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Related Concept Videos

Relative Frequency Distribution00:55

Relative Frequency Distribution

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A relative frequency distribution is the proportion or fraction of times a value occurs in a data set. To find the relative frequencies, one can divide each frequency by the total number of data points in the sample. It is very similar to a regular frequency distribution, except that instead of reporting how many data values fall in a class, a relative frequency distribution reports the fraction of data values that fall in a class. These fractions or proportions are called relative frequencies...
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The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
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Expected Frequencies in Goodness-of-Fit Tests01:19

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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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The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
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A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
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In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
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Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
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In-text citation's frequencies-based recommendations of relevant research papers.

Abdul Shahid1, Muhammad Tanvir Afzal2, Abdullah Alharbi3

  • 1Institute of Computing, Kohat University of Science & Technology, Kohat, Pakistan.

Peerj. Computer Science
|June 21, 2021
PubMed
Summary

Analyzing in-text citation frequency significantly improves document relevance discovery. This method surpasses traditional bibliographic and content-based approaches for enhanced information retrieval.

Keywords:
CitationsDigital LibrariesIn-text CitationRelevant Documents

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Area of Science:

  • Information Science
  • Computer Science
  • Bibliometrics

Background:

  • Document retrieval relies on bibliographic data, but textual features offer improved accuracy.
  • Previous research indicated deep citation analysis (content level) is superior to surface-level (bibliographic) analysis.
  • A high relevancy was observed when in-text citation frequency exceeded five instances within the citing paper.

Purpose of the Study:

  • To extend previous findings by evaluating in-text citation frequency on a comprehensive dataset.
  • To compare the effectiveness of in-text citation analysis against state-of-the-art methods (content, metadata, bibliography).
  • To assess the impact of in-text citation frequency on document relevance in digital libraries.

Main Methods:

  • Evaluation of a comprehensive dataset comprising 1,200 documents and approximately 16,000 references from the Journal of Computer Science (J.UCS).
  • Conducting a user study to assess the precision of different document retrieval techniques.
  • Comparative analysis against content-based, metadata-based, and bibliographic coupling methods.

Main Results:

  • In-text citation frequency demonstrated higher precision in identifying relevant papers compared to content, bibliographic coupling, and metadata techniques.
  • The study confirmed the significant role of deep citation analysis in improving document relevance.
  • User study results validated the superiority of in-text citation frequency over other evaluated methods.

Conclusions:

  • In-text citation frequency is a highly effective metric for enhancing document relevance in information retrieval systems.
  • The findings suggest that incorporating in-text citation analysis can improve the quality of digital libraries and information systems.
  • Future research may redefine sophisticated measures by leveraging in-text citation data for more accurate document discovery.