Deciles divide a dataset into ten equal parts, each representing 10% of the data. For example, the first decile represents the point below which 10% of the data falls, the second decile represents the point below which 20% of the data falls, and so on. Let’s see how to calculate deciles in Python using the statistics.quantiles function with n=10, which returns the cut points that split your data into ten equal parts.
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Let’s see how to calculate quartiles in Python using the statistics.quantiles function for Q1, Q2, and Q3, and NumPy to compute the interquartile range (IQR).
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Mode represents the single most frequent value in a list, while multimode provides a list of all the values that occur with the highest frequency. In statistical analysis, the mode is a measure of central tendency that identifies the most frequently occurring value in a dataset. However, datasets can sometimes exhibit multiple modes, where two or more values share the highest frequency. In such cases, the multimode becomes a valuable descriptive statistic, providing a more complete picture of the data’s distribution by highlighting all values that achieve this maximum frequency. This tutorial shows how to calculate multimode in Python using…
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Let’s see how to calculate mode in Python.
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This guide shows how to calculate geometric mean in Python using the statistics module, NumPy logarithmic method, or SciPy’s gmean function for data analysis tasks. The geometric mean is a measure of central tendency that is useful for dealing with data that has a wide range of values and is often used in finance, biology, and other fields.
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The harmonic mean is a type of average useful for rates and ratios, and this guide shows how to calculate harmonic mean in Python using the built-in statistics module, SciPy, or manual formulas. Let’s see how to calculate harmonic mean in Python.