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* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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.