Write an example of calculating variance in Golang

In the Go language, calculating the variance of a set of data is a common mathematical calculation operation. Variance measures the dispersion of values ​​in a data set and is one of the important concepts in statistics. Below we will use an example to demonstrate how to use the Go language to implement variance calculation.

First of all, we need to clarify the definition of variance: given a set of samples {x1, x2, ..., xn} containing n data points, the calculation formula for its variance is as follows:

$$ Var(X) = rac{1}{n} sum_{i=1}^{n} (x_i - ar{x})^2 $$

Where, $ ar {x}$ is the mean of the sample, and the calculation formula is:

$$ ar{x} = rac{1}{n} sum_{i=1}^{n} x_i $$

Next, we will implement the calculation of variance through a sample code:

package main

import (
    "fmt"
    "math"
)

func calculateMean(data []float64) float64 {
    total := 0.0
    for _, value := range data {
        total += value
    }
    return total / float64(len(data))
}

func calculateVariance(data []float64) float64 {
    mean := calculateMean(data)
    variance := 0.0
    for _, value := range data {
        variance += math.Pow(value-mean, 2)
    }
    return variance / float64(len(data))
}

func main() {
    data := []float64{1.2, 3.4, 5.6, 7.8, 9.0}
    variance := calculateVariance(data)
    fmt.Printf("数据集合 %v 的方差为 %.2f
", data, variance)
}

In the above sample code, two functions calculateMean and calculateVariance are first defined . Among them, the calculateMean function is used to calculate the mean of the data set, and the calculateVariance function is used to calculate the variance of the data set.

In the main function, a slice containing data points data is defined, and then the calculateVariance function is called to calculate the variance of the data set, and output the result.

Through the above example code, we can easily use Go language to calculate the variance of a set of data. Variance calculation is a very important concept in statistics, which can help us understand the degree of dispersion of values ​​in a data set and thereby better analyze the data.

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