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Related Concept Videos

Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Hybrid Zones02:29

Hybrid Zones

Hybrid zones are narrow regions where two closely related species interact, mate, and produce hybrids. Relative to either parent species, hybrids may possess distinct phenotypic or genetic differences that impact their survival and reproductive success. The genetic variances introduced by hybridization influence species diversity and speciation processes within the hybrid zone.Gene flow and natural selection are evolutionary mechanisms that shape the outcome of a hybrid zone. Gene flow...
Decision Making: P-value Method01:09

Decision Making: P-value Method

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 have a...

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Related Experiment Videos

Using hybrid method to evaluate the green performance in uncertainty.

Ming-Lang Tseng1, Lawrence W Lan, Ray Wang

  • 1Department of Business Innovation & Development, College of Management, MingDao University, Changhua, Taiwan. ml.tseng@msa.hinet.net

Environmental Monitoring and Assessment
|June 24, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for evaluating green performance, combining multiple techniques to handle complex data and improve business sustainability. It helps companies assess environmental criteria more effectively for competitive advantage.

Related Experiment Videos

Area of Science:

  • Business Administration
  • Environmental Management
  • Operations Research

Background:

  • Evaluating green performance is crucial for sustainable competitive advantage.
  • Complexity and linguistic vagueness in data challenge green performance assessment.
  • Existing methods struggle with interdependencies among green performance aspects and criteria.

Purpose of the Study:

  • To propose a novel approach for evaluating the dependence aspects and criteria of firm's green performance.
  • To integrate fuzzy set theory, Analytical Network Process (ANP), and Importance-Performance Analysis (IPA) for a comprehensive green performance evaluation.
  • To address the challenges of data vagueness and complex interdependencies in green performance measurement.

Main Methods:

  • The study proposes the 'green network balanced scorecard' approach.
  • It combines Balanced Scorecard with fuzzy set theory, ANP, and IPA.
  • Fuzzy set theory handles linguistic vagueness, ANP models interdependencies, and IPA assesses performance and importance.

Main Results:

  • The green network balanced scorecard effectively evaluates dependence aspects and criteria of firm's green performance.
  • The approach successfully integrates qualitative and quantitative data, accounting for linguistic vagueness.
  • An empirical case study on Taiwan's PCB firms demonstrated the practical application and validity of the proposed method.

Conclusions:

  • The proposed green network balanced scorecard offers a robust framework for evaluating green performance.
  • This method enhances managerial decision-making for continuous improvement and sustainable competitive advantage.
  • The study provides valuable insights for businesses, particularly in manufacturing sectors, to optimize their environmental strategies.