In a paper consisting of twelve pages statistic questions are answered involving such topics as arrays, raw data, leaf and stem displays versus histograms, frequency distribution and central tendency measures. Thirteen sources are cited in the bibliography.
Name of Research Paper File: D0_MTbizst2.rtf
Unformatted Sample Text from the Research Paper:
Continuous data is a set of data in which the values or observations belonging to the data can take on any particular value with and a
finite or infinite interval (University of Glasgow, (a) 2003). Continuous data can be counted, ordered and measured (University of Glasgow, (a)
2003). An example of continuous data would be height and weight, temperature, the amount of sugar a soft drink or an orange might have or the time required to run
a mile (University of Glasgow, (a) 2003). A continuous set of data in this example could involve a measurement, overtime amounts, or someones weight.
On the other hand, data is said to be discrete if the values belonging to that set are different, distinct or separate from another (University of Glasgow,
(b) 2003). Much like continuous data, discrete data can be counted -- and examples of this kind of data would be the number of kittens in a letter,
gender analysis or a blood group (University of Glasgow, (b) 2003). To summarize, continuous data is data representing one class or
set. Discrete data involves different sets. What are the advantages of putting raw data into an array? What information can be readily found from an array?
In order to answer this question, it would first be helpful to define both raw data and array (Sytsma, 2003). Raw data, in its most basic form, is the data
as is, data that has just been collected and not yet sorted or analyzed (Sytsma, 2003). The data array then, is basically a way to organize the data so that