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Related Concept Videos

Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

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.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate + error bound)
The...
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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.
William S. Gosset (1876–1937) of the Guinness...
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.
On...
Regression Toward the Mean01:52

Regression Toward the Mean

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 researchers try to extrapolate results...
What are Estimates?01:06

What are Estimates?

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 as the mean,...
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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A computer program for estimating the re-transformed mean in heteroscedastic two-part models.

Xiao-Hua Zhou1, Hao Cheng

  • 1VA Puget Sound Health Care System, Seattle, WA 98108, USA. azhou@u.washington.edu

Computer Methods and Programs in Biomedicine
|March 22, 2008
PubMed
Summary

This study introduces R software to accurately predict healthcare costs, addressing skewed and zero-inclusive data. The tool aids researchers in making reliable inferences about patient healthcare expenditures.

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Area of Science:

  • Biostatistics
  • Health Economics
  • Statistical Software Development

Background:

  • Healthcare cost data often exhibits skewness, heteroscedasticity, and zero values, complicating accurate prediction.
  • Standard statistical methods may yield biased predictions and incorrect inferences for such data.
  • Previous work by Welsh and Zhou proposed a semi-parametric regression model to handle these data characteristics.

Purpose of the Study:

  • To develop and implement a software program for a semi-parametric regression model to predict healthcare costs.
  • To provide clinical researchers with a tool for more accurate healthcare cost prediction.
  • To address the distributional challenges inherent in healthcare cost data.

Main Methods:

  • Development of a software program in the R statistical language.
  • Implementation of a semi-parametric regression model accounting for skewed, heteroscedastic, and zero-inflated data.
  • Inclusion of features for calculating mean estimators, standard deviations, and confidence intervals (including bootstrap options).

Main Results:

  • The developed R program effectively implements the semi-parametric regression method.
  • The software offers user-friendly interactive and efficient batch modes.
  • It provides essential statistical outputs for healthcare cost analysis.

Conclusions:

  • The R software facilitates improved prediction of patient healthcare costs.
  • It offers a robust solution for researchers dealing with complex healthcare cost data distributions.
  • The program enhances the reliability of inferences in health economic studies.