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Determination of Expected Frequency01:08

Determination of Expected Frequency

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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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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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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Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
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BaSAR-A tool in R for frequency detection.

Emma Granqvist1, Matthew Hartley, Richard J Morris

  • 1Department of Computational & Systems Biology, The John Innes Centre, Norwich Research Park, Norwich, UK. emma.granqvist@jic.ac.uk

Bio Systems
|August 29, 2012
PubMed
Summary
This summary is machine-generated.

Scientists developed BaSAR, a new R package for Bayesian Spectrum Analysis. It helps find key frequencies in noisy biological time series data, even without pre-processing.

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Area of Science:

  • * Computational Biology
  • * Bioinformatics
  • * Data Analysis

Background:

  • * Biological processes like cell cycles and circadian rhythms exhibit periodicity.
  • * Analyzing short, noisy time series data for frequencies is challenging.
  • * Existing methods may require data pre-processing, complicating analysis.

Purpose of the Study:

  • * To introduce BaSAR (Bayesian Spectrum Analysis in R), a novel software package.
  • * To provide a robust method for extracting frequency information from biological time series.
  • * To offer a tool that handles noisy and short datasets effectively.

Main Methods:

  • * Utilizes Bayesian inference techniques for robust frequency detection.
  • * Focuses on identifying a single dominant frequency within time series.
  • * Designed to work without requiring data pre-processing like detrending.

Main Results:

  • * BaSAR successfully extracts frequency information from challenging biological time series.
  • * The Bayesian approach effectively handles noise and short data segments.
  • * The core functions are optimized for detecting key periodicities.

Conclusions:

  • * BaSAR offers an advanced, user-friendly solution for frequency analysis in biology.
  • * The package simplifies the analysis of periodic biological processes.
  • * BaSAR is freely available, promoting wider accessibility in research.