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Econometric Views (EViews)01:29

Econometric Views (EViews)

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Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
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What are Estimates?01:06

What are Estimates?

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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
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Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Related Experiment Video

Updated: Nov 5, 2025

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

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A strategy to improve expert technology forecasts.

Tamara Savage1, Alex Davis1, Baruch Fischhoff1

  • 1Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213.

Proceedings of the National Academy of Sciences of the United States of America
|May 15, 2021
PubMed
Summary
This summary is machine-generated.

Expert technology forecasts are often inaccurate and overconfident. A new hybrid approach combining technical experts with policy and adoption experts may improve forecast accuracy and reduce overconfidence.

Keywords:
debiasingexpert elicitationoverconfidencetechnology forecasting

Related Experiment Videos

Last Updated: Nov 5, 2025

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
20:24

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

16.8K

Area of Science:

  • Technology Forecasting
  • Expert Elicitation
  • Decision Analysis

Background:

  • Technology forecasts are crucial for decision-making but often lack accuracy.
  • Expert predictions frequently exhibit overconfidence, with outcomes outside predicted ranges.

Purpose of the Study:

  • To propose and evaluate a hybrid expert elicitation approach for improving technology forecasts.
  • To systematically incorporate broader societal, policy, and economic factors into forecasting.

Main Methods:

  • Iteratively combining judgments from technical experts and experts in technology adoption/public policy.
  • Conducting a pilot study to assess the impact of broader factor briefings on forecasters.

Main Results:

  • Forecasters receiving briefings on policy, economic, and social factors produced wider forecast intervals.
  • The hybrid approach aims to mitigate overconfidence and improve forecast reliability.

Conclusions:

  • A hybrid expert elicitation method shows promise for more robust technology forecasting.
  • Systematic consideration of non-technical factors is essential for accurate future predictions.