|
Quantitative Data:
Numeric data values. i.e. heights of female volleyball
players.
Qualitative Data:
Data values that may be categorized according to a characteristic.
i.e. hair color of secretaries. Sometimes called categorical data
or categorical values.
Independent Variable:
A variable that is manipulated by an experimenter.
Dependent Variable:
The variable on which we examine the impact of the manipulation of the
independent variable. I.e. If we change the Independent
variable, do we see a change in the dependent variable?
Discrete Variables:
Variables that assume values that can be counted and will assume a
succinct value. i.e. number of days it rained in Hawaii in
2005.
Continuous Variables:
Variables that can assume all values between any two given values - i.e.
the time it takes for you to do your summer reading.
Population: All
elements that are being studied comprise a population. I.e. If we
want to study the distribution of IQ scores among politicians in
Florida, the population will be the IQ scores. In statistics, the
way we approach a problem and the formulas we use are completely
dependent on whether we are examining a population or a
sample.
Sample: A
subset of the population. I.e., Listing the IQ scores of every
tenth politician in Florida from an alphabetical listing of
politicians. In statistics, the way we approach a problem and the
formulas we use are completely dependent on whether we are examining a
population or a sample.
Census: A
sample of the entire population. I.e. We will list the IQ scores
for all politicians in Florida.
Parameter: A
characteristic of a population. I.E. the average IQ score of
politicians in Florida.
Statistic: A
characteristic of a sample. I.E. The average IQ score for
every tenth politician in Florida.
Random Sample:
A sample selected so that each group (of equal size) has the same chance
of being selected .
|
