Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Stereotypes, Prejudice, and Discrimination02:55

Stereotypes, Prejudice, and Discrimination

Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who are...
Unusual Results01:16

Unusual Results

Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ  from the mean, μ  is considered unusual.
Maximum unusual value = μ + 2σ
Minimum unusual value...
Outliers and Influential Points01:08

Outliers and Influential Points

An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the vertical...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Applications of Life Tables01:22

Applications of Life Tables

Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same journal

Marvin Goodfriend: economist and central banker.

Business economics (Cleveland, Ohio)·2023
Same journal

Estimating the impact of NABE member characteristics on compensation.

Business economics (Cleveland, Ohio)·2023
Same journal

Disruption in the meat industry: new technologies in nonmeat substitutes.

Business economics (Cleveland, Ohio)·2023
Same journal

Do looks matter in supply chain contracting? An experimental study.

Business economics (Cleveland, Ohio)·2023
Same journal

Public sentiment and opinion regarding the CARES Act.

Business economics (Cleveland, Ohio)·2023
Same journal

ROC approach to forecasting recessions using daily yield spreads.

Business economics (Cleveland, Ohio)·2022

Related Experiment Video

Updated: Jul 20, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

The importance of demographics in economic analysis: the unusual suspects.

D W Berson

    Business Economics (Cleveland, Ohio)
    |January 1, 1997
    PubMed
    Summary

    Integrating demographic data improves economic forecasting, especially for the housing market. Accurate analysis requires combining demographic trends with econometric models for better predictions.

    Keywords:
    AmericasData CollectionDemographic AnalysisDeveloped CountriesEconomic ConditionsEconomic FactorsEconomic ModelEstimation TechnicsGeographic FactorsHousingMacroeconomic FactorsModels, TheoreticalNeedsNorth AmericaNorthern AmericaPopulationPopulation ForecastResearch MethodologyResidence CharacteristicsSpatial DistributionUnited States

    More Related Videos

    Combining Behavioral Endocrinology and Experimental Economics: Testosterone and Social Decision Making
    11:51

    Combining Behavioral Endocrinology and Experimental Economics: Testosterone and Social Decision Making

    Published on: March 2, 2011

    Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System
    09:23

    Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System

    Published on: November 1, 2017

    Related Experiment Videos

    Last Updated: Jul 20, 2026

    Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
    20:36

    Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

    Published on: July 4, 2007

    Combining Behavioral Endocrinology and Experimental Economics: Testosterone and Social Decision Making
    11:51

    Combining Behavioral Endocrinology and Experimental Economics: Testosterone and Social Decision Making

    Published on: March 2, 2011

    Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System
    09:23

    Methodology for Developing Life Tables for Sessile Insects in the Field Using the Whitefly, Bemisia tabaci, in Cotton As a Model System

    Published on: November 1, 2017

    Area of Science:

    • Economics
    • Demography
    • Econometrics

    Background:

    • Economic forecasting, particularly for the housing market, faces challenges due to inherent complexities.
    • Short-term housing market predictions are difficult, and long-term forecasts are often impossible without accounting for demographic shifts.

    Purpose of the Study:

    • To demonstrate the significant improvement in economic analysis and forecasting accuracy by incorporating demographic information.
    • To highlight the necessity of demographic factors in understanding housing market dynamics.

    Main Methods:

    • Utilizing detailed demographic analysis.
    • Employing sound structural econometric modeling.
    • Analyzing cyclical factors influencing housing demand and supply.

    Main Results:

    • Achieved significantly more accurate analyses of the U.S. housing market.
    • Produced improved forecasts for the U.S. housing market.
    • Demonstrated the critical role of demographic data in economic modeling.

    Conclusions:

    • The integration of demographic analysis with econometric modeling substantially enhances the accuracy of economic forecasts.
    • Demographic factors are indispensable for reliable short-term and long-term housing market analysis and prediction.