4 Quantitative Scales that you haven’t heard of
When talking about the world of statistics, one cannot talk without mentioning types of quantitative scales. Such a title is quite deserved. It is very important for all kinds of professions – teachers, students, researchers, corporate managers, media outlets, etc. – to have some knowledge about such scales in order to make their work easier and to come up with better output and value-added data sets.
Different scales can be used. One common type is the ordinal scale, which is widely used in almost all fields of study. Ordinal scales are graphical works that transform a domain of numerical values into a range of more definite graphical values (i.e., numbers, colors, measurements). For example, the distance from the sun to the earth or the weight of an object can be thought of as an ordinal dimension. Another common type of quantitative scale is the ratio scale, used to represent time (and thus, height and temperature) and distance.
One more important type of quantitative scale is the probability scale, which compares the value of a property over time. This can be thought of as a range value or probability density over time. Another common type is the spatial probability scale, which is used to represent spatial data domains (such as locations). The third popularly used type is the binomial curve, which calculates the probability distribution of a particular data range. This is based on logistic regression.
Some scales are binomial (x-intercept) scales (log-normal deviation), log-normal (x-intercept), or quadratic (log-normal plus tails) based. Other scales are simply point-form distributions. One common example is the binomial logistic or Gaussian random variable. A log-normal exponential curve has the same range as the natural log. The probability of sampling even a single point from a Gaussian curve is very low.
There are also other types of scales that can be considered useful for scientific analysis. One is the arithmetic mean, which is the asymmetrical mean of the underlying distribution. Another is the binomial curve, which involves a mean and a mode. Another type is the mathematical cephalic mean, which is formed by dividing the range of values by the square root of their frequencies. And, a few more include the logistic scale, the exponential distribution, and the logistic function scale.
The most visual analysis uses some kind of graphical scale. It may use a range, a data set, or just one or more of the many graphical scale types. The most common visual scale is the plotted line or spike chart, but more complex charts that make use of color for presentation also appear to be quite popular in the quantitative field. Other types of charts that are often used in science, engineering, and statistics studies include the histogram, the scatter plot, and the panel chart.
One thing that must be understood about the use of quantitative data in research is that different types of quantitative scales cannot be used at the same time, if at all. This is because data interpretation and the reporting of that information require a wide variety of methods, which depend on the type of scale being used. Some quantitative scales cannot be analyzed using more than one type.
For example, the Student’s t-test, also known as the Wertheimer Test, can be performed using either a one-element array (a two-element array consisting of two numbers) or a two-element ensemble (a two-element array of numbers and their corresponding interpretive symbol). One-element arrays can be analyzed using a normal bell curve or by a logistic function.
Two-element arrays, however, cannot be analyzed by a normal bell curve or by a logistic function. If the data set involves a significant number of numerical elements, then it is usually preferable to analyze the data using a logistic function or a two-element ensemble rather than the Student’s t-test. In cases where data analysis requires the employment of a two-dimensional array, then the common type of analytical method employed is the parabola.
This was a small post to share few scales that are not very common. To check the scales that are commonly used, you may watch the following video.
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