mathematic statistics

简明释义

数理统计学

英英释义

例句

1.We used mathematic statistics 数学统计 to predict future trends based on historical data.

我们使用mathematic statistics 数学统计根据历史数据预测未来趋势。

2.In our research project, we applied mathematic statistics 数学统计 to analyze the data collected from surveys.

在我们的研究项目中，我们应用了mathematic statistics 数学统计来分析从调查中收集的数据。

3.The findings of our study were validated using mathematic statistics 数学统计 methods.

我们的研究结果通过mathematic statistics 数学统计方法得到了验证。

4.The course on mathematic statistics 数学统计 covers topics such as hypothesis testing and regression analysis.

关于mathematic statistics 数学统计的课程涵盖了假设检验和回归分析等主题。

5.Understanding mathematic statistics 数学统计 is essential for interpreting experimental results accurately.

理解mathematic statistics 数学统计对于准确解读实验结果至关重要。

作文

Mathematic statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. It plays a crucial role in various fields, including science, economics, and social research. Understanding mathematic statistics (数学统计学) is essential for making informed decisions based on data. In today's data-driven world, the ability to analyze and interpret statistical information is more important than ever.One of the key components of mathematic statistics (数学统计学) is the concept of probability. Probability theory provides the foundation for statistical inference, allowing us to make predictions and generalizations about a population based on a sample. For instance, if researchers want to understand the average height of adult males in a country, they can measure a sample group and use mathematic statistics (数学统计学) to estimate the average height of the entire population. This process involves calculating confidence intervals and conducting hypothesis tests to determine the reliability of the results.Another important aspect of mathematic statistics (数学统计学) is the different types of data distributions. Understanding whether the data follows a normal distribution, binomial distribution, or any other type is crucial for selecting the appropriate statistical methods for analysis. For example, many statistical tests assume that the data is normally distributed. If this assumption is violated, the results may be misleading. Therefore, statisticians must carefully examine the data before applying any statistical techniques.Descriptive statistics is another fundamental concept within mathematic statistics (数学统计学). Descriptive statistics summarize and describe the main features of a dataset. Measures such as mean, median, mode, variance, and standard deviation provide valuable insights into the data's central tendency and variability. By employing these measures, researchers can present their findings in a clear and concise manner, making it easier for others to understand the implications of the data.Moreover, inferential statistics, a key area of mathematic statistics (数学统计学), allows us to draw conclusions about a population based on sample data. This involves using various statistical tests, such as t-tests, chi-square tests, and ANOVA, to determine whether observed differences between groups are statistically significant. The ability to perform these tests is vital in fields like medicine, where clinical trials rely on mathematic statistics (数学统计学) to assess the effectiveness of new treatments.In conclusion, mathematic statistics (数学统计学) is an indispensable tool for analyzing data and making informed decisions. Its applications span across multiple disciplines, providing a framework for understanding complex information. As we continue to generate vast amounts of data in our daily lives, the importance of mastering mathematic statistics (数学统计学) cannot be overstated. With a solid grasp of statistical principles, individuals can better navigate the complexities of the modern world, enhancing their critical thinking and problem-solving skills. Ultimately, the study of mathematic statistics (数学统计学) empowers individuals to make data-driven decisions that can lead to positive outcomes in various aspects of life.

数学统计学是数学的一个分支，涉及数据的收集、分析、解释、呈现和组织。它在科学、经济学和社会研究等多个领域中发挥着至关重要的作用。理解数学统计学（mathematic statistics）对于基于数据做出明智决策至关重要。在当今数据驱动的世界中，分析和解释统计信息的能力比以往任何时候都更加重要。数学统计学（mathematic statistics）的一个关键组成部分是概率的概念。概率论为统计推断提供了基础，使我们能够根据样本对总体进行预测和概括。例如，如果研究人员想了解一个国家成年男性的平均身高，他们可以测量一组样本，并利用数学统计学（mathematic statistics）来估计整个群体的平均身高。这个过程涉及计算置信区间和进行假设检验，以确定结果的可靠性。数学统计学（mathematic statistics）的另一个重要方面是不同类型的数据分布。理解数据是否遵循正态分布、二项分布或其他任何类型对于选择适当的统计分析方法至关重要。例如，许多统计检验假设数据呈正态分布。如果这一假设被违反，结果可能会产生误导。因此，统计学家必须在应用任何统计技术之前仔细检查数据。描述性统计是数学统计学（mathematic statistics）中的另一个基本概念。描述性统计总结并描述数据集的主要特征。均值、中位数、众数、方差和标准差等度量提供了关于数据集中趋势和变异性的宝贵见解。通过使用这些度量，研究人员可以以清晰简洁的方式呈现他们的发现，使他人更容易理解数据的含义。此外，推断统计学是数学统计学（mathematic statistics）的一个关键领域，它使我们能够根据样本数据对总体得出结论。这涉及使用各种统计检验，如t检验、卡方检验和方差分析，来确定组之间观察到的差异是否具有统计显著性。执行这些检验的能力在医学等领域至关重要，因为临床试验依赖于数学统计学（mathematic statistics）来评估新治疗的有效性。总之，数学统计学（mathematic statistics）是分析数据和做出明智决策的不可或缺的工具。它的应用跨越多个学科，为理解复杂信息提供了框架。随着我们在日常生活中继续生成大量数据，掌握数学统计学（mathematic statistics）的重要性不言而喻。通过扎实掌握统计原理，个人可以更好地驾驭现代世界的复杂性，增强批判性思维和解决问题的能力。最终，学习数学统计学（mathematic statistics）赋予个人做出基于数据的决策的能力，从而在生活的各个方面带来积极的结果。