Table of contents
- 1. Intro to Stats and Collecting Data55m
- 2. Describing Data with Tables and Graphs1h 55m
- 3. Describing Data Numerically1h 45m
- 4. Probability2h 16m
- 5. Binomial Distribution & Discrete Random Variables2h 33m
- 6. Normal Distribution and Continuous Random Variables1h 38m
- 7. Sampling Distributions & Confidence Intervals: Mean1h 3m
- 8. Sampling Distributions & Confidence Intervals: Proportion1h 12m
- 9. Hypothesis Testing for One Sample1h 1m
- 10. Hypothesis Testing for Two Samples2h 8m
- 11. Correlation48m
- 12. Regression1h 4m
- 13. Chi-Square Tests & Goodness of Fit1h 20m
- 14. ANOVA1h 0m
3. Describing Data Numerically
Standard Deviation
Problem 2.4.51b
Textbook Question
Extending Concepts
Alternative Formula You used SSₓ = Σ(x − x̄)² when calculating variance and standard deviation. An alternative formula that is sometimes more convenient for hand calculations is
SSₓ = Σ x² − (Σ x)² / n.
You can find the sample variance by dividing the sum of squares by n − 1 and the sample standard deviation by finding the square root of the sample variance.
b. Use the alternative formula to calculate the sample standard deviation for the data set in Exercise 15.

1
Step 1: Understand the alternative formula for the sum of squares (SSₓ). The formula is SSₓ = Σx² − (Σx)² / n, where Σx² is the sum of the squares of the data values, Σx is the sum of the data values, and n is the number of data points.
Step 2: Calculate Σx² by squaring each data value in the data set and then summing these squared values.
Step 3: Calculate Σx by summing all the data values in the data set. Then, square this sum and divide it by n to compute (Σx)² / n.
Step 4: Subtract (Σx)² / n from Σx² to find the sum of squares (SSₓ).
Step 5: Divide SSₓ by n − 1 to calculate the sample variance. Finally, take the square root of the sample variance to find the sample standard deviation.

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Key Concepts
Here are the essential concepts you must grasp in order to answer the question correctly.
Sum of Squares (SS)
The Sum of Squares (SS) is a measure used in statistics to quantify the total variation within a dataset. It is calculated by summing the squared differences between each data point and the mean of the dataset. This value is crucial for determining variance and standard deviation, as it reflects how much the data points deviate from the average.
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Sample Variance
Sample variance is a statistic that measures the dispersion of a sample dataset. It is calculated by dividing the Sum of Squares (SS) by the number of observations minus one (n - 1), which corrects for bias in the estimation of the population variance. This adjustment is important because it provides a more accurate representation of variability when working with a sample rather than the entire population.
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Standard Deviation
Standard deviation is a widely used measure of the amount of variation or dispersion in a set of values. It is the square root of the variance, providing a measure that is in the same units as the original data. A low standard deviation indicates that the data points tend to be close to the mean, while a high standard deviation indicates greater spread among the values.
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