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相关概念视频

Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Statistical Analysis: Overview01:11

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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
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Bias01:22

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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
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Introduction to R01:11

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R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
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Data: Types and Distribution01:19

Data: Types and Distribution

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In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
Distributions in...
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Statgraphics01:10

Statgraphics

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Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
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让数学研究数据公平:改善数据共享的途径

Tim O F Conrad1, Eloi Ferrer2, Daniel Mietchen3,4

  • 1Zuse Institute Berlin, Berlin, Germany. conrad@zib.de.

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此摘要是机器生成的。

分享研究数据加速了科学发现和知识的产生. 本研究分析了基于Web的系统,用于共享数学研究数据,突出了当前的趋势和需求.

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科学领域:

  • 科学研究科学研究
  • 数据共享数据的共享.
  • 知识的产生知识的产生.

背景情况:

  • 研究数据共享对于可重复性和加速科学进步至关重要.
  • 虽然天文学等一些领域已经建立了数据共享实践,但其他领域的发展程度较低.
  • 目前存在着各种各样的基于Web的数据共享系统.

研究的目的:

  • 分析现有的基于网络的研究数据共享系统.
  • 专注于与数学研究数据相关的系统.
  • 了解当前的情况,并确定数学数据共享的需求.

主要方法:

  • 详细分析基于Web的系统.
  • 专注于适用于数学研究数据的系统.
  • 审查现有的数据共享平台.

主要成果:

  • 研究数据共享系统的景观是多样化的.
  • 数学研究数据共享系统各不相同.
  • 分析揭示了现有平台的特定特征.

结论:

  • 了解系统的多样性是有效的数据共享的关键.
  • 数学研究数据共享平台可能需要进一步开发.
  • 促进数据共享实践对于科学进步至关重要.