Table 1 Frequency City (Millions of people) Relative Frequency Los Angeles 3.83 0.2048 Chicago 2.84 0.1519 Houston 2.21 0.1182 Phoenix 1.55 0.0829 New York City 8.27 0.4422 Total 18.70 100 The U.S. population is about 300 million. The frequency of Los Angeles residents in the U.S. population is about 3.83 million people. The relative frequency of Los Angeles residents in the U.S. population is about In 1935, Harvard linguist George Zipf pointed out that the frequency of the kth most frequent word in a language is roughly proportional to 1/k. This implies that the second most frequent word in a language has a frequency one-half that of the most frequent word, the third most frequent word has a frequency one-third that of the most frequent word, and so on. A distribution that follows this rule is said to obey Zipf's Law. Zipf's Law has been observed not only in word distributions, but in other phenomena as well, such as the populations of cities. The frequency of the second most frequent word in the Brown Corpus is that of the most frequent word. The population of the second largest city in the United States is that of the largest city. The frequency of the fourth most frequent word in the Brown Corpus is that of the most frequent word. The population of the fourth largest city in the United States is that of the largest city.

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Author:Amos Gilat
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Table 1
Frequency
City
(Millions of people)
Relative Frequency
Los Angeles
3.83
0.2048
Chicago
2.84
0.1519 ▼
Houston
2.21 v
0.1182
Phoenix
1.55
0.0829
New York City
8.27
0.4422
Total
18.70
100
The U.S. population is about 300 million. The frequency of Los Angeles residents in the U.s. population
about 3.83 million
people. The relative
frequency of Los Angeles residents in the U.S. population is about
In 1935, Harvard linguist George Zipf pointed out that the frequency of the kth most frequent word in a language is roughly proportional to 1/k. This
implies that the second most frequent word in a language has a frequency one-half that of the most frequent word, the third most frequent word has
a frequency one-third that of the most frequent word, and so on. A distribution that follows this rule is said to obey Zipf's Law.
Zipf's Law has been observed not only in word distributions, but in other phenomena as well, such as the populations of cities.
The frequency of the second most frequent word in the Brown Corpus is
that of the most frequent word. The population of the second largest
city in the United States is
v that of the largest city.
The frequency of the fourth most frequent word in the Brown Corpus is
v that of the most frequent word. The population of the fourth
largest city in the United States is
v that of the largest city.
Transcribed Image Text:Table 1 Frequency City (Millions of people) Relative Frequency Los Angeles 3.83 0.2048 Chicago 2.84 0.1519 ▼ Houston 2.21 v 0.1182 Phoenix 1.55 0.0829 New York City 8.27 0.4422 Total 18.70 100 The U.S. population is about 300 million. The frequency of Los Angeles residents in the U.s. population about 3.83 million people. The relative frequency of Los Angeles residents in the U.S. population is about In 1935, Harvard linguist George Zipf pointed out that the frequency of the kth most frequent word in a language is roughly proportional to 1/k. This implies that the second most frequent word in a language has a frequency one-half that of the most frequent word, the third most frequent word has a frequency one-third that of the most frequent word, and so on. A distribution that follows this rule is said to obey Zipf's Law. Zipf's Law has been observed not only in word distributions, but in other phenomena as well, such as the populations of cities. The frequency of the second most frequent word in the Brown Corpus is that of the most frequent word. The population of the second largest city in the United States is v that of the largest city. The frequency of the fourth most frequent word in the Brown Corpus is v that of the most frequent word. The population of the fourth largest city in the United States is v that of the largest city.
A corpus is a technical term for a collection of texts used to analyze a language and verify its linguistic properties. The first modern, computer-
readable corpus was the Brown Corpus of Standard American English, compiled by Henry Kucera and W. Nelson Francis of Brown University. The
Brown Corpus draws from American English texts printed in 1961 and was for many years a widely cited resource in computational linguistics.
The five most frequently occurring words in the Brown Corpus are the, of, and, to, and a. Consider a data set consisting of all occurrences of these
words in the Corpus. The values of the variable named Word are and, to, of, the, and a, so Word is a nominal variable with five categories.
Frequency and relative frequency distributions are constructed to summarize the data. They are shown in the table that follows, but the table is
incomplete. Use the dropdown menus to complete the table.
Table 1
Word
Frequency
Relative Frequency
(Thousands of occurrences)
and
28.9
0.1566
to
26.1
0.1415 v
of
36.4 v
0.1973
the
70.0
0.3794
a
23.1
0.1252
Total
184.5
1.0000 v
The Brown Corpus contains about 1 million words. The frequency of the word and in the entire corpus is about 28,90 0 v occurrences. The relative
frequency of the word and in the entire corpus is about 0.0289 ▼
A census is an enumeration of a population. The U.S. Census Bureau conducts a census every 10 years, but in addition, the Population Estimates
Program of the bureau publishes population estimates for incorporated places every year. According to 2007 estimates, the five largest U.S. cities (by
population) are New York City, Los Angeles, Chicago, Houston, and Phoenix.
Consider a data set consisting of all the residents of these five cities. The values of the variable named City are Los Angeles, Chicago, Houston,
Phoenix, and New York City, so City is a nominal variable with five categories. Frequency and relative frequency distributions are provided in the table
below, but the table is incomplete. Use the dropdown menus to complete the table.
Transcribed Image Text:A corpus is a technical term for a collection of texts used to analyze a language and verify its linguistic properties. The first modern, computer- readable corpus was the Brown Corpus of Standard American English, compiled by Henry Kucera and W. Nelson Francis of Brown University. The Brown Corpus draws from American English texts printed in 1961 and was for many years a widely cited resource in computational linguistics. The five most frequently occurring words in the Brown Corpus are the, of, and, to, and a. Consider a data set consisting of all occurrences of these words in the Corpus. The values of the variable named Word are and, to, of, the, and a, so Word is a nominal variable with five categories. Frequency and relative frequency distributions are constructed to summarize the data. They are shown in the table that follows, but the table is incomplete. Use the dropdown menus to complete the table. Table 1 Word Frequency Relative Frequency (Thousands of occurrences) and 28.9 0.1566 to 26.1 0.1415 v of 36.4 v 0.1973 the 70.0 0.3794 a 23.1 0.1252 Total 184.5 1.0000 v The Brown Corpus contains about 1 million words. The frequency of the word and in the entire corpus is about 28,90 0 v occurrences. The relative frequency of the word and in the entire corpus is about 0.0289 ▼ A census is an enumeration of a population. The U.S. Census Bureau conducts a census every 10 years, but in addition, the Population Estimates Program of the bureau publishes population estimates for incorporated places every year. According to 2007 estimates, the five largest U.S. cities (by population) are New York City, Los Angeles, Chicago, Houston, and Phoenix. Consider a data set consisting of all the residents of these five cities. The values of the variable named City are Los Angeles, Chicago, Houston, Phoenix, and New York City, so City is a nominal variable with five categories. Frequency and relative frequency distributions are provided in the table below, but the table is incomplete. Use the dropdown menus to complete the table.
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