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Kurtosis what is it. The meaning of the concept

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Kurtosis what is it. The meaning of the concept
Kurtosis what is it. The meaning of the concept

Video: What is Kurtosis? (+ the "peakedness" controversy!) 2024, July

Video: What is Kurtosis? (+ the "peakedness" controversy!) 2024, July
Anonim

The article describes the role of statistics as a science. The concept of excess and its use in science is considered.

Statistics. Basic concept

Statistics is a basic derivative of mathematical science. This subject belongs to a number of social disciplines that are aimed at forming students' picture of worldview and competent analysis of events.

Statistics studies all kinds of processes and events in people's lives, highlights their patterns and presents everything in the form of short statistical reports. Such a science is socially useful and requires constant improvement. Kurtosis - what is it? This is a basic concept in graphical statistics that helps to determine the correctness of perfect estimates. Kurtosis should not have a strong deviation.

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Statistics can satisfy the need of people for reliable information about a particular phenomenon, event, and so on. Determining the dynamics of life factors, their decline, stagnation or growth - this is exactly what this science does.

In the modern world, statistics occupy one of the main places in the scientific arena. Let's look at the concept of "excess." What is statistical purpose and observation? Where do these concepts apply? Read about all this later in the article.

What is an excess in statistics?

Kurtosis is a statistical concept that represents the acuity of each peak in a distribution graph. There is a special formula for its exact calculation.

Mathematical expectation says that for a more even distribution of statistical data, the excess must be equal to a positive number. Literally, this concept means a certain deviation from the norm and further abnormal development or functioning of the statistical system.

An excess expression of excess on the statistical graph may indicate an incorrect study or errors in the initial data of the graph. Such a concept is scalar in nature, which means that with a final miscalculation, a number should be obtained that will not contain variables or auxiliary functions. That is what the excess looks like.