In one of Sollivan’s statistics sections, the standard deviation of the heights of all students was 3.9 inches. The standard deviation of the heights of males was 3.4 inches and the standard deviation of females was 3.3 inches. Why is the standard deviation of the entire class more than the standard deviation of the males and females considered separately?
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 3.3.7
Textbook Question
Exit Velocity Use the frequency distribution whose class width is 4 obtained in Problem 25 in Section 2.2 to approximate the mean and standard deviation exit velocity. Compare these results to the actual mean and standard deviation exit velocity.
Verified step by step guidance1
Identify the midpoints of each class interval in the frequency distribution. The midpoint for a class is calculated as \(\text{Midpoint} = \frac{\text{Lower class limit} + \text{Upper class limit}}{2}\).
Multiply each class midpoint by its corresponding frequency to find the total contribution of each class to the sum of the data values.
Calculate the approximate mean using the formula: \(\bar{x} = \frac{\sum (f \times x)}{\sum f}\), where \(f\) is the frequency and \(x\) is the class midpoint.
To approximate the standard deviation, first calculate the variance using the formula: \(s^2 = \frac{\sum f (x - \bar{x})^2}{\sum f - 1}\), where \(\bar{x}\) is the approximate mean, \(x\) is the class midpoint, and \(f\) is the frequency.
Take the square root of the variance to find the approximate standard deviation: \(s = \sqrt{s^2}\). Then, compare these approximate values of mean and standard deviation to the actual values given or calculated from the raw data.
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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Frequency Distribution and Class Width
A frequency distribution organizes data into classes or intervals, showing how often data points fall within each range. Class width is the size of each interval, which affects the grouping of data and the accuracy of approximations for measures like mean and standard deviation.
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Intro to Frequency Distributions
Approximating Mean and Standard Deviation from Grouped Data
When data is grouped into intervals, the mean and standard deviation can be estimated using class midpoints and frequencies. This involves calculating a weighted average for the mean and using the squared deviations of midpoints to approximate variability.
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Calculating Standard Deviation
Comparison of Approximate and Actual Statistics
Comparing approximate statistics from grouped data to actual values from raw data helps assess the accuracy of grouping methods. Differences arise due to data grouping, and understanding this comparison highlights the trade-off between data simplification and precision.
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