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Related Concept Videos

Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Regression Analysis01:11

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Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
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Testing a Claim about Standard Deviation01:19

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A complete procedure to test a claim about population standard deviation or population variance is explained here.
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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
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An R-Based Landscape Validation of a Competing Risk Model
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Regression standardization with the R package stdReg.

Arvid Sjölander1

  • 1, Nobels väg 12 A, 171 77, Stockholm, Sweden. arvid.sjolander@ki.se.

European Journal of Epidemiology
|May 16, 2016
PubMed
Summary

This study introduces stdReg, an R package for regression standardization. It helps estimate population causal effects from logistic and Cox regression models using real-world data.

Keywords:
Cox regressionHazard ratioLogistic regressionOdds ratioStandardization

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical Software

Background:

  • Regression models like logistic and Cox regression are standard for adjusting confounders in epidemiologic research.
  • These models estimate conditional associations (odds ratios, hazard ratios) but not marginal effects directly.
  • Estimating marginal associations is crucial for inferring population causal effects.

Purpose of the Study:

  • To introduce the stdReg R package for performing regression standardization.
  • To facilitate the estimation of marginal measures of association from regression models.
  • To enable the calculation of population causal effects when confounding is adequately controlled.

Main Methods:

  • The stdReg package implements regression standardization for generalized linear models and Cox regression.
  • It allows users to apply these methods to their own datasets.
  • The package is demonstrated using publicly available real data.

Main Results:

  • The stdReg package provides a user-friendly tool for advanced statistical analysis.
  • Demonstrations show the practical application of regression standardization.
  • The package supports estimation of marginal associations and population causal effects.

Conclusions:

  • Regression standardization is a valuable technique for causal inference in observational studies.
  • The stdReg R package simplifies the implementation of regression standardization.
  • This tool can enhance the ability of researchers to estimate population causal effects.