The statistics.fmean function in Python’s statistics module calculates the arithmetic mean (average) of a given set of numbers using floating-point arithmetic. This function is particularly useful for maintaining precision in calculations with floating-point numbers.
The statistics.quantiles function in Python’s statistics module calculates the quantiles for a given dataset. Quantiles are points taken at regular intervals from the cumulative distribution function (CDF) of a random variable, dividing the range into continuous intervals with equal probabilities.
The statistics.pvariance function in Python’s statistics module calculates the population variance of a given set of numbers. Population variance measures how much the values in a dataset differ from the mean of the population. It is useful for understanding the spread or dispersion of an entire population dataset.
The statistics.pstdev function in Python’s statistics module calculates the population standard deviation of a given set of numbers. Population standard deviation measures the spread of the data points in a population dataset relative to its mean.
The statistics.variance function in Python’s statistics module calculates the variance of a given set of numbers. Variance measures how far a set of numbers are spread out from their average value. It provides an insight into the degree of variation or dispersion within a dataset.
The statistics.stdev function in Python’s statistics module calculates the standard deviation of a given set of numbers. Standard deviation is a measure of the amount of variation or dispersion in a dataset. It provides insight into how spread out the values are around the mean.
The statistics.geometric_mean function in Python’s statistics module calculates the geometric mean of a given set of numbers. The geometric mean is the nth root of the product of the numbers, where n is the number of values. It is useful for finding the central tendency of multiplicative data.
The statistics.harmonic_mean function in Python’s statistics module calculates the harmonic mean of a given set of numbers. The harmonic mean is the reciprocal of the arithmetic mean of the reciprocals of the data. It is useful for calculating the average of rates or ratios.
The statistics.multimode function in Python’s statistics module returns a list of the most frequently occurring values in the dataset. This function is useful when dealing with multimodal datasets, where there can be more than one mode.
The statistics.mode function in Python’s statistics module is used to calculate the mode of a given set of numbers. The mode is the value that appears most frequently in the dataset. This function is useful for finding the most common value in a dataset.
The statistics.median_grouped function in Python’s statistics module calculates the median of grouped continuous data, using interpolation. This function is useful for finding the median in datasets where data is grouped into bins or intervals.
The statistics.median_high function in Python’s statistics module calculates the high median of a given set of numbers. The high median is the middle value in a sorted list of numbers. If the list has an even number of elements, the high median is the higher of the two middle values.
The statistics.median_low function in Python’s statistics module calculates the low median of a given set of numbers. The low median is the middle value in a sorted list of numbers. If the list has an even number of elements, the low median is the lower of the two middle values.
The statistics.median function in Python’s statistics module is used to calculate the median (the middle value) of a given set of numbers. This function is useful for finding the central value of a dataset, especially when the dataset contains outliers or skewed values.
The statistics.mean function in Python’s statistics module is used to calculate the arithmetic mean (average) of a given set of numbers. This function is useful for finding the central value of a dataset.