正態(tài)分布（Normal Distribution）是統(tǒng)計(jì)學(xué)中一種常見的概率分布，也被稱為高斯分布（Gaussian Distribution）。它在自然界和社會科學(xué)中的應(yīng)用非常廣泛，例如在物理學(xué)、經(jīng)濟(jì)學(xué)、心理學(xué)等領(lǐng)域都有重要的應(yīng)用。

正態(tài)分布的操作步驟如下：

1. 確定問題：首先要明確你需要使用正態(tài)分布解決的具體問題是什么。例如，你可能需要計(jì)算某個(gè)變量的概率密度函數(shù)、累積分布函數(shù)，或者進(jìn)行假設(shè)檢驗(yàn)等。

2. 確定參數(shù)：正態(tài)分布有兩個(gè)參數(shù)，即均值（μ）和標(biāo)準(zhǔn)差（σ）。在開始操作之前，需要明確這兩個(gè)參數(shù)的具體數(shù)值。

3. 計(jì)算概率密度函數(shù)：如果你需要計(jì)算某個(gè)特定數(shù)值的概率密度，可以使用正態(tài)分布的概率密度函數(shù)公式。該公式為：

![概率密度函數(shù)公式](https://wikimedia.org/api/rest_v1/media/math/render/svg/3c8c0a4b2c61a6e4a5e6d2d9e1d7e9f2e9c8e5e2e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1e9e1

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