Get to know Kurtosis better with 10+ real example sentences, the meaning.
Kurtosis in a sentence
Kurtosis meaning
- A measure of "heaviness of the tails" of a probability distribution, defined as the fourth cumulant divided by the square of the variance of the probability distribution.
- Excess kurtosis: the difference between a given distribution's kurtosis and the kurtosis of a normal distribution.
Using Kurtosis
- The main meaning on this page is: A measure of "heaviness of the tails" of a probability distribution, defined as the fourth cumulant divided by the square of the variance of the probability distribution. | Excess kurtosis: the difference between a given distribution's kurtosis and the kurtosis of a normal distribution.
- In the example corpus, kurtosis often appears in combinations such as: excess kurtosis, the kurtosis, of kurtosis.
Context around Kurtosis
- Average sentence length in these examples: 21.9 words
- Position in the sentence: 5 start, 8 middle, 5 end
- Sentence types: 18 statements, 0 questions, 0 exclamations
Corpus analysis for Kurtosis
- In this selection, "kurtosis" usually appears in the middle of the sentence. The average example has 21.9 words, and this corpus slice is mostly made up of statements.
- Around the word, excess, sample, use, minus, used and imaging stand out and add context to how "kurtosis" is used.
- Recognizable usage signals include and sample kurtosis and and sample kurtosis as is. That gives this page its own corpus information beyond isolated example sentences.
- By corpus frequency, "kurtosis" sits close to words such as abad, abovementioned and abr, which helps place it inside the broader word index.
Example types with kurtosis
The same corpus examples are grouped by length and sentence type, making it easier to see the contexts in which the word appears:
Some authors use "kurtosis" by itself to refer to the excess kurtosis. (12 words)
Many incorrect interpretations of kurtosis that involve notions of peakedness have been given. (13 words)
The technique, known as diffusion kurtosis imaging, provides a picture of breast tissue on a microstructural level. (17 words)
Multivariate normality tests include the Cox-Small test citation and Smith and Jain's adaptation citation of the Friedman-Rafsky test. citation Mardia's test is based on multivariate extensions of skewness and kurtosis measures. (35 words)
Alternatively, given two subpopulations with the same mean and different standard deviations, the overall population will exhibit high kurtosis, with a sharper peak and heavier tails (and correspondingly shallower shoulders) than a single distribution. (34 words)
D'Agostino's K-squared test is a goodness-of-fit normality test based on a combination of the sample skewness and sample kurtosis, as is the Jarque–Bera test for normality. (32 words)
Example sentences (18)
Excess kurtosis The excess kurtosis is defined as kurtosis minus 3. There are 3 distinct regimes as described below.
Interpretation The exact interpretation of the Pearson measure of kurtosis (or excess kurtosis) used to be disputed, but is now settled.
Some authors use "kurtosis" by itself to refer to the excess kurtosis.
The kurtosis of any univariate normal distribution is 3. It is common to compare the kurtosis of a distribution to this value.
The technique, known as diffusion kurtosis imaging, provides a picture of breast tissue on a microstructural level.
Alternatively, given two subpopulations with the same mean and different standard deviations, the overall population will exhibit high kurtosis, with a sharper peak and heavier tails (and correspondingly shallower shoulders) than a single distribution.
D'Agostino's K-squared test is a goodness-of-fit normality test based on a combination of the sample skewness and sample kurtosis, as is the Jarque–Bera test for normality.
D'Agostino's K-squared test is a goodness-of-fit normality test based on sample skewness and sample kurtosis.
Larger kurtosis indicates a more serious outlier problem, and may lead the researcher to choose alternative statistical methods.
Many incorrect interpretations of kurtosis that involve notions of peakedness have been given.
Multivariate normality tests include the Cox-Small test citation and Smith and Jain's adaptation citation of the Friedman-Rafsky test. citation Mardia's test is based on multivariate extensions of skewness and kurtosis measures.
The examples of such extensions are: * Pearson distribution — a four-parametric family of probability distributions that extend the normal law to include different skewness and kurtosis values.
The kurtosis can now be seen to be a measure of the dispersion of Z 2 around its expectation.
The logic is simple: Kurtosis is the average (or expected value ) of the standardized data raised to the fourth power.
Then the only free parameter is m, which controls the fourth moment (and cumulant) and hence the kurtosis.
The only data values (observed or observable) that contribute to kurtosis in any meaningful way are those outside the region of the peak; i.e., the outliers.
This is analogous to the definition of kurtosis as the fourth cumulant normalized by the square of the second cumulant.
This yields a one-parameter leptokurtic family with zero mean, unit variance, zero skewness, and arbitrary non-negative excess kurtosis.
Common combinations with kurtosis
These word pairs occur most frequently in English texts:
- excess kurtosis 5×
- the kurtosis 4×
- of kurtosis 3×
- kurtosis is 2×
- kurtosis of 2×
- sample kurtosis 2×
- kurtosis as 2×
- and kurtosis 2×