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Related Concept Videos

Multiple Regression01:25

Multiple Regression

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...
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Updated: Jun 19, 2026

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

Bayesian variable selection for multivariate spatially varying coefficient regression.

Brian J Reich1, Montserrat Fuentes, Amy H Herring

  • 1Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695, USA. reich@stat.ncsu.edu

Biometrics
|October 13, 2009
PubMed
Summary
This summary is machine-generated.

Physical activity during pregnancy is recommended but rarely achieved. Individual factors, not neighborhood environments, were found to be more influential on pregnant women's activity levels in North Carolina.

Related Experiment Videos

Last Updated: Jun 19, 2026

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

Area of Science:

  • Public Health
  • Environmental Health
  • Reproductive Health

Background:

  • Physical activity offers significant health benefits, including cardiovascular fitness and weight management.
  • Current recommendations advise 30 minutes of moderate exercise for pregnant women, yet adherence is low.
  • Research is shifting focus from individual factors to the built environment's influence on physical activity.

Purpose of the Study:

  • To examine the impact of built environment characteristics on physical activity levels in pregnant women.
  • To investigate associations between physical activity and personal, meteorological, and neighborhood factors.
  • To explore spatial variations in these associations.

Main Methods:

  • Utilized a socioecologic framework to analyze data from pregnant women in North Carolina.
  • Simultaneously analyzed six types of physical activity, considering cross-dependencies.
  • Employed a multivariate regression model with spatially varying coefficients and Bayesian variable selection.

Main Results:

  • Individual-level factors demonstrated a stronger influence on pregnant women's physical activity than neighborhood characteristics.
  • Some individual factors exhibited spatially varying associations with physical activity levels.
  • The study identified key predictors influencing physical activity in this population.

Conclusions:

  • Personal characteristics are more critical than neighborhood environments in determining pregnant women's physical activity.
  • Understanding spatially varying associations is crucial for targeted interventions.
  • Further research can leverage these findings to promote physical activity in pregnant populations.