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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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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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Relative Frequency Distribution00:55

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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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A percentage frequency distribution, in general, is a display of data that indicates the percentage of observations for each data point or grouping of data points. It is a commonly used method for expressing the relative frequency of survey responses and other data. The percentage frequency distributions are often displayed as bar graphs, pie charts, or tables.
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Relative Frequency Histogram01:14

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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 cumulative frequency distribution is another type of frequency distribution. Instead of reporting how many data values fall in some classes, it reports how many data values are contained in either that class or any class to its left. Technically, it means the sum of frequencies of the class and all the classes below it in a frequency distribution. A cumulative frequency is calculated by adding the frequency of each class lower than the corresponding class interval or category. In general, a...
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Subtlex-pl: subtitle-based word frequency estimates for Polish.

Paweł Mandera1, Emmanuel Keuleers, Zofia Wodniecka

  • 1Department of Experimental Psychology, Ghent University, Henri Dunantlaan 2, 9000, Gent, Belgium, pawel.mandera@ugent.be.

Behavior Research Methods
|June 20, 2014
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Summary
This summary is machine-generated.

We introduce SUBTLEX-PL, a new database of Polish word frequencies from movie subtitles. Combining these with written corpus data best predicts human lexical decisions, offering valuable psycholinguistic research tools.

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Area of Science:

  • Psycholinguistics
  • Corpus Linguistics
  • Lexical Processing

Background:

  • Existing Polish word frequency lists often rely on written corpora.
  • Written corpora may not accurately reflect spoken language frequencies, potentially overestimating formal word usage.

Purpose of the Study:

  • To introduce SUBTLEX-PL, a novel database of Polish word frequencies derived from movie subtitles.
  • To compare the predictive power of SUBTLEX-PL frequencies against traditional written corpus frequencies in a lexical decision task.
  • To investigate the complementary utility of subtitle-based and written corpus-based frequency measures.

Main Methods:

  • Development of the SUBTLEX-PL database, incorporating word form frequencies, contextual diversity, part-of-speech information, lemma frequencies, and bigrams.
  • Conducting two lexical decision experiments with Polish participants.
  • Statistical analysis comparing the performance of different frequency measures in predicting reaction times.

Main Results:

  • SUBTLEX-PL provides Polish word frequencies based on a large corpus of movie subtitles.
  • Complementary use of SUBTLEX-PL and written corpus frequencies significantly improved predictions of human performance in lexical decision tasks.
  • Written corpora were found to overestimate frequencies for formal words compared to subtitle-derived frequencies.

Conclusions:

  • SUBTLEX-PL offers a valuable resource for psycholinguistic research in Polish, complementing existing written corpora.
  • The findings highlight the importance of considering different corpus types for accurate frequency estimation in lexical research.
  • The database's comprehensive features support diverse psycholinguistic investigations in Polish.