What Is the Zygomatic Bone?

The z-score, also known as the standard score, is the process of dividing the difference between a number and the average by the standard deviation. In statistics, the standard score is the number of symbols in which the value of an observation or data point is higher than the standard deviation of the average of the observed or measured values.

The z-score can truly reflect the relative standard distance of a fractional distance average. If we convert each score to a z-score, then each z-score will start with

The Z score is also called standard score. It uses the standard deviation as a rule to measure the distance that an original score deviates from the average. This distance contains several standard deviations. In order to determine the position of this data in the overall data. This process is called standardization. The conversion formula is:

The application of Z-scores are: indicating the relative position of each raw data in the data set; for normal data, it can indicate the ratio of the data below or above the data, and specifically can solve problems such as the score line problem or the number of people ratio; Represents the score of the standardized test; used for the selection of outliers. Standard scores often have the following five applications in the evaluation of student education: vertical and horizontal comparison, grading of grades, standard conversion, determination of grade ratios, and quantitative assessment of quality.

In mathematical statistics, the normalization of an arbitrary variable X is to subtract its expected value E (X) and divide it by its standard deviation.

,which is:

If the above arbitrary variable X is an arbitrary sampling result

average of

,which is:

Then, the standardized format becomes: ^[2]

Because the Z score has positive and negative numbers, there are decimals. This makes the Z scores somewhat incomprehensible when calculating and interpreting experimental and test results. Therefore, it is often necessary to perform a linear transformation on the Z scores, that is, to convert Z to T scores. T scores have the distribution status of z scores, which is the standard normal distribution It is easy to understand and explain. Use linear formula conversion:

Y = m + k (z)

In the formula: Y: the converted score; m, k constants, m is the average of the new score after conversion, and k is the standard deviation of the new score after conversion.

The T-score is the converted score with an average of 50 and a standard deviation of 10.

T = 50 + 10 (z)

T-scores are essentially the same as Z-scores, keeping the units equally spaced, and attribute the data distribution to the standard normal distribution.

In education evaluation, the T-score is a standard score Z, with an average of 50 and a standard deviation of 10.

In bone density measurement, the T-score is the standard score for a measurement compared to a population of healthy 30-year-old adults. ^[3]