Download AYÇAGÜL ÇATAL (2) Data organization

AYÇAGÜL ÇATAL
(2) Data organization - It is concerned with the most efficient way to present data, as in
graphs, charts, tables, or diagrams. Some graphs serve a definite purpose. Bar graphs, for
instance, show the respective quantities of discrete objects through bar lengths. Pie graphs
show quantities and percentages through proportional sectors of a circle.
(3) Data interpretation - This is the where all the formulae of statistical analysis are used.
Data interpretation depends on the type of conclusions sought. Here two other divisions of
statistics should be examined. Descriptive statistics concerns itself with the general attributes
of the data being studied. It is just showing the characteristics of the given data. Examples
include getting the measures of distribution (frequency distribution, histogram, stem-and-leaf
plotting), measures of central tendency (mean, median, mode), and measures of dispersion
(e.g. range and standard deviation). Inferential statistics concerns itself with deriving
conclusions beyond the given data. Remember that most statistical studies use samples instead
of entire populations.
Inferential statistics is used, given the data from the sample, to make conclusions about the
general population where the sample comes from. The most common inferential statistics
methods are t-test, ANOVA (analysis of variance), regression analysis, and chi-square
analysis. Statistics has plenty of real-world applications, the most common of which is
interpreting scores and conducting surveys:
Interpreting scores include plenty of descriptive statistics, like this: mean - average score;
median - the score where there are equal quantities of scores higher and lower than that score;
mode - the most frequently occurring score; range - the difference between the highest and
lowest score; and standard deviation - a measure of how 'stable' the scores are, or how far
apart the scores are from the mean. Surveying often requires massive amounts of descriptive
and inferential statistics. Furthermore, there should be extra sensitivity in selecting
respondents and organizing survey results so that the conclusions derived from statistical
analysis will be as impartial as possible.