![]() ![]() ![]() A cumulative distribution is useful for finding percentiles which reflect what percentage of the sample scored at a specific value or below. ![]() Table 5.1 also shows the ‘Valid Percent’ which is computed only for those inspectors in the sample who gave a valid or non-missing response.įinally, it is possible to add up the ‘Valid Percent’ values, starting at the low score end of the distribution, to form the cumulative distribution or ‘Cumulative Percent’. Table 5.1 shows the ‘Percent’ or relative frequency of each score (the percentage of the 112 inspectors obtaining each score, including those inspectors who were missing scores, which SPSS labels as ‘System’ missing). It is possible to compute various percentages and percentile values from a frequency distribution. For each value of a variable, the frequency of its occurrence in the sample of data is reported. The display of frequency tabulation is often referred to as the frequency distribution for the sample of scores. Consider the following questions that Maree might wish to address with respect to decision accuracy and speed scores: Reflect on the QCI research scenario and the associated data set discussed in Chap. What remains after their application is simply for us to interpret and tell the story. These statistical procedures are designed to identify or display specific patterns or trends in the data. Rather we utilise procedures and measures which provide a general depiction of how the data are behaving. We seldom interpret individual data points or observations primarily because it is too difficult for the human brain to extract or identify the essential nature, patterns, or trends evident in the data, particularly if the sample is large. By ‘describe’ we generally mean either the use of some pictorial or graphical representation of the data or the computation of an index or number designed to summarise a specific characteristic of a variable or measurement. The purpose of the procedures and fundamental concepts in this category is quite straightforward: to facilitate the description and summarisation of data. The first broad category of statistics we discuss concerns descriptive statistics. Along the way, we explore the fundamental concepts of probability and the normal distribution. a histogram, box plot, radar plot, stem-and-leaf display, icon plot or line graph) or the computation of an index or number designed to summarise a specific characteristic of a variable or measurement (e.g., frequency counts, measures of central tendency, variability, standard scores). By ‘describe’ we generally mean either the use of some pictorial or graphical representation of the data (e.g. The purpose of the procedures and fundamental concepts reviewed in this chapter is quite straightforward: to facilitate the description and summarisation of data. This chapter discusses and illustrates descriptive statistics.
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