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Related Concept Videos

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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Frequency-dependent Selection01:21

Frequency-dependent Selection

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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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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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Construction of Frequency Distribution01:15

Construction of Frequency Distribution

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A frequency distribution table can be constructed using the steps given below.
First, make a table with two columns—one with the title of the data that needs to be organized, and the other column for frequency. [Draw a third column for tally marks if needed]. Then, take a look at the items given in the data set and decide if an ungrouped frequency distribution table or a grouped frequency distribution table would be more suitable. If there are large sets of different values, then it is...
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Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

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The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
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What is a Frequency Distribution00:51

What is a Frequency Distribution

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A frequency is the number of times a value of the data occurs. The sum of all the frequency values represents the total number of students included in the sample. It is commonly used to group data of quantitative types. Frequency distributions can be displayed in a table, histogram, line graph, dot plot, or pie chart, just to name a few. A histogram is a graphical representation of tabulated frequencies, shown as adjacent rectangles, erected over discrete intervals (bins), with an area equal to...
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A theoretically-sufficient and computationally-practical technique for deterministic frequency seriation.

Carl P Lipo1, Mark E Madsen2, Robert C Dunnell3

  • 1Department of Anthropology and IIRMES, California State University Long Beach, 1250 Bellflower Blvd., Long Beach, CA 90840, USA.

Plos One
|April 30, 2015
PubMed
Summary
This summary is machine-generated.

Archaeologists can now better study cultural transmission through time and space using the new Iterative Deterministic Seriation Solution (IDSS) algorithm. This method provides a computationally feasible way to analyze complex archaeological data, revealing new details about past populations.

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Area of Science:

  • Archaeology
  • Computational Social Science
  • Cultural Evolution

Background:

  • Frequency seriation is a foundational archaeological method for creating chronologies.
  • Previous limitations in quantitative algorithms hindered deterministic seriation solutions for large datasets.
  • Probabilistic approaches offer computational efficiency but sacrifice data detail and model fit assessment.

Purpose of the Study:

  • To introduce a computationally feasible algorithm for constructing deterministic frequency seriations.
  • To enable the study of cultural transmission beyond simple chronological ordering.
  • To address limitations of existing seriation methods in handling complex datasets and model violations.

Main Methods:

  • Development of the Iterative Deterministic Seriation Solution (IDSS) algorithm.
  • Constraining the search space for valid assemblage orders.
  • Application of IDSS to frequency seriation for analyzing cultural transmission.

Main Results:

  • The IDSS algorithm provides a computationally manageable approach to deterministic seriation.
  • The method allows for a more comprehensive use of data features compared to probabilistic methods.
  • Analysis of late prehistoric ceramics from the Mississippi River Valley revealed new details on cultural transmission structures.

Conclusions:

  • The IDSS algorithm represents a significant step towards resolving long-standing problems in archaeological seriation.
  • This new method opens avenues for researching cultural relatedness and transmission.
  • IDSS enhances the utility of frequency seriation for contemporary archaeological research questions.