Limits...
Confidence interval based parameter estimation--a new SOCR applet and activity.

Christou N, Dinov ID - PLoS ONE (2011)

Bottom Line: The SOCR confidence interval applet enables the user to empirically explore and investigate the effects of the confidence-level, the sample-size and parameter of interest on the corresponding confidence interval.Two applications of the new interval estimation computational library are presented.The first one is a simulation of confidence interval estimating the US unemployment rate and the second application demonstrates the computations of point and interval estimates of hippocampal surface complexity for Alzheimers disease patients, mild cognitive impairment subjects and asymptomatic controls.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, University of California Los Angeles, Los Angeles, California, United States of America.

ABSTRACT
Many scientific investigations depend on obtaining data-driven, accurate, robust and computationally-tractable parameter estimates. In the face of unavoidable intrinsic variability, there are different algorithmic approaches, prior assumptions and fundamental principles for computing point and interval estimates. Efficient and reliable parameter estimation is critical in making inference about observable experiments, summarizing process characteristics and prediction of experimental behaviors. In this manuscript, we demonstrate simulation, construction, validation and interpretation of confidence intervals, under various assumptions, using the interactive web-based tools provided by the Statistics Online Computational Resource (http://www.SOCR.ucla.edu). Specifically, we present confidence interval examples for population means, with known or unknown population standard deviation; population variance; population proportion (exact and approximate), as well as confidence intervals based on bootstrapping or the asymptotic properties of the maximum likelihood estimates. Like all SOCR resources, these confidence interval resources may be openly accessed via an Internet-connected Java-enabled browser. The SOCR confidence interval applet enables the user to empirically explore and investigate the effects of the confidence-level, the sample-size and parameter of interest on the corresponding confidence interval. Two applications of the new interval estimation computational library are presented. The first one is a simulation of confidence interval estimating the US unemployment rate and the second application demonstrates the computations of point and interval estimates of hippocampal surface complexity for Alzheimers disease patients, mild cognitive impairment subjects and asymptomatic controls.

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Related in: MedlinePlus

Confidence interval for proportion : Sampling from .
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pone-0019178-g014: Confidence interval for proportion : Sampling from .

Mentions: We will examine now the confidence interval for the population parameter . The SOCR confidence interval applet provides calculation for the three types of confidence interval for mentioned in the Background Section. The applet's unique feature is that the user can again sample from any of the available SOCR distributions and define what the meaning of a success is. Success is defined as a randomly chosen observation falling within a user-specified interval. Success intervals are selected by drawing left and right interval limit on the distribution curve using the mouse, or by entering appropriate numerical limits in the distribution left and right text fields (see Figure 14). For example, suppose we choose to sample observations from a normal distribution with mean , and standard deviation . The user may define as success any observations that fall between 4 and 7 or any other valid range. Once this is done, it is easy to compute the sample proportion using where is the number of observations that fall between 4 and 7, and .


Confidence interval based parameter estimation--a new SOCR applet and activity.

Christou N, Dinov ID - PLoS ONE (2011)

Confidence interval for proportion : Sampling from .
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3104980&req=5

pone-0019178-g014: Confidence interval for proportion : Sampling from .
Mentions: We will examine now the confidence interval for the population parameter . The SOCR confidence interval applet provides calculation for the three types of confidence interval for mentioned in the Background Section. The applet's unique feature is that the user can again sample from any of the available SOCR distributions and define what the meaning of a success is. Success is defined as a randomly chosen observation falling within a user-specified interval. Success intervals are selected by drawing left and right interval limit on the distribution curve using the mouse, or by entering appropriate numerical limits in the distribution left and right text fields (see Figure 14). For example, suppose we choose to sample observations from a normal distribution with mean , and standard deviation . The user may define as success any observations that fall between 4 and 7 or any other valid range. Once this is done, it is easy to compute the sample proportion using where is the number of observations that fall between 4 and 7, and .

Bottom Line: The SOCR confidence interval applet enables the user to empirically explore and investigate the effects of the confidence-level, the sample-size and parameter of interest on the corresponding confidence interval.Two applications of the new interval estimation computational library are presented.The first one is a simulation of confidence interval estimating the US unemployment rate and the second application demonstrates the computations of point and interval estimates of hippocampal surface complexity for Alzheimers disease patients, mild cognitive impairment subjects and asymptomatic controls.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, University of California Los Angeles, Los Angeles, California, United States of America.

ABSTRACT
Many scientific investigations depend on obtaining data-driven, accurate, robust and computationally-tractable parameter estimates. In the face of unavoidable intrinsic variability, there are different algorithmic approaches, prior assumptions and fundamental principles for computing point and interval estimates. Efficient and reliable parameter estimation is critical in making inference about observable experiments, summarizing process characteristics and prediction of experimental behaviors. In this manuscript, we demonstrate simulation, construction, validation and interpretation of confidence intervals, under various assumptions, using the interactive web-based tools provided by the Statistics Online Computational Resource (http://www.SOCR.ucla.edu). Specifically, we present confidence interval examples for population means, with known or unknown population standard deviation; population variance; population proportion (exact and approximate), as well as confidence intervals based on bootstrapping or the asymptotic properties of the maximum likelihood estimates. Like all SOCR resources, these confidence interval resources may be openly accessed via an Internet-connected Java-enabled browser. The SOCR confidence interval applet enables the user to empirically explore and investigate the effects of the confidence-level, the sample-size and parameter of interest on the corresponding confidence interval. Two applications of the new interval estimation computational library are presented. The first one is a simulation of confidence interval estimating the US unemployment rate and the second application demonstrates the computations of point and interval estimates of hippocampal surface complexity for Alzheimers disease patients, mild cognitive impairment subjects and asymptomatic controls.

Show MeSH
Related in: MedlinePlus