Identify The Tables That Represent The Relative Frequencies Of This Data Either By Row Or By Column. Round Your Answers To The Nearest Hundredth.$[ \begin{tabular}{|l|l|l|} \hline & 150 Cc & 180 Cc \ \hline Black & 0.43 & 0.57 \ \hline Blue &

Understanding Relative Frequencies

Relative frequencies are a way to express the proportion of each category in a dataset. They are calculated by dividing the frequency of each category by the total number of observations. In this article, we will identify the tables that represent the relative frequencies of the given data either by row or by column.

Given Data

The given data is presented in a table with two rows and two columns. The rows represent different categories, and the columns represent different sizes (150 cc and 180 cc). The table is as follows:

Identifying Relative Frequencies by Row

To identify the relative frequencies by row, we need to calculate the proportion of each category in each row. The relative frequency of each category is calculated by dividing the frequency of each category by the total number of observations in that row.

For the first row (Black), the relative frequency is calculated as follows:

Relative frequency = (0.43 + 0.57) / 1 = 1

However, since we are asked to round our answers to the nearest hundredth, we will round the relative frequency to 1.00.

For the second row (Blue), the relative frequency is calculated as follows:

Relative frequency = (0.00 + 0.00) / 1 = 0

Since the frequencies for the Blue category are both 0, the relative frequency is also 0.

Identifying Relative Frequencies by Column

To identify the relative frequencies by column, we need to calculate the proportion of each category in each column. The relative frequency of each category is calculated by dividing the frequency of each category by the total number of observations in that column.

For the first column (150 cc), the relative frequency is calculated as follows:

Relative frequency = (0.43 + 0.00) / 1 = 0.43

Rounded to the nearest hundredth, the relative frequency is 0.43.

For the second column (180 cc), the relative frequency is calculated as follows:

Relative frequency = (0.57 + 0.00) / 1 = 0.57

Rounded to the nearest hundredth, the relative frequency is 0.57.

Conclusion

In conclusion, the tables that represent the relative frequencies of the given data either by row or by column are as follows:

• By row:
  • Black: 1.00
  • Blue: 0.00
• By column:
  • 150 cc: 0.43
  • 180 cc: 0.57

Q: What is relative frequency?

A: Relative frequency is a measure of the proportion of each category in a dataset. It is calculated by dividing the frequency of each category by the total number of observations.

Q: How do I calculate relative frequency?

A: To calculate relative frequency, you need to divide the frequency of each category by the total number of observations. For example, if you have a dataset with 10 observations and 3 of them belong to a particular category, the relative frequency of that category would be 3/10 = 0.30.

Q: What is the difference between relative frequency and frequency?

A: Frequency refers to the number of times a particular category occurs in a dataset, while relative frequency refers to the proportion of each category in the dataset. For example, if a category occurs 3 times in a dataset of 10 observations, the frequency is 3, but the relative frequency is 0.30 (3/10).

Q: Why is relative frequency important?

A: Relative frequency is important because it helps to understand the proportion of each category in a dataset. This can be useful in a variety of contexts, such as:

• Identifying trends and patterns in data
• Making informed decisions based on data
• Comparing data across different groups or categories

Q: Can I use relative frequency to compare data across different datasets?

A: Yes, you can use relative frequency to compare data across different datasets. However, you need to make sure that the datasets are comparable and that the categories are the same.

Q: How do I interpret relative frequency values?

A: Relative frequency values can be interpreted as follows:

• A value of 0 indicates that a category does not occur in the dataset.
• A value of 1 indicates that a category occurs in every observation in the dataset.
• A value between 0 and 1 indicates the proportion of the category in the dataset.

Q: Can I use relative frequency to identify outliers?

A: Yes, you can use relative frequency to identify outliers. If a category has a relative frequency that is significantly different from the others, it may indicate an outlier.

Q: How do I calculate relative frequency in a table?

A: To calculate relative frequency in a table, you need to divide the frequency of each category by the total number of observations. You can use the following formula:

Relative frequency = (Frequency of category / Total number of observations)

Q: Can I use relative frequency to make predictions?

A: Yes, you can use relative frequency to make predictions. However, you need to make sure that the data is representative of the population and that the categories are the same.

Q: How do I use relative frequency in data analysis?

A: Relative frequency can be used in a variety of data analysis tasks, such as:

• Identifying trends and patterns in data
• Making informed decisions based on data
• Comparing data across different groups or categories
• Identifying outliers

By understanding relative frequency and how to use it, you can gain valuable insights from your data and make informed decisions.