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相关概念视频

Scanning Electron Microscopy01:07

Scanning Electron Microscopy

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A scanning electron microscope (SEM) is used to study the surface features of a sample by using an electron beam that scans the sample surface in a two-dimensional manner. Typically, areas between ~1 centimeter to 5 micrometers in width can be imaged. SEM can be used to image bacteria, viruses, tissues as well as larger samples like insects. Conventional SEM gives a magnification ranging from 20X to 30,000X and spatial resolution of 50 to 100 nanometers.
Fundamental Principles
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Introduction to R01:11

Introduction to R

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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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Response Surface Methodology01:16

Response Surface Methodology

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Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
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Preparation of Samples for Electron Microscopy01:20

Preparation of Samples for Electron Microscopy

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To be visualized by an electron microscope, either transmission or scanning, biological samples need to be fixed (stabilized) so the electron beam does not destroy them and dried thoroughly (desiccated/dehydrated) so the vacuum does not affect them. Fixation needs to be done as quickly as possible because the sample properties will start changing as soon as it is removed from its natural environment. For example, in a tissue sample, the oxygen levels begin decreasing, causing an altered...
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Interpreting R Charts01:22

Interpreting R Charts

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R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
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Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
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相关实验视频

Updated: Sep 15, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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使用R理解SEM的教程:所有数字来自哪里?

Yves Rosseel1, Marc Vidal2

  • 1Department of Data Analysis, Ghent University, Ghent, Belgium.

The British journal of mathematical and statistical psychology
|July 14, 2025
PubMed
概括
此摘要是机器生成的。

本教程通过展示如何使用R手动计算参数估计和适合措施等关键结果来消除结构方程建模 (SEM) 的神秘性. 它使研究人员能够访问复杂的SEM计算.

关键词:
在 R 代码的 R 代码中.确认的因素分析.结构方程建模 结构方程建模这是一个自学教程.

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科学领域:

  • 统计 统计 统计 统计
  • 量化心理学 量化心理学
  • 数据科学数据科学数据科学

背景情况:

  • 结构方程建模 (SEM) 被认为是复杂的.
  • 了解SEM在软件输出中的基本计算是具有挑战性的.
  • 在R中存在开源的SEM工具,但它们的源代码可能是压倒性的.

研究的目的:

  • 为提供标准SEM分析背后的基本计算提供了可访问的介绍.
  • 为了澄清如何计算SEM软件输出数字.
  • 为了提高读者对SEM内部工作的理解.

主要方法:

  • 使用简单的R脚本手动复制关键的SEM结果.
  • 使用两个众所周知的示例数据集进行演示.
  • 关注清晰度和概念理解,而不是计算效率.

主要成果:

  • 证明了对参数估计的手动计算.
  • 展示了标准错误的复制.
  • 描绘了SEM适合度的计算方法.

结论:

  • 读者可以通过手动计算更好地掌握"引擎盖下"的SEM.
  • 本教程有助于在独立研究中应用SEM计算概念.
  • 解密了SEM计算,使研究人员更容易接近该方法.