distributions

简明释义

英[dɪsˈtrɪb.juː.ʃənz]美[dɪsˈtrɪb.jə.ʃənz]

n. 分派；分派；分销（distribution 的复数形式）

英英释义

单词用法

同义词

反义词

例句

1.Fortunately, most modern distributions have this functionality built in.

幸运的是，大多数最新发行版都内置了这项功能。

2.And here's where I'm going to create distributions.

我会在这儿创建一个分布。

3.Species distributions are also shifting.

物种分布是在不断变化的。

4.Most distributions come with media players of some sort.

大多数发行版都随带了某种类型的媒体播放器。

5.Most other distributions should have this package included as well.

大多数其他发行版应该也包含这个包。

6.Both distributions will be generally available in 2004.

在2004年这两个发行版本将广为可用。

7.STATLEVEL is the level or granularity of the column distributions.

STATLEVEL是一种列分布级别或粒度。

8.The company analyzed the sales distributions to identify trends in customer preferences.

公司分析了销售分布以识别客户偏好的趋势。

9.In statistics, normal distributions are often used to model real-world phenomena.

在统计学中，正态分布常用于建模现实世界现象。

10.The team created visual representations of data distributions to facilitate understanding.

团队创建了数据分布的可视化表示，以便于理解。

11.We need to evaluate the distributions of income levels across different regions.

我们需要评估不同地区收入水平的分布。

12.The professor explained how probability distributions can be applied in various fields.

教授解释了概率分布如何应用于各个领域。

作文

In the field of statistics, the concept of distributions (分布) plays a crucial role in understanding how data is spread across various values. A distribution (分布) can be defined as the way in which the values of a random variable are assigned probabilities. It provides a comprehensive picture of the likelihood of different outcomes in a given scenario. For instance, when analyzing test scores from a group of students, one might use a normal distribution (分布) to illustrate how most students scored around the average, with fewer students achieving extremely high or low scores. This visual representation helps educators identify trends and make informed decisions regarding curriculum adjustments. Moreover, distributions (分布) are not limited to academic scenarios; they are also widely used in business and economics. Companies often rely on distributions (分布) to forecast sales, manage inventory, and understand customer behavior. By analyzing past sales data, a business can identify patterns and predict future sales distributions (分布). For example, if a retail store notices that sales of winter clothing follow a specific distribution (分布) pattern, they can stock their inventory accordingly to meet anticipated demand. Another significant aspect of distributions (分布) is their application in risk assessment and management. Financial analysts utilize various statistical distributions (分布) to evaluate potential risks and returns associated with investments. The normal distribution (分布), for example, is commonly used to model stock prices and returns. Understanding the distribution (分布) of potential returns allows investors to make more informed choices and develop strategies to mitigate risks. Additionally, there are many types of distributions (分布) that serve different purposes. The binomial distribution (分布) is used in scenarios where there are two possible outcomes, such as success or failure. The Poisson distribution (分布) is useful for modeling the number of events occurring within a fixed interval of time or space. Each of these distributions (分布) has its own unique characteristics and applications, making it essential for researchers and professionals to choose the appropriate distribution (分布) based on their specific needs. In conclusion, the concept of distributions (分布) is fundamental to various fields, including statistics, business, and finance. Understanding how data is distributed allows individuals and organizations to make better predictions, assess risks, and optimize their strategies. As we continue to collect and analyze data in an increasingly complex world, the importance of mastering the concept of distributions (分布) cannot be overstated. Whether you are a student, a business professional, or a researcher, having a solid grasp of distributions (分布) will undoubtedly enhance your analytical skills and decision-making abilities.

在统计学领域，distributions（分布）的概念在理解数据如何在不同值之间分散方面起着至关重要的作用。distribution（分布）可以定义为随机变量的值被分配概率的方式。它提供了一个全面的图景，展示了在特定场景中不同结果的可能性。例如，在分析一组学生的测试成绩时，可能会使用正态distribution（分布）来说明大多数学生的成绩集中在平均值附近，而极少数学生则取得极高或极低的分数。这种可视化的表现帮助教育工作者识别趋势，并根据需要做出明智的课程调整。 此外，distributions（分布）不仅限于学术场景；它们在商业和经济学中也得到了广泛应用。公司通常依赖于distributions（分布）来预测销售、管理库存和理解客户行为。通过分析过去的销售数据，企业可以识别模式并预测未来的销售distributions（分布）。例如，如果一家零售店注意到冬季服装的销售遵循特定的distribution（分布）模式，他们可以相应地储备库存，以满足预期需求。 Distributions（分布）的另一个重要方面是它们在风险评估和管理中的应用。金融分析师利用各种统计distributions（分布）来评估与投资相关的潜在风险和回报。例如，正态distribution（分布）常用于建模股票价格和回报。理解潜在回报的distribution（分布）使投资者能够做出更明智的选择，并制定减轻风险的策略。 此外，还有许多类型的distributions（分布）用于不同的目的。二项distribution（分布）用于只有两种可能结果的场景，例如成功或失败。泊松distribution（分布）适用于建模在固定时间或空间内发生的事件数量。这些distributions（分布）各自具有独特的特征和应用，使研究人员和专业人士必须根据具体需求选择合适的distribution（分布）。 总之，distributions（分布）的概念在统计学、商业和金融等多个领域中是基础性的。理解数据如何分布使个人和组织能够更好地进行预测、评估风险和优化战略。随着我们在一个日益复杂的世界中继续收集和分析数据，掌握distributions（分布）这一概念的重要性不言而喻。无论你是学生、商业专业人士还是研究人员，扎实掌握distributions（分布）无疑会增强你的分析能力和决策能力。