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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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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...
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The Sense of Self: Reflected Self-Appraisal and Social Comparison02:57

The Sense of Self: Reflected Self-Appraisal and Social Comparison

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According to Charles Cooley, we base our image on what we think other people see (Cooley 1902). We imagine how we must appear to others, then react to this speculation. We don certain clothes, prepare our hair in a particular manner, wear makeup, use cologne, and the like—all with the notion that our presentation of ourselves is going to affect how others perceive us. We expect a certain reaction, and, if lucky, we get the one we desire and feel good about it. But more than that, Cooley...
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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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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Interval Level of Measurement00:55

Interval Level of Measurement

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For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...
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QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii
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A Comparison on Some Interval Mapping Approaches for QTL Detection.

Zobaer Akond1,2,3, Md Jahangir Alam1, Mohammad Nazmol Hasan1,4

  • 1Bioinformatics Lab,Department of Statistics,University of Rajshahi,Rajshahi-6205,Bangladesh.

Bioinformation
|August 23, 2019
PubMed
Summary
This summary is machine-generated.

Composite Interval Mapping (CIM) significantly outperforms other methods in detecting quantitative trait loci (QTLs). CIM identified more QTL positions in both simulated and real rice datasets compared to standard interval mapping techniques.

Keywords:
Composite Interval MappingLogarithm-Of-Odds (LOD)Quantitative trait locus (QTL)Simple Interval Mapping

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Area of Science:

  • Genetics
  • Statistical Genomics
  • Bioinformatics

Background:

  • Quantitative trait locus (QTL) analysis integrates phenotypic and genotypic data for gene linkage mapping.
  • Various QTL mapping methods exist, including Standard Interval Mapping (SIM) and Composite Interval Mapping (CIM).
  • Method performance is typically evaluated using the LOD score, with a threshold of 3.0 for significance.

Purpose of the Study:

  • To compare the performance of different QTL mapping methods.
  • To evaluate the effectiveness of Composite Interval Mapping (CIM) against Standard Interval Mapping (SIM) methods.
  • To identify significant marker positions associated with traits in genetic datasets.

Main Methods:

  • Utilized simulated backcross data and a real rice dataset.
  • Applied Standard Interval Mapping (SIM) and Composite Interval Mapping (CIM) techniques.
  • Assessed QTL detection based on LOD scores and significant marker positions.

Main Results:

  • Composite Interval Mapping (CIM) demonstrated superior performance in detecting QTLs.
  • CIM identified three significant QTLs in simulated data, while other methods failed.
  • For a real rice dataset, CIM detected 12 QTL positions, compared to six by each of the four SIM methods.

Conclusions:

  • Composite Interval Mapping (CIM) is a more effective method for QTL detection than standard interval mapping approaches.
  • CIM shows significant advantages in identifying genetic linkage for both linked and unlinked genes.
  • The findings support CIM's utility in genetic research for trait analysis.