构建统计分析工具包：PHP中的均值，中位和标准偏差

计算平均值：使用 array_sum() 除以元素个数得到均值；2. 计算中位数：排序后取中间值，偶数个元素时取中间两个数的平均值；3. 计算标准差：先求均值，再计算每个值与均值差的平方的平均数（样本用 n-1），最后取平方根；通过封装这三个函数可构建基础统计工具类，适用于中小规模数据的分析，且需注意处理空数组和非数值输入，最终实现无需依赖外部库即可获得数据的核心统计特征。

When working with data in PHP—whether it’s user analytics, survey results, or financial figures—you’ll often need to summarize and understand the distribution of your numbers. While PHP doesn’t include built-in functions for all statistical measures, creating a simple toolkit for mean, median, and standard deviation is straightforward and highly useful.

Here’s how to implement these three core statistical functions in PHP.

1. Calculating the Mean (Average)

The mean is the sum of all values divided by the number of values. It’s the most common measure of central tendency.

function calculateMean($data) {
    if (empty($data)) {
        return 0; // or throw an exception
    }
    return array_sum($data) / count($data);
}

Example:

$scores = [85, 90, 78, 92, 88];
echo calculateMean($scores); // Output: 86.6

Note: This function assumes the input is a numeric array. You may want to add type checking or filtering with is_numeric() if dealing with unclean data.

2. Finding the Median

The median is the middle value when the data is sorted. If there’s an even number of observations, it's the average of the two middle values.

function calculateMedian($data) {
    if (empty($data)) {
        return 0;
    }

    sort($data);
    $count = count($data);
    $middle = (int)($count / 2);

    if ($count % 2) {
        // Odd count: return middle value
        return $data[$middle];
    } else {
        // Even count: return average of two middle values
        return ($data[$middle - 1]   $data[$middle]) / 2;
    }
}

Example:

$values = [3, 1, 4, 1, 5, 9, 2];
echo calculateMedian($values); // Output: 3

Why it matters: The median is less affected by outliers than the mean, making it better for skewed data.

3. Computing Standard Deviation

The standard deviation measures how spread out the numbers are from the mean. A low SD means values are close to the mean; high SD means they’re spread out.

We’ll implement sample standard deviation (using n-1 in the denominator), which is commonly used when analyzing a subset of data.

function calculateStandardDeviation($data) {
    $count = count($data);
    if ($count < 2) {
        return 0; // SD not meaningful with less than 2 values
    }

    $mean = calculateMean($data);
    $squaredDifferences = array_map(function($value) use ($mean) {
        return ($value - $mean) ** 2;
    }, $data);

    $variance = array_sum($squaredDifferences) / ($count - 1); // Sample variance
    return sqrt($variance);
}

Example:

$dataset = [2, 4, 4, 4, 5, 5, 7, 9];
echo calculateStandardDeviation($dataset); // Output: ~2.14

Tip: Use population standard deviation (/ $count instead of / ($count - 1)) if you're working with complete data (the entire population).

Putting It All Together: A Simple Toolkit

You can group these into a helper class or utility file:

class StatisticsToolkit {
    public static function mean($data) {
        return empty($data) ? 0 : array_sum($data) / count($data);
    }

    public static function median($data) {
        if (empty($data)) return 0;
        sort($data);
        $count = count($data);
        $mid = (int)($count / 2);
        return ($count % 2) ? $data[$mid] : ($data[$mid - 1]   $data[$mid]) / 2;
    }

    public static function stdev($data) {
        $n = count($data);
        if ($n < 2) return 0;
        $mean = self::mean($data);
        $variance = array_sum(array_map(fn($x) => ($x - $mean) ** 2, $data)) / ($n - 1);
        return sqrt($variance);
    }
}

Usage:

$data = [10, 12, 23, 23, 16, 23, 21, 16];

echo "Mean: " . StatisticsToolkit::mean($data) . "\n";
echo "Median: " . StatisticsToolkit::median($data) . "\n";
echo "Standard Deviation: " . round(StatisticsToolkit::stdev($data), 2) . "\n";

Output:

Mean: 18
Median: 18.5
Standard Deviation: 5.26

Final Notes

• These functions work well for small to medium datasets.
• For large-scale or complex statistical analysis, consider using external tools (like Python with Pandas) and integrating via APIs.
• Always validate and clean input data—ensure it's numeric and handle edge cases like empty arrays.

Building your own statistical toolkit in PHP gives you flexibility and insight without relying on external libraries for basic tasks.

Basically, with just a few functions, you can go from raw numbers to meaningful summaries.

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