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Z-Score, T-Score, Percentile Rank and Box-Plot Graph
BMJ, 2010
Bone mineral density (BMD) can be measured by a variety of techniques at several skeletal sites. Once measured, the manufacturers' software uses the BMD to calculate a T-score and/or Z-score. Both T-scores and Z-scores are derived by comparison to a reference population on a standard deviation scale. The recommended reference group for the T-score is a young gender-matched population at peak bone mass, while the Z-score should be derived from an age-matched reference population. T-scores and Z-scores are widely quoted in scientific publications on osteoporosis and BMD studies, and are the values used for DXA diagnostic criteria and current clinical guidelines for the management of osteoporosis. Errors in BMD measurement, differences in reference populations, and variations in calculation methods used, can all affect the actual T-score and Z-score value. Attempts to standardize these values have made considerable progress, but inconsistencies remain within and across BMD technologies. This can be a source of confusion for clinicians interpreting BMD results. A clear understanding of T-scores and Z-scores is essential for correct interpretation of BMD studies in clinical practice.
Z-scores are a means of expressing the deviation of a given measurement from the size or age specific population mean. By taking account of growth or age, Z-scores are an excellent means of charting serial measurements in paediatric cardiological practice. They can be applied to echocardiographic measurements, blood pressure and patient growth, and thus may assist in clinical decision-making.
Weathington/Understanding Business Research, 2012
The z -score is a standard deviate that allows you to use the standard normal distribution. The normal distribution has a mean of 0.0 and a standard deviation of 1.0. The normal distribution is symmetrical. The values in .1 represent the proportion of area in the standard normal curve that occurs between specific points. The table contains z -scores between 0.00 and 3.98. Because the normal distribution is symmetrical, the table represents z -scores ranging between −3.98 and 3.98.
Journal of Statistical Software, 2003
A variant of the boxplot is proposed in which the sides contain the information of a percentile plot (which is equivalent to the empirical cumulative distribution function). Unlike boxplots, there is no question about how long to draw the whiskers, nor is there loss of information due to grouping. Side-by-side comparisons of distributions are especially effective. In spite of including more detail, the impact on statisticallyuntrained readers remains similar to that of traditional boxplots.
Academia.edu
How to assign norm-referenced letter grades Different set of raw scores obtained by a sample of subjects cannot be summed up directly. Because there is no evidence that their units are identical. By annalogy we are not allowed to obtain the raw sum of monetary values declared in terms of different currencies: How can we obtain the sum total of our fortune in our bank account deposited in several currencies? In order to be able to add up 2$+5£+3€ all of the components have to be expressed in terms of identical units i.e. $, £, € or TL. Same analogy applies to the measurement of length in terms of different units e.g. cm, inches, etc. Shortly a set of raw scores obtained for a sample of subjects must be converted to a common unit. Analogy stops here. Unlike currencies or units for length there is no universal unit for educational and psychological constructs. Perhaps "Point" score is the most common unit used in the world. But there no two identical "points" assigned for a subject in an exam or in a questionnaire. The procedure to obtain standard scores for a set of raw scores expressed in "points" of different size is on the Sheet named Standard Scores.
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Analysis of buses' arrival time to their stations to see if the time board shows the right arrival time. And calculating the results by using z values and standart normal distrubition
In order to find whether a student is eligible for admission to higher academic institutions, it is often necessary to estimate the percentile score of the student, based on the marks obtained in a competitive examination. In the present article, we have discussed a very simple mathematical model to calculate the percentile score based on marks. For this purpose, we have defined a function representing the probability that a certain fraction of the syllabus has been studied by a candidate before appearing for the examination. Another function has been derived, in terms of that fraction, representing the probable percentage of marks obtained by the candidate. Using these functions, we have derived expressions for the expected percentile score and the rank of the candidate in terms of the percentage of marks. To determine the values of the constant parameters involved in the present model, one is supposed to use the marksversus-percentile-versus-rank data obtained from the results of previous years. Due to the unavailability of these data, we have used different combinations of values of the parameters to show graphically how the percentile score and rank of a candidate vary as functions of the percentage of marks obtained in the examination.
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