key term - Standard Deviation

Definition

Standard deviation is a statistical measure that quantifies the amount of variation or dispersion of a set of data values from the mean or average value. It is a fundamental concept in probability theory and statistics, used to understand the spread and distribution of data points.

5 Must Know Facts For Your Next Test

1. Standard deviation is calculated by taking the square root of the variance, which is the average of the squared differences from the mean.
2. A higher standard deviation indicates a greater spread or dispersion of the data points, while a lower standard deviation indicates a tighter clustering around the mean.
3. In a normal distribution, approximately 68% of the data points fall within one standard deviation of the mean, 95% fall within two standard deviations, and 99.7% fall within three standard deviations.
4. Standard deviation is an important measure in financial analysis, as it helps to quantify the risk or volatility of an investment or portfolio.
5. Standard deviation is also used in quality control and process improvement, as it helps to identify and manage sources of variation in a manufacturing process.

Review Questions

• Explain how standard deviation is calculated and its relationship to variance.
  • Standard deviation is calculated by taking the square root of the variance. Variance is a measure of the average squared deviation from the mean, calculated by summing the squared differences between each data point and the mean, and dividing by the number of data points. Standard deviation is the square root of this variance, and provides a measure of the spread or dispersion of the data around the mean. A higher standard deviation indicates a greater spread of the data, while a lower standard deviation indicates a tighter clustering around the mean.
• Describe the relationship between standard deviation and the normal distribution.
  • In a normal distribution, the standard deviation is a critical measure that helps to understand the spread and distribution of the data. Approximately 68% of the data points fall within one standard deviation of the mean, 95% fall within two standard deviations, and 99.7% fall within three standard deviations. This relationship is known as the 68-95-99.7 rule, and is a fundamental concept in understanding the characteristics of a normal distribution and the likelihood of data points falling within certain ranges.
• Analyze the importance of standard deviation in financial analysis and quality control.
  • Standard deviation is a crucial measure in financial analysis, as it helps to quantify the risk or volatility of an investment or portfolio. A higher standard deviation indicates greater risk, as it means the data points are more spread out from the mean, making the investment or portfolio more volatile. In quality control and process improvement, standard deviation is used to identify and manage sources of variation in a manufacturing process. By understanding the standard deviation of a process, manufacturers can make adjustments to reduce variability and improve the consistency and quality of their products.

Related terms

Variance: Variance is a measure of the average squared deviation from the mean, and is calculated by taking the sum of the squared differences between each data point and the mean, divided by the number of data points.

Normal Distribution: The normal distribution, also known as the Gaussian distribution, is a continuous probability distribution that has a bell-shaped curve, with the mean, median, and mode all located at the center of the distribution.

Z-score: A z-score, or standard score, is a measure of how many standard deviations a data point is from the mean of a distribution. It is calculated by subtracting the mean from the data point and dividing the result by the standard deviation.