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BIT studying
Chapter 1
 Descriptive statistics= (aka Exploratory Data Analysis) Concerned w/ numerical &
graphical techniques for describing some characteristics of the data.
Inferential statistics= make inferences about a data set based on the info obtained from a
sample. The info used to make these inferences is some type of descriptive statistic.
population= largest set of subjects that you went to make inferences about.
Variable= some characteristic of the population you want to measure. Subject to change
trialtotrail.
Sample= a subset of the population of interest.
Statistical Inference= a statement about the population based on data collected in the
sample.
Reliabilty?
 Random Sample= on each selection, each subject remaining in the population has the
same.
 Sampling with Replacement= set subject & then get more. Etc.
 Sampling without t Replacement= sample and don’t replace. Better, more accurate
Qualitative= a variable whose measurement change in kind but not degree. Cannot
arrange by magnitude.
Quantitative= A variable whose measurement changes in magnitude from trialtotrial
such that some order or ranking can be applied.
 Discrete= A variable whos measurement can assume a countable # of variables.
Continuous= A variable whos measurement can assume any one of
a countless # of
values in a little interval.
Nominal= measurement that classify the object by name only, with the name being
categories of the object.
Ordinal= Measurement can be ranked from low to high but we cannot assign a distance
between ranks
Interval we can determine the distance between two measurements can be ranked, but
the origin is undefined.
 Ratio= Levels can be ranked, distances can be established, and there is a meaning of
origin that allows us to form ratios.
Chapter 2 and 3
 Bar graph=
1) each possible outcome of a qualitative variable is listed as a class.
2) the number of times each outcome is observed in the data is recorded as the
variable’s frequency, denoted Fi
3) Classes can be listed as row or column heading
4) tables can also be used to offer a comparison of percentages of a category
5) vertical scale should start at 0
Pie Chart=
1) Partitions a set of measurements into a few categories, much as you might slice a
pie.
2) best suited for displaying percentages
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 Histogram=
1) First select the number of class intervals
2) class interval are selected so that they do not overlap, no gaps, and they are
equal width
3) equal width is called class width
4) given # of intervals, we then calculate the range.
5) Range= Max. Obs Min Obs
6) Calculate interval width
7) Class width= Range/ # of Interval
~
Given number of intervals and class width, we need to choose class intervals.
~ROT= choose the lower limit of the first interval to be 0 or a multiple of the class width
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 Fall '08
 PLKitchin

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