The variance is 27.0
The variance for 3 5 12 3 2 is: 16.5
There are 7 variances associated with a budget ( which are generally calculated for controlling purposes) 1- Material Price variance 2- Material Quantity variance 3- Labor rate variance 4- Labor efficiency variance 5- Spending variance 6- Efficiency variance 7- Capacity variance
Variance is std dev squared. Therefore, if std dev = 12.4, variance = 12.4^2 = 153.76.
The variance of 2 6 1 4 2 2 4 3 2 = 2.3611
You need to use the variance and covariance functions in Excel 1. Calculate the covariance of the stock returns with respect to an index 2. Calculate the variance of the index 3. Divide the first number by the second. See the related link for a spreadsheet
To efficiently calculate and visualize the plot covariance matrix in Python, you can use the NumPy library to calculate the covariance matrix and the Seaborn library to visualize it. First, import the necessary libraries: import numpy as np import seaborn as sns Next, calculate the covariance matrix using NumPy: data = np.random.rand(10, 2) # Example data cov_matrix = np.cov(data.T) Finally, visualize the covariance matrix using Seaborn: sns.heatmap(cov_matrix, annot=True, cmap='coolwarm', xticklabels=['Feature 1', 'Feature 2'], yticklabels=['Feature 1', 'Feature 2']) This will create a heatmap visualization of the covariance matrix with annotations showing the values.
See related link. You can use Excel, if you dataset is not too big. Generally, if I have a table of data, with n columns corresponding to n variables with N observations, I can calculate the covariance of columns a and b, using excel covar function, covar(range of first data values, range of second data values) To keep things organized, you may want to name the ranges of your columns and use them as the arguments in the covar.
[((.39)^2)*160 +((.61)^2)*340+2*.61*.39*190]^.5 = 15.5323
[N*(N-1)]/2 N=1700 (1700*1699)/2 = 1,444,150 Covariance
This years' sales plus last years' sales divided by 2
Variance = sigma((value - mean)2) / (# values - 1) Mean = (0+1+1+2)/4 = 1 Variance = ((0-1)2+(1-1)2+(1-1)2+(2-1)2)/(4-1) Variance = (1+0+0+1)/3 Variance = 2/3 Variance ~ 0.667
The variance of 2 3 5 12 = 20.3333
The variance is 27.0
var(X) = (xm/a - 1)2 a/a-2 . If a < or equal to 2, the variance does not exist.
Variance = (std dev) ^2 = 36^2 = 1296.
The variance for 3 5 12 3 2 is: 16.5