Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Confidence Intervals01:21

Confidence Intervals

6.2K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
6.2K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.3K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.3K
Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
Correlation and Regression00:53

Correlation and Regression

1.2K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
1.2K
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

6.0K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
6.0K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Characterizing the vertical structure of forests in the Brazilian Amazon.

Communications earth & environment·2026
Same author

Tiger Habitat Occupancy in Chitwan-Parsa Complex: Implications for Human-Tiger Conflict Management Strategies.

Ecology and evolution·2025
Same author

An invasive prey alters local and landscape contributions of sources and sinks for an endangered predator.

Ecology·2025
Same author

Sex Drives Intraspecific Scaling of Home Range Size in Mammals.

Ecology letters·2025
Same author

A lack of open data standards for large infrastructure projects hampers social-ecological research in the Brazilian Amazon.

PeerJ·2025
Same author

Aboveground biomass density maps for post-hurricane Ian forest monitoring in Florida.

Scientific data·2025

相关实验视频

Updated: Jul 3, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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

Published on: July 3, 2020

3.4K

在线性回归模型中使用比例共变量时的估计和解释问题和解决方案.

Denis Valle1, Jeffrey Mintz2, Ismael Verrastro Brack1

  • 1School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, Florida, USA.

Ecology
|February 16, 2024
PubMed
概括

使用比例变量 (组成数据) 的生态研究可以导致诸如多对线性等统计问题. 这项研究为配合和解释带有比例共变量的线性模型提供了解决方案,改进了生态数据分析.

关键词:
构成中的共变量.有条件的地图.推理推论是指一个推理.线性模型是一个线性模型.边缘地图是一个边缘地图.多对线性多对线性.可以识别参数的识别性.参数解释参数的解释.

更多相关视频

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

3.9K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K

相关实验视频

Last Updated: Jul 3, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

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

Published on: July 3, 2020

3.4K
Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

3.9K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K

科学领域:

  • 生态生态学 生态生态学
  • 统计建模 统计建模
  • 数据分析 数据分析

背景情况:

  • 在生态研究中,比例变量或组成数据很普遍.
  • 许多科学家不知道在统计分析中使用比例变量作为共变量的负面影响.
  • 这些问题包括多对线性和参数识别问题.

研究的目的:

  • 描述比例共变量如何导致多对线性和参数识别问题.
  • 用模拟和实证生态数据来证明这些问题.
  • 为配合和解释具有比例共变量的线性模型提出实际解决方案.

主要方法:

  • 利用模拟的鸟类物种丰富度数据作为土地利用的函数.
  • 在R.应用频率主义和贝叶斯模型框架.
  • 在线性模型中检查了多对线性和参数识别问题.
  • 建议的解决方案包括放弃协变量/截取值,并专注于斜率差异.
  • 建议的可视化技术,如条件和边缘图.

主要成果:

  • 证明比例共变量导致统计模型中的多对线性和参数识别问题.
  • 表明类似的模型可以产生实质上不同的参数估计和结论.
  • 确定放弃协变量或拦截可以解决这些特定问题.
  • 证实,即使在解决可识别性问题之后,解释挑战仍然存在.

结论:

  • 比例共变量在生态学中的统计建模中带来了重大挑战.
  • 已有解决方案可以缓解多对线性和可识别性问题,增强模型的合适性.
  • 专注于斜率差异和采用特定的可视化技术有助于解释涉及比例数据的结果.