding percentagesread carefully*Don’t forget to look aat
the variables separately*Don’t overstate your case: Most variables aren’t entirely independent of each other. Simpson’s Paradox: when
comparing different variables make sure that the quantities you are averaging are comparable (average elevation with populationunrelated)
*
CH4*
Histogram
: Uses adjacent bars to show the distribution of a quantitative variable*Gap: A region of distribution where there are no
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This note was uploaded on 09/30/2008 for the course ILRST 2100 taught by Professor Vellemanp during the Fall '07 term at Cornell University (Engineering School).
 Fall '07
 VELLEMANP

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