Use frequency distribution formulas to estimate the sample mean and the sample standard deviation of the data set in Exercise 2.
Table of contents
- 1. Intro to Stats and Collecting Data1h 14m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically2h 5m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables3h 6m
- 6. Normal Distribution and Continuous Random Variables2h 11m
- 7. Sampling Distributions & Confidence Intervals: Mean3h 23m
- Sampling Distribution of the Sample Mean and Central Limit Theorem19m
- Distribution of Sample Mean - Excel23m
- Introduction to Confidence Intervals15m
- Confidence Intervals for Population Mean1h 18m
- Determining the Minimum Sample Size Required12m
- Finding Probabilities and T Critical Values - Excel28m
- Confidence Intervals for Population Means - Excel25m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 25m
- 9. Hypothesis Testing for One Sample3h 29m
- 10. Hypothesis Testing for Two Samples4h 50m
- Two Proportions1h 13m
- Two Proportions Hypothesis Test - Excel28m
- Two Means - Unknown, Unequal Variance1h 3m
- Two Means - Unknown Variances Hypothesis Test - Excel12m
- Two Means - Unknown, Equal Variance15m
- Two Means - Unknown, Equal Variances Hypothesis Test - Excel9m
- Two Means - Known Variance12m
- Two Means - Sigma Known Hypothesis Test - Excel21m
- Two Means - Matched Pairs (Dependent Samples)42m
- Matched Pairs Hypothesis Test - Excel12m
- 11. Correlation1h 24m
- 12. Regression1h 50m
- 13. Chi-Square Tests & Goodness of Fit2h 21m
- 14. ANOVA1h 57m
3. Describing Data Numerically
Standard Deviation
Problem 2.4.48
Textbook Question
Comparing Variation in Different Data Sets In Exercises 45–50, find the coefficient of variation for each of the two data sets. Then compare the results.
Heights and Weights The heights (in inches) and weights (in pounds) of every France national soccer team player that started the 2018 FIFA Men’s World Cup final are listed. (Source: ESPN)

Verified step by step guidance1
Step 1: Understand the coefficient of variation (CV). The CV is a measure of relative variability and is calculated as the ratio of the standard deviation (SD) to the mean (μ), expressed as a percentage: CV = (SD / μ) × 100.
Step 2: Calculate the mean (μ) for each data set. For the heights, sum all the values and divide by the number of players. Repeat the same process for the weights.
Step 3: Calculate the standard deviation (SD) for each data set. Use the formula: SD = sqrt(Σ(xi - μ)^2 / n), where xi represents each individual value, μ is the mean, and n is the number of values in the data set.
Step 4: Compute the coefficient of variation (CV) for each data set using the formula CV = (SD / μ) × 100. Perform this calculation separately for the heights and weights.
Step 5: Compare the CVs of the two data sets. A higher CV indicates greater relative variability in the data set. Discuss the implications of the comparison in terms of the variability of heights versus weights.
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Coefficient of Variation
The coefficient of variation (CV) is a statistical measure of the relative variability of a data set. It is calculated as the ratio of the standard deviation to the mean, expressed as a percentage. This metric allows for the comparison of the degree of variation between different data sets, regardless of their units or scales, making it particularly useful in fields like finance and quality control.
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Standard Deviation
Standard deviation is a measure of the amount of variation or dispersion in a set of values. A low standard deviation indicates that the values tend to be close to the mean, while a high standard deviation indicates that the values are spread out over a wider range. It is a crucial component in calculating the coefficient of variation, as it quantifies the extent of variability in the data.
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Mean
The mean, or average, is a measure of central tendency that is calculated by summing all the values in a data set and dividing by the number of values. It provides a single value that represents the center of the data distribution. In the context of the coefficient of variation, the mean serves as the baseline against which the standard deviation is compared, allowing for a relative assessment of variability.
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