Calculating the Sample Variance

Oct 13, 2025 by sorusgentrification.com 124 views

Introduction

In this article, we will explore the concept of computing the range, sample variance, and sample standard deviation from a given set of data. The data provided represents the cost of repair for four cars that were crashed in succession at a speed of 5 miles per hour. We will use this data to calculate the range, sample variance, and sample standard deviation.

Calculating the Range

The range is the difference between the highest and lowest values in a dataset. To calculate the range, we need to identify the highest and lowest values in the dataset.

Highest value: $\$$ 459
Lowest value: $\$$ 233

The range is calculated as follows:

Range = Highest value - Lowest value Range = $\$$ 459 - $\$$ 233 Range = $\$$ 226

Calculating the Sample Variance

The sample variance is a measure of the spread or dispersion of a dataset. It is calculated as the average of the squared differences from the mean.

First, we need to calculate the mean of the dataset.

Mean = (Sum of all values) / (Number of values) Mean = ( $\$$ 419 + $\$$ 459 + $\$$ 401 + $\$$ 233) / 4 Mean = $\$$ 1412 / 4 Mean = $\$$ 353

Next, we need to calculate the squared differences from the mean.

Squared differences: ( $\$$ 419 - $\$$ 353)^2 = $\$$ 66^2 = $\$$ 4356 ( $\$$ 459 - $\$$ 353)^2 = $\$$ 106^2 = $\$$ 11236 ( $\$$ 401 - $\$$ 353)^2 = $\$$ 48^2 = $\$$ 2304 ( $\$$ 233 - $\$$ 353)^2 = $\$$ -120^2 = $\$$ 14400

Now, we can calculate the sample variance.

Sample variance = (Sum of squared differences) / (Number of values - 1) Sample variance = ( $\$$ 4356 + $\$$ 11236 + $\$$ 2304 + $\$$ 14400) / 3 Sample variance = $\$$ 29696 / 3 Sample variance = $\$$ 9889.33

Calculating the Sample Standard Deviation

The sample standard deviation is the square root of the sample variance.

Sample standard deviation = √(Sample variance) Sample standard deviation = √( $\$$ 9889.33) Sample standard deviation = $\$$ 99.45

Conclusion

In this article, we calculated the range, sample variance, and sample standard deviation from a given set of data. The range was calculated as the difference between the highest and lowest values in the dataset, which was $\$$ 226. The sample variance was calculated as the average of the squared differences from the mean, which was $\$$ 9889.33. Finally, the sample standard deviation was calculated as the square root of the sample variance, which was $\$$ 99.45.

Discussion

The sample variance and sample standard deviation are important measures of the spread or dispersion of a dataset. They can be used to understand the variability of a dataset and to make inferences about the population from which the sample was drawn. In this case, the sample variance and sample standard deviation provide insight into the cost of repair for the four cars that were crashed in succession at a speed of 5 miles per hour.

References

[1] "Statistics for Dummies" by Deborah J. Rumsey
[2] "Mathematics for Dummies" by Mary Jane Sterling
[3] "Statistics and Probability with Technology" by Jim Hastings

Future Work

In future work, we can explore other statistical concepts and techniques, such as hypothesis testing and confidence intervals. We can also apply these concepts and techniques to real-world problems and datasets.

Limitations

One limitation of this article is that it only calculates the range, sample variance, and sample standard deviation from a small dataset. In practice, we would typically work with larger datasets and more complex statistical models. Another limitation is that this article assumes that the data is normally distributed, which may not always be the case in real-world datasets.

Conclusion

In conclusion, this article provides a step-by-step guide to calculating the range, sample variance, and sample standard deviation from a given set of data. The range was calculated as the difference between the highest and lowest values in the dataset, the sample variance was calculated as the average of the squared differences from the mean, and the sample standard deviation was calculated as the square root of the sample variance. These measures of spread or dispersion are important in statistics and can be used to understand the variability of a dataset and to make inferences about the population from which the sample was drawn.

Introduction

In our previous article, we explored the concept of calculating the range, sample variance, and sample standard deviation from a given set of data. We used a dataset representing the cost of repair for four cars that were crashed in succession at a speed of 5 miles per hour. In this article, we will answer some frequently asked questions (FAQs) related to calculating the range, sample variance, and sample standard deviation.

Q: What is the range, and how is it calculated?

A: The range is the difference between the highest and lowest values in a dataset. It is calculated by subtracting the lowest value from the highest value.

Example: If the highest value is $\$$ 459 and the lowest value is $\$$ 233, the range would be $\$$ 226.

Q: What is the sample variance, and how is it calculated?

A: The sample variance is a measure of the spread or dispersion of a dataset. It is calculated as the average of the squared differences from the mean.

Example: If the mean is $\$$ 353 and the squared differences are $\$$ 4356, $\$$ 11236, $\$$ 2304, and $\$$ 14400, the sample variance would be ( $\$$ 4356 + $\$$ 11236 + $\$$ 2304 + $\$$ 14400) / 3 = $\$$ 9889.33.

Q: What is the sample standard deviation, and how is it calculated?

A: The sample standard deviation is the square root of the sample variance.

Example: If the sample variance is $\$$ 9889.33, the sample standard deviation would be √( $\$$ 9889.33) = $\$$ 99.45.

Q: Why is it important to calculate the range, sample variance, and sample standard deviation?

A: These measures of spread or dispersion are important in statistics because they can be used to understand the variability of a dataset and to make inferences about the population from which the sample was drawn.

Q: What are some common applications of the range, sample variance, and sample standard deviation?

A: These measures of spread or dispersion have many applications in statistics, including:

Understanding the variability of a dataset
Making inferences about the population from which the sample was drawn
Comparing the spread of different datasets
Identifying outliers in a dataset

Q: What are some common mistakes to avoid when calculating the range, sample variance, and sample standard deviation?

A: Some common mistakes to avoid when calculating these measures of spread or dispersion include:

Not checking for outliers in the dataset
Not using the correct formula for calculating the sample variance and sample standard deviation
Not using the correct units for the data (e.g., using dollars instead of cents)

Q: How can I use the range, sample variance, and sample standard deviation in real-world applications?

A: These measures of spread or dispersion can be used in a variety of real-world applications, including:

Understanding the cost of repair for cars
Analyzing the spread of exam scores
Comparing the spread of different datasets
Identifying outliers in a dataset

Conclusion

In this article, we answered some frequently asked questions (FAQs) related to calculating the range, sample variance, and sample standard deviation. We hope that this article has provided you with a better understanding of these measures of spread or dispersion and how they can be used in real-world applications.

References

[1] "Statistics for Dummies" by Deborah J. Rumsey
[2] "Mathematics for Dummies" by Mary Jane Sterling
[3] "Statistics and Probability with Technology" by Jim Hastings

Future Work

Limitations

One limitation of this article is that it only answers a few frequently asked questions (FAQs) related to calculating the range, sample variance, and sample standard deviation. In practice, we would typically work with larger datasets and more complex statistical models. Another limitation is that this article assumes that the data is normally distributed, which may not always be the case in real-world datasets.

Conclusion

In conclusion, this article provides a Q&A guide to calculating the range, sample variance, and sample standard deviation. We hope that this article has provided you with a better understanding of these measures of spread or dispersion and how they can be used in real-world applications.