Contents
Is used to comparing the variability?
Unlike the previous measures of variability, the variance includes all values in the calculation by comparing each value to the mean. To calculate this statistic, you calculate a set of squared differences between the data points and the mean, sum them, and then divide by the number of observations.
What is comparison of variance?
Bartlett’s test can be used to compare two or more variances. This test is sensitive to the normality of the data. In other words, if the hypothesis of normality of the data seems fragile, it is better to use Levene’s or Fisher’s test. Bartlett’s statistic follows a Chi-square distribution with one degree of freedom.
What is the variability within groups due to?
the variability between your groups is equal to the variability due to chance, individual differences, etc. what is the Sum of Squares (SS) equal to? what is the Mean Square (MS)?
What is variance between groups?
This measures the interaction between the groups or samples. If the group means don’t differ greatly from each other and the grand mean, the SS(B) will be small. Note that for k groups, there will be k-1 degrees of freedom. The between groups variance is the variation, or SS(B), divided by its degree of freedom.
How to statistically compare two time series?
This common model could be estimated globally and separately for each of the two series and then one could construct an F test to test the hypothesis of a common set of parameters. Consider the grangertest () in the lmtest library. It is a test to see if one time series is useful in forecasting another. Just came across this.
Which is a measure of volatility for a time series?
This can be a measure for a single time series or a relative measure comparing multiple time series together. Let’s assume a Dickey-Fuller test has already been conducted, and all the time series do not have a unit root.
Which is the best metric for time series data?
Hyndman’s contention is that his MASE metric is optimal for time series data. MASE is a normalized loss function. After creating train and test data, the test data residuals are normalized or divided by the average error in the training data.
Is there a good correlation between two series?
If the r is small your conclusion would be that they are weakly related and so no desirable comparisons and a larger value if r would suggest good comparisons s between the two series. The third step where there is good correlation is to test the statistical significance of the r.