Financial modelling terms explained

Variance

Variance is an accounting term that refers to the difference between the actual costs incurred in production and the budgeted costs. The variance indicates the deviation that has occurred between the planned and the actual cost incurred.

What Is Variance?

In statistics, variance is a measure of the dispersion of a set of data values. It is calculated as the average squared deviation of the data values from their mean. Variance is expressed as the square of the standard deviation.

How Is Variance Different to Variability?

Variance and variability are both measures of dispersion or spread, but they are calculated in different ways and have different properties. Variance is calculated as the average squared deviation from the mean, while variability is calculated as the standard deviation. Variance is a more powerful measure than variability, because it is not affected by the size of the sample. It is also a more stable measure, because it is not affected by the presence of outliers.

What Is the Difference Between Variance and Standard Deviation?

Variance is a measure of how spread out a set of data points are. Standard deviation is variance's more commonly used cousin, and is a measure of how much a set of data points deviates from the average. In other words, standard deviation is the average distance a data point is from the mean.

What Is the Difference Between Variance and Coefficient of Variation?

Variance is the measure of how spread out a set of data points are. It is calculated by taking the difference of each data point from the mean, squaring them, and then dividing by the number of data points. Coefficient of variation is a measure of how spread out a data set is relative to its mean. It is calculated by taking the variance and dividing it by the mean.

How Do You Calculate Variance?

Variance is a measure of how dispersed a set of data points are around their mean value. It is calculated by taking the square of the standard deviation of the data set. The standard deviation measures the degree of variation in a set of data points, and is calculated by taking the average of the squared distances of each data point from the mean.

What Do You Have to Watch out for When You're Calculating Variance?

When calculating variance, one has to be aware of the difference between population variance and sample variance. The population variance is the variance of all the values in the population, while the sample variance is the variance of a sample of the population. The sample variance is always smaller than the population variance, because it is only based on a subset of the population.

When calculating variance, one also has to be aware of the Central Limit Theorem. The Central Limit Theorem states that the sample variance is approximately equal to the population variance, as the sample size increases. This approximation becomes more accurate as the sample size increases.

What Are the Uses of Variance?

Variance is a measure of the dispersion of a set of data points from their mean. It is calculated by taking the difference of each data point from the mean, squaring them, and then dividing by the number of data points. This gives a measure of how spread out the data is.

Variance is used in financial modelling to measure the risk of a portfolio. It can be used to calculate the standard deviation, which is a measure of how volatile the portfolio is. This can help to determine how much risk the portfolio is exposed to and whether it is within the acceptable range.

What Are the Limitations of Variance?

There are a few limitations to variance that should be noted. First, variance is only able to measure the variability of a set of data points around their mean. This means that it can't be used to measure the variability of data points that are not normally distributed. Second, variance can be affected by outliers in a set of data. If there are a few unusually high or low data points, they can skew the results and give a false impression of the variability of the data. Finally, variance is not always a good indicator of risk. For example, a set of data with a high variance may not be as risky as a set of data with a low variance if the data points are all clustered around the same value.

Get started today with Causal

Start building your own custom financial models, in minutes not days.