JPARK 's question at Yahoo Answers (Cardinality)

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In summary, the question is asking to show that if the cardinality of sets A and B are less than or equal to the cardinality of set C, then the cardinality of set A is also less than or equal to the cardinality of set C. This can be proven by showing that if there exist injective functions from A to B and from B to C, then the composition of these functions (g ∘ f) is also injective, proving that the cardinality of A is less than or equal to the cardinality of C.
  • #1
Fernando Revilla
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Here is the question:

My professor wasn't clear in lecture today, so I'm not exactly sure how I should show this...Can anyone help?

Let A,B,C be sets. Show that if |A| ≤ |B| and |B| ≤ |C|, then |A| ≤ |C|

Here is a link to the question:

Cardinality of Sets Homework Problem? - Yahoo! Answers

I have posted a link there to this topic so the OP can find my response.
 
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  • #2
Hello JPARK, $$|A|\leq |B|\Leftrightarrow \exists f:A\to B\mbox{ injective}\\
|B|\leq |C|\Leftrightarrow \exists g:B\to C\mbox{ injective}$$ But $g\circ f:A\to C$ is injective (the composition of injective maps is injective), hence $|A|\leq |C|$.
 

Related to JPARK 's question at Yahoo Answers (Cardinality)

1. What is cardinality and why is it important in data analysis?

Cardinality refers to the number of distinct values in a dataset. It is important in data analysis because it helps to identify unique records and determine the size and complexity of a dataset. It also plays a crucial role in database design and optimization.

2. How do I calculate cardinality in my dataset?

To calculate cardinality, you need to count the number of unique values in a specific column or attribute of your dataset. This can be done using tools such as Excel or programming languages like SQL. You can also use the COUNT DISTINCT function in most data analysis software.

3. What is the difference between low and high cardinality?

Low cardinality means that a dataset has a small number of distinct values, while high cardinality indicates a large number of distinct values. Low cardinality can be useful for categorical data, while high cardinality is often seen in numerical or continuous data. High cardinality can also make data analysis and processing more complex and time-consuming.

4. How does cardinality affect data storage and processing?

High cardinality can significantly impact data storage and processing, as it requires more resources and time to handle a large number of distinct values. It can also lead to data redundancy and make database queries more complicated. On the other hand, low cardinality can help to optimize data storage and processing, making it easier to manage and analyze.

5. Can cardinality be changed or manipulated?

Cardinality is a fundamental property of a dataset and cannot be changed or manipulated. However, it can be managed by applying data cleaning techniques, such as merging or grouping similar values, to reduce the number of distinct values and improve data analysis. Database optimization techniques can also help to manage cardinality in large datasets.

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