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The ages (in years) and heights (in inches) of all pitchers for a baseball team are listed. Find the coefficient of variation for each of the two data sets. Then compare the results. Click the icon to view the data sets. CV heights = 4.1 % (Round to one decimal place as needed.) CV ages 15.5% (Round to one decimal place as needed.) Compare the results. What can you conclude? O A. Heights are more variable than ages for all pitchers on this team. B. Ages are more variable than heights for all pitchers on this team. OC. Ages and heights for all pitchers on this team have about the same amount of variability.

The ages (in years) and heights (in inches) of all pitchers for a baseball team are listed. Find the coefficient of variation for each of the two data sets. Then compare the results. Click the icon to view the data sets. CV heights = 4.1 % (Round to one decimal place as needed.) CV ages 15.5% (Round to one decimal place as needed.) Compare the results. What can you conclude? O A. Heights are more variable than ages for all pitchers on this team. B. Ages are more variable than heights for all pitchers on this team. OC. Ages and heights for all pitchers on this team have about the same amount of variability.

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.5: Correlation And Causation
Problem 12PPS
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### Heights and Ages Dataset The table below represents a simple dataset that includes two columns: **Heights** and **Ages**. The Heights are measured in unspecified units, and the Ages are measured in years. The dataset could be used to analyze the relationship between height and age. | **Heights** | **Ages** | |-------------|----------| | 75 | 21 | | 79 | 27 | | 74 | 26 | | 78 | 25 | | 70 | 25 | | 79 | 24 | | 79 | 31 | | 78 | 31 | | 78 | 30 | | 73 | 35 | | 71 | 32 | | 75 | 36 | ### Explanation of the Table Each row in the table corresponds to a pair of height and age values. This type of dataset can be useful in educational settings for various statistical analyses, such as calculating the mean, median, mode, or performing a correlation study to see if heights and ages are related in any significant way. Students and educators can use this data for assignments, projects, or as a basis for learning data analysis techniques.
Transcribed Image Text:### Heights and Ages Dataset The table below represents a simple dataset that includes two columns: **Heights** and **Ages**. The Heights are measured in unspecified units, and the Ages are measured in years. The dataset could be used to analyze the relationship between height and age. | **Heights** | **Ages** | |-------------|----------| | 75 | 21 | | 79 | 27 | | 74 | 26 | | 78 | 25 | | 70 | 25 | | 79 | 24 | | 79 | 31 | | 78 | 31 | | 78 | 30 | | 73 | 35 | | 71 | 32 | | 75 | 36 | ### Explanation of the Table Each row in the table corresponds to a pair of height and age values. This type of dataset can be useful in educational settings for various statistical analyses, such as calculating the mean, median, mode, or performing a correlation study to see if heights and ages are related in any significant way. Students and educators can use this data for assignments, projects, or as a basis for learning data analysis techniques.
### Analyzing Variability in Ages and Heights of Baseball Team Pitchers #### Problem Statement: The ages (in years) and heights (in inches) of all pitchers for a baseball team are provided in datasets. The task is to find the coefficient of variation (CV) for each data set and then compare the results to draw conclusions about the variability in ages and heights. #### Calculation of Coefficient of Variation (CV): - The coefficient of variation for heights (CV_heights) is calculated and found to be 4.1% (rounded to one decimal place). - The coefficient of variation for ages (CV_ages) is calculated and found to be 15.5% (rounded to one decimal place). #### Interpretation: **CV_heights = 4.1%** **CV_ages = 15.5%** The coefficient of variation is a measure of relative variability. It indicates the extent of variability in relation to the mean of the population. #### Comparison and Conclusion: Given the calculated CV values: - **CV_heights = 4.1% (Heights)** - **CV_ages = 15.5% (Ages)** By comparing these two percentages, we can conclude that:
Transcribed Image Text:### Analyzing Variability in Ages and Heights of Baseball Team Pitchers #### Problem Statement: The ages (in years) and heights (in inches) of all pitchers for a baseball team are provided in datasets. The task is to find the coefficient of variation (CV) for each data set and then compare the results to draw conclusions about the variability in ages and heights. #### Calculation of Coefficient of Variation (CV): - The coefficient of variation for heights (CV_heights) is calculated and found to be 4.1% (rounded to one decimal place). - The coefficient of variation for ages (CV_ages) is calculated and found to be 15.5% (rounded to one decimal place). #### Interpretation: **CV_heights = 4.1%** **CV_ages = 15.5%** The coefficient of variation is a measure of relative variability. It indicates the extent of variability in relation to the mean of the population. #### Comparison and Conclusion: Given the calculated CV values: - **CV_heights = 4.1% (Heights)** - **CV_ages = 15.5% (Ages)** By comparing these two percentages, we can conclude that:
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