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相关概念视频

Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.0K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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Decision Making: P-value Method01:09

Decision Making: P-value Method

5.3K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
5.3K
Decision Making01:20

Decision Making

103
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
103
Bootstrapping01:24

Bootstrapping

597
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
597
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.5K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Creative Thinking01:25

Creative Thinking

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Creative thinking encompasses innovative and unconventional methods for addressing challenges, often leading to groundbreaking solutions. Instead of focusing solely on enhancing existing systems, such as increasing smartphone battery capacity, creative thinking might inspire advancements like energy-efficient batteries or processors that minimize power consumption. This multidimensional approach underscores the importance of exploring novel pathways to innovation.
Divergent thinking is the...
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Updated: Jun 19, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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融资创新和风险:一个灰色的初创公司投资决策

Manoj Kumar Srivastava1, Ashutosh Dash1, Imlak Shaikh1

  • 1Management Development Institute, Gurgaon, India.

Evaluation review
|July 25, 2024
PubMed
概括
此摘要是机器生成的。

风险投资者可以通过考虑关键属性来优化早期科技创业公司的投资. 根据一种新的灰色系统理论方法,农业技术成为首选投资选择,其次是电子商务和教育技术.

关键词:
灰色的系统理论理论.采用多个标准的决策.创业公司创业资金 创业公司创业资金科技初创公司风险投资公司的风险资本

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An R-Based Landscape Validation of a Competing Risk Model
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相关实验视频

Last Updated: Jun 19, 2025

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

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An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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科学领域:

  • 决策科学 决策科学 决策科学
  • 风险资本投资 风险资本投资
  • 技术管理 技术管理

背景情况:

  • 风险投资者 (VC) 面临决策挑战,原因是印度创业生态系统的信息过载.
  • 有限的理性和认知偏见影响风险投资决策.
  • 了解推动早期技术风险投资的因素至关重要.

研究的目的:

  • 确定影响科技初创公司早期投资决策的关键属性.
  • 通过灰色系统理论,为风险投资者提出一个最佳的决策方法.
  • 为了对风险资本投资的科技创业部门进行排名.

主要方法:

  • 文献审查以确定八个主要的投资属性.
  • 专家采访以确定八个关键技术领域.
  • 灰色系统理论,包括语言变量和灰色可能性度,用于排名初创公司.

主要成果:

  • 农业技术被确定为早期风投投资的排名第一的行业.
  • 电子商务和教育技术分别排名第二和第三.
  • 其他排名的行业包括电动汽车基础设施,保险技术,金融技术,太空技术和软件即服务.

结论:

  • 该研究为风险投资公司提供了一个数据驱动的框架,以导航早期投资决策.
  • 农业技术为风险资本融资提供了高潜力的行业.
  • 灰色系统理论方法提供了一种强大的方法,用于在动态市场中对技术创业公司进行排名.