A sample of size 100.
Standard deviation is the square root of the variance. Since you stated the variance is 4, the standard deviation is 2.
See the related links on how to calculate standard deviation. If there are more than a dozen data points, it is tedious to calculate by hand. Use excel or an online calculator. To get 2 standard deviations, multiply the calculated std deviation by 2.
The z score is (1650-1500)/150 = 150/150 = 1
The standard deviation provides in indication of what proportion of the entire distribution of the sample falls within a certain distance from the mean or average for that sample. If your data falls on a normal (or bell shaped) distribution, a SD of 1 indicates that about 68% of your data points (scores or whatever else) fall within 1 point (plus or minus) of the average (mean) of the data, and 95% fall within 2 points.
Variance is standard deviation squared. If standard deviation can be zero then the variance can obviously be zero because zero squared is still zero. The standard deviation is equal to the sum of the squares of each data point in your data set minus the mean, all that over n. The idea is that if all of your data points are the same then the mean will be the same as every data point. If the mean is the equal to every data point then the square of each point minus the mean would be zero. All of the squared values added up would still be zero. And zero divided by n is still zero. In this case the standard deviation would be zero. Short story short: if all of the points in a data set are equal than the variance will be zero. Yes the variance can be zero.
The standard deviation is a measure of how spread out the numbers are. Three points is needed to calculate a statistically valid meaningful standard deviation.
To calculate the standard deviation of a portfolio in Excel, you can use the STDEV.P function. This function calculates the standard deviation based on the entire population of data points in your portfolio. Simply input the range of values representing the returns of your portfolio into the function to get the standard deviation.
No, standard deviation is not a point in a distribution; rather, it is a measure of the dispersion or spread of data points around the mean. It quantifies how much individual data points typically deviate from the mean value. A lower standard deviation indicates that the data points are closer to the mean, while a higher standard deviation indicates greater variability.
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.
Standard deviation is the square root of the variance. Since you stated the variance is 4, the standard deviation is 2.
No. A small standard deviation with a large mean will yield points further from the mean than a large standard deviation of a small mean. Standard deviation is best thought of as spread or dispersion.
The lowest value that standard deviation can be is zero. This occurs when all the data points in a dataset are identical, meaning there is no variation among them. In such cases, the standard deviation, which measures the dispersion of data points around the mean, indicates that there is no spread.
It is 15 points.
This would increase the mean by 6 points but would not change the standard deviation.
The ideal value of standard deviation depends on the context and the nature of the data being analyzed. In general, a lower standard deviation indicates that the data points are closer to the mean, suggesting less variability. Conversely, a higher standard deviation indicates greater dispersion among the data points. Ultimately, the "ideal" standard deviation varies based on the goals of the analysis and the specific characteristics of the dataset.
A standard deviation of zero means that all the data points are the same value.
Yes, outliers can significantly affect the standard deviation. Since standard deviation measures the dispersion of data points from the mean, the presence of an outlier can increase the overall variability, leading to a higher standard deviation. This can distort the true representation of the data's spread and may not accurately reflect the typical data points in the dataset.