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Your question is a bit difficult to answer, as "succinct" is usually a quality in reference to a description or explanation. It is defined by Webster's dictionary as "marked by compact precise expression without wasted words." See related link.
For this reason, I have reworded your question as follows: Does the variance fully describe or summarize the raw data? The answer is no.
For any set of data, many statistical measures can be calculated, including the mean and variance. The variance or more commonly the square of the variance (standard deviation) is a very useful in identifying the dispersion of data, but is incomplete in fully describing the data. The mean is also important. Graphs can improve the summarization of data in a more visual manner.
What else can I help you with?
What is the purpose of calculating the mean and the variance?
Calculating the mean helps to understand the central tendency of a data set, while calculating the variance provides information about the spread or dispersion of the data points around the mean. Together, the mean and variance provide a summary of the data distribution, enabling comparisons and making statistical inferences.
Is the standard deviation of the data values in a sample is 17 what is the variance of the data values?
The variance of a set of data values is the square of the standard deviation. If the standard deviation is 17, the variance can be calculated as (17^2), which equals 289. Therefore, the variance of the data values in the sample is 289.
How do you get a variance?
To calculate variance, first find the mean (average) of your data set. Then, subtract the mean from each data point and square the result to eliminate negative values. Next, sum these squared differences and divide by the number of data points (for population variance) or by the number of data points minus one (for sample variance). This final result is the variance, which measures the spread of the data points around the mean.
Does variance and standard deviation assume nominal data?
No. Variance and standard deviation are dependent on, but calculated irrespective of the data. You do, of course, have to have some variation, otherwise, the variance and standard deviation will be zero.
What is the variance of this data set 10 8 8 6 5?
The variance is: 3.8
How do you correct Variance?
To correct variance, you can employ techniques such as transforming data, removing outliers, or applying regularization methods. Standardization (z-score normalization) can also help to stabilize variance across different features. Additionally, using robust statistical methods that are less sensitive to outliers can provide a more accurate estimation of variance. Ultimately, the choice of correction method depends on the specific context and nature of the data.
How do you calculate variances in a data set?
To calculate the variance of a data set, first determine the mean (average) of the data. Then, subtract the mean from each data point to find the deviation of each point, square these deviations, and sum them up. Finally, divide this total by the number of data points (for population variance) or by the number of data points minus one (for sample variance) to obtain the variance. This gives you a measure of how spread out the data points are from the mean.
Statistical term that describes the amount of variation in data?
The statistical term that describes the amount of variation in data is "variance." Variance quantifies how much individual data points differ from the mean of the dataset, indicating the spread of the data. A higher variance signifies greater dispersion among the data points, while a lower variance indicates that the data points are closer to the mean. Another related measure is the standard deviation, which is the square root of the variance and provides a more interpretable scale of variability.
The mean is 10 What is the variance of the data set 4 9 9 13?
The variance is 13.5833
What is the variance with sample data set 9 14 19 16 8 6 17 11 18?
The variance of this data set is 22.611
What is the significance of variance in statistics?
Variance is a measure of "relative to the mean, how far away does the other data fall" - it is a measure of dispersion. A high variance would indicate that your data is very much spread out over a large area (random), whereas a low variance would indicate that all your data is very similar.Standard deviation (the square root of the variance) is a measure of "on average, how far away does the data fall from the mean". It can be interpreted in a similar way to the variance, but since it is square rooted, it is less susceptible to outliers.
How many cases are needed for analysis of variance?
There only needs to be one data point to calculate variance.
