statistical distribution

简明释义

统计分布，统计分配

英英释义

例句

1.To predict customer behavior, marketers study the statistical distribution 统计分布 of purchase frequencies.

为了预测客户行为，市场营销人员研究购买频率的统计分布 统计分布。

2.In data analysis, understanding the statistical distribution 统计分布 of your dataset is crucial for making informed decisions.

在数据分析中，理解你的数据集的统计分布 统计分布 对于做出明智的决策至关重要。

3.A uniform statistical distribution 统计分布 indicates that all outcomes are equally likely.

均匀统计分布 统计分布 表示所有结果的可能性相等。

4.The normal statistical distribution 统计分布 is often used in psychology to represent test scores.

正态统计分布 统计分布 常用于心理学中表示测试分数。

5.When modeling financial risks, analysts frequently rely on the statistical distribution 统计分布 of asset returns.

在建模金融风险时，分析师通常依赖于资产收益的统计分布 统计分布。

作文

Understanding the concept of statistical distribution is crucial for anyone who wishes to analyze data effectively. In statistics, a statistical distribution describes how values of a random variable are spread or distributed across different outcomes. It provides invaluable insights into the patterns and tendencies that may exist within a dataset. For example, the normal distribution, often referred to as the bell curve, is a common statistical distribution that indicates that most observations cluster around the mean, with fewer observations appearing as we move away from the mean in either direction.When we talk about statistical distribution, we are essentially discussing how likely different outcomes are to occur. This concept is pivotal in various fields, including economics, psychology, and natural sciences. By understanding the statistical distribution of a dataset, researchers can make predictions and informed decisions based on empirical evidence rather than mere speculation.To illustrate the importance of statistical distribution, consider a practical example involving test scores in a classroom. If the scores of a mathematics test are plotted, one might observe a statistical distribution that reveals how many students scored in particular ranges. If the scores form a normal distribution, educators can infer that most students performed at an average level, while only a few scored very high or very low. Such insights can guide teachers in tailoring their instructional methods to better meet the needs of their students.Moreover, understanding statistical distribution is essential for hypothesis testing. Researchers often formulate hypotheses about populations and then use sample data to test these hypotheses. The statistical distribution of the sample means, for instance, follows a normal distribution due to the Central Limit Theorem, which states that the means of sufficiently large samples will be normally distributed regardless of the original population's distribution. This property allows researchers to apply various statistical tests and determine the significance of their findings.In addition to the normal distribution, there are several other types of statistical distributions worth mentioning. The binomial distribution, for example, models the number of successes in a fixed number of trials, each with the same probability of success. This type of distribution is particularly useful in scenarios where there are two possible outcomes, such as flipping a coin. Another important distribution is the Poisson distribution, which is used to model the number of events occurring within a fixed interval of time or space, given a constant mean rate of occurrence.In conclusion, the concept of statistical distribution plays a fundamental role in data analysis and interpretation. It helps us understand the likelihood of various outcomes and the underlying patterns in our data. Whether in academia, business, or any field that relies on data-driven decision-making, a firm grasp of statistical distribution enables professionals to derive meaningful conclusions and make informed choices. As we continue to generate vast amounts of data in our increasingly digitized world, mastering the principles of statistical distribution will be more important than ever for effective analysis and interpretation of information.

理解统计分布的概念对于任何希望有效分析数据的人来说都是至关重要的。在统计学中，统计分布描述了随机变量的值如何在不同结果之间传播或分布。它为数据集中可能存在的模式和趋势提供了宝贵的见解。例如，正态分布通常被称为钟形曲线，是一种常见的统计分布，它表明大多数观察值聚集在均值附近，随着我们向均值两侧移动，观察值逐渐减少。当我们谈论统计分布时，我们实际上是在讨论不同结果发生的可能性。这个概念在经济学、心理学和自然科学等多个领域中都是至关重要的。通过理解数据集的统计分布，研究人员可以根据经验证据而不是单纯的推测进行预测和做出明智的决策。为了说明统计分布的重要性，考虑一个涉及教室测试分数的实际例子。如果将数学测试的分数绘制出来，可能会观察到一种统计分布，揭示有多少学生在特定范围内得分。如果分数形成正态分布，教育工作者可以推断出大多数学生的表现处于平均水平，而只有少数人得分非常高或非常低。这种见解可以指导教师调整他们的教学方法，以更好地满足学生的需求。此外，理解统计分布对于假设检验至关重要。研究人员通常会对人群提出假设，然后使用样本数据来检验这些假设。例如，样本均值的统计分布由于中心极限定理的存在而遵循正态分布，该定理指出，只要样本足够大，样本均值将呈正态分布，而不管原始人群的分布如何。这一特性使得研究人员能够应用各种统计检验，并确定其发现的显著性。除了正态分布外，还有几种其他类型的统计分布值得一提。例如，二项分布建模固定次数试验中的成功次数，每次试验都有相同的成功概率。这种类型的分布在只有两种可能结果的情况下特别有用，例如抛硬币。另一个重要的分布是泊松分布，它用于建模在固定时间或空间间隔内发生事件的数量，前提是发生的平均速率保持不变。总之，统计分布的概念在数据分析和解释中发挥着基础性作用。它帮助我们理解各种结果的可能性以及数据中潜在的模式。无论是在学术界、商业还是任何依赖数据驱动决策的领域，牢固掌握统计分布的原则使专业人士能够得出有意义的结论并做出明智的选择。随着我们在日益数字化的世界中继续生成大量数据，掌握统计分布的原则将比以往任何时候都更加重要，以便有效分析和解释信息。