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Sagot :
To create a stem and leaf plot with the provided data, we need to match each city's population with the correct stem and leaf.
Step-by-step solution:
1. Identify Stems and Leaves:
- Stem: The first digit of the population size (i.e., the millions place)
- Leaf: The remaining part of the population size (i.e., the hundred-thousands to units place)
2. Segregate Data by Stems:
- Collect all cities that share the same stem.
Data Categorization According to Stems:
Stem 6:
- Foshan: Stem = 6, Leaf = 151622 (6,151,622)
- Hanol: Stem = 6, Leaf = 844100 (6,844,100)
Stem 8:
- Lima: Stem = 8, Leaf = 693387 (8,693,387)
- Bangkok: Stem = 8, Leaf = 280925 (8,280,925)
Stem 3:
- Xiamen: Stem = 3, Leaf = 531347 (3,531,347)
- Yokohama: Stem = 3, Leaf = 680267 (3,680,267)
- Berlin: Stem = 3, Leaf = 517424 (3,517,424)
- Madrid: Stem = 3, Leaf = 207247 (3,207,247)
- Wenzhou: Stem = 3, Leaf = 039439 (3,039,439)
- New Tajpei City: Stem = 3, Leaf = 954929 (3,954,929)
- Nairobi: Stem = 3, Leaf = 138369 (3,138,369)
- Quanzhou: Stem = 3, Leaf = 520846 (3,520,846)
- Kabu: Stem = 3, Leaf = 414100 (3,414,100)
Stem 4:
- Dar es Salaam: Stem = 4, Leaf = 364541 (4,364,541)
- Xian: Stem = 4, Leaf = 467837 (4,467,837)
- Suzhou: Stem = 4, Leaf = 327066 (4,327,066)
- Chennal: Stem = 4, Leaf = 792949 (4,792,949)
Summarizing the result in a stem-and-leaf table:
[tex]\[ \begin{array}{|c|l|} \hline \text{Stem} & \text{Leaf ( City, Leaf part )} \\ \hline 6 & \text{(Foshan, 151622), (Hanol, 844100)} \\ \hline 8 & \text{(Lima, 693387), (Bangkok, 280925)} \\ \hline 3 & \text{(Xiamen, 531347), (Yokohama, 680267), (Berlin, 517424), (Madrid, 207247),} \\ & \text{(Wenzhou, 039439), (New Tajpei City, 954929), (Nairobi, 138369), (Quanzhou, 520846), (Kabu, 414100)} \\ \hline 4 & \text{(Dar es Salaam, 364541), (Xian, 467837), (Suzhou, 327066), (Chennal, 792949)} \\ \hline \end{array} \][/tex]
Through these steps, we can accurately categorize the population sizes of various cities into a stem-and-leaf plot structure.
Step-by-step solution:
1. Identify Stems and Leaves:
- Stem: The first digit of the population size (i.e., the millions place)
- Leaf: The remaining part of the population size (i.e., the hundred-thousands to units place)
2. Segregate Data by Stems:
- Collect all cities that share the same stem.
Data Categorization According to Stems:
Stem 6:
- Foshan: Stem = 6, Leaf = 151622 (6,151,622)
- Hanol: Stem = 6, Leaf = 844100 (6,844,100)
Stem 8:
- Lima: Stem = 8, Leaf = 693387 (8,693,387)
- Bangkok: Stem = 8, Leaf = 280925 (8,280,925)
Stem 3:
- Xiamen: Stem = 3, Leaf = 531347 (3,531,347)
- Yokohama: Stem = 3, Leaf = 680267 (3,680,267)
- Berlin: Stem = 3, Leaf = 517424 (3,517,424)
- Madrid: Stem = 3, Leaf = 207247 (3,207,247)
- Wenzhou: Stem = 3, Leaf = 039439 (3,039,439)
- New Tajpei City: Stem = 3, Leaf = 954929 (3,954,929)
- Nairobi: Stem = 3, Leaf = 138369 (3,138,369)
- Quanzhou: Stem = 3, Leaf = 520846 (3,520,846)
- Kabu: Stem = 3, Leaf = 414100 (3,414,100)
Stem 4:
- Dar es Salaam: Stem = 4, Leaf = 364541 (4,364,541)
- Xian: Stem = 4, Leaf = 467837 (4,467,837)
- Suzhou: Stem = 4, Leaf = 327066 (4,327,066)
- Chennal: Stem = 4, Leaf = 792949 (4,792,949)
Summarizing the result in a stem-and-leaf table:
[tex]\[ \begin{array}{|c|l|} \hline \text{Stem} & \text{Leaf ( City, Leaf part )} \\ \hline 6 & \text{(Foshan, 151622), (Hanol, 844100)} \\ \hline 8 & \text{(Lima, 693387), (Bangkok, 280925)} \\ \hline 3 & \text{(Xiamen, 531347), (Yokohama, 680267), (Berlin, 517424), (Madrid, 207247),} \\ & \text{(Wenzhou, 039439), (New Tajpei City, 954929), (Nairobi, 138369), (Quanzhou, 520846), (Kabu, 414100)} \\ \hline 4 & \text{(Dar es Salaam, 364541), (Xian, 467837), (Suzhou, 327066), (Chennal, 792949)} \\ \hline \end{array} \][/tex]
Through these steps, we can accurately categorize the population sizes of various cities into a stem-and-leaf plot structure.
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