The mean would be negative, but standard deviation is always positive.
Subtracting a constant value from each data point in a dataset does not affect the standard deviation. The standard deviation measures the spread of the values relative to their mean, and since the relative distances between the data points remain unchanged, the standard deviation remains the same. Therefore, the standard deviation of the resulting data set will still be 3.5.
The standard deviation itself is a measure of variability or dispersion within a dataset, not a value that can be directly assigned to a single number like 2.5. If you have a dataset where 2.5 is a data point, you would need the entire dataset to calculate the standard deviation. However, if you are referring to a dataset where 2.5 is the mean and all values are the same (for example, all values are 2.5), then the standard deviation would be 0, since there is no variability.
The standard deviation of a single value, such as 34, is not defined in the traditional sense because standard deviation measures the spread of a set of data points around their mean. If you have a dataset that consists solely of the number 34, the standard deviation would be 0, since there is no variation. However, if you're referring to a dataset that includes 34 along with other values, the standard deviation would depend on the entire dataset.
Standard deviation is a number and you would divide it in exactly the same way as you would divide any other number!
A population standard deviation of 1 indicates that the data points in the population tend to deviate from the mean by an average of 1 unit. It reflects the degree of variation or dispersion within the dataset; a smaller standard deviation would suggest that the data points are closer to the mean, while a larger one would indicate more spread out values. In practical terms, if the population's values are measured in a certain unit, most of the data will fall within one unit above or below the mean.
Yes. For this to happen, the values would all have to be the same.
The standard deviation itself is a measure of variability or dispersion within a dataset, not a value that can be directly assigned to a single number like 2.5. If you have a dataset where 2.5 is a data point, you would need the entire dataset to calculate the standard deviation. However, if you are referring to a dataset where 2.5 is the mean and all values are the same (for example, all values are 2.5), then the standard deviation would be 0, since there is no variability.
The standard deviation of a single value, such as 34, is not defined in the traditional sense because standard deviation measures the spread of a set of data points around their mean. If you have a dataset that consists solely of the number 34, the standard deviation would be 0, since there is no variation. However, if you're referring to a dataset that includes 34 along with other values, the standard deviation would depend on the entire dataset.
Your middle point or line for the plot (mean) would be 6.375. Then you would add/subtract 1.47 from your mean. For example, one standard deviation would equal 6.375 + 1.47 and one standard deviation from the left would be 6.375 - 1.47
To determine the standard deviation of a portfolio, you would need to calculate the weighted average of the individual asset standard deviations and their correlations. This involves multiplying the squared weight of each asset by its standard deviation, adding these values together, and then taking the square root of the result. This calculation helps measure the overall risk and volatility of the portfolio.
The first set would have most data points very close to 50 while in the second set they would be much further away.
It would be 3*5 = 15.
Standard deviation is a number and you would divide it in exactly the same way as you would divide any other number!
A large standard deviation means that the data were spread out. It is relative whether or not you consider a standard deviation to be "large" or not, but a larger standard deviation always means that the data is more spread out than a smaller one. For example, if the mean was 60, and the standard deviation was 1, then this is a small standard deviation. The data is not spread out and a score of 74 or 43 would be highly unlikely, almost impossible. However, if the mean was 60 and the standard deviation was 20, then this would be a large standard deviation. The data is spread out more and a score of 74 or 43 wouldn't be odd or unusual at all.
A population standard deviation of 1 indicates that the data points in the population tend to deviate from the mean by an average of 1 unit. It reflects the degree of variation or dispersion within the dataset; a smaller standard deviation would suggest that the data points are closer to the mean, while a larger one would indicate more spread out values. In practical terms, if the population's values are measured in a certain unit, most of the data will fall within one unit above or below the mean.
This would increase the mean by 6 points but would not change the standard deviation.
A standard deviation of zero means that all the data points are the same value.