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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
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Confidence Intervals01:21

Confidence Intervals

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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...
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Determination of Expected Frequency01:08

Determination of Expected Frequency

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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

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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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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
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A method to construct confidence interval for expected response to multi-trait selection.

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An interval estimation of expected response to selection.

G C Tai1

  • 1Agriculture Canada Research Station, Fredericton, New Brunswick, Canada.

TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik
|December 7, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a method for estimating expected response to selection using progeny test data. It analyzes how the number of lines and replicates affects the precision of these predictions.

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Area of Science:

  • Animal breeding and genetics
  • Quantitative genetics

Background:

  • Response to selection is a key metric in animal breeding.
  • Progeny testing is a common method for evaluating genetic merit.
  • Accurate estimation of expected response to selection is crucial for genetic gain.

Purpose of the Study:

  • To develop a method for interval estimation of expected response to selection.
  • To investigate the impact of experimental design on prediction precision.

Main Methods:

  • Utilized progeny test experiment results.
  • Developed a method for constructing confidence limits for expected response to selection.
  • Analyzed the influence of the number of lines and replicates.

Main Results:

  • A method for interval estimation of expected response to selection was established.
  • The number of lines and replicates significantly influences the precision of selection response predictions.
  • Confidence limit structure provides insights into prediction accuracy.

Conclusions:

  • The presented method allows for a more robust estimation of expected response to selection.
  • Optimizing the number of lines and replicates in progeny tests is essential for improving prediction accuracy.
  • This work contributes to more precise genetic evaluations in breeding programs.