# Comments

1. The selection of ranges (Input Range and Output Range) would be made easier if one could use the standard keyboard sequences (Shift, End, Arrow-Down, End, Arrow-Right to select a whole block).

2. The Casewise PCA scores do not equal the results of the calculation shown in the Help file. They are de-meaned; maybe that should be made clear.

More generally, have you thought about adding some time series capabilities to the product?

## Comments

I will check out the PCA calculations that you mention and update the Help File accordingly ... thanks for the pointer.

We have thought about time series stuff and realise that it would be a valuable addition to statistiXL. We currently have a number of other additions that we are working on, however, and don't have any ETA for time series functions just yet.

Cheers

Alan Roberts

statistiXL

Thanks

Alan Roberts

statistiXL

It is standard for PCA analyses to have the scores output in standardised form (to a mean of 0), for the following reason.

For PCA based on the correlation matrix, the raw data columns are standardised for the mean (to 0) and standard deviation (to 1) prior to analysis, to account for differences in scale of the different variables. It therefore makes sense to output the standardised PCA scores (to column means of 0) because the data have already been standardised.

For PCA based on the covariance matrix, where the raw data are NOT required to be standardised for the mean and standard deviation, it might make less sense to output the standardised PCA scores (to a mean of 0) because the data do not have to be standardised for analysis. However, it seems that for mathematical reasons (convenience of matrix calculation?) that the raw scores are first standardised to a mean of 0 before the PCA analysis, and the PCA scores are output standardised to a mean of 0 . Note that when using the covariance matrix, the raw scores are NOT standardised to a standard deviation of 1. So, you get different results from statistiXL if you use the raw data or standardised data for the covariance approach (but you get exactly the same results for the correlation approach because the raw data have been standardised for both mean and SD).

In statistiXL, we also provide the option of standardising the PCA scores to a standard deviation of 1, as an option in the dialog box. Having the PCA scores standardised to a mean of 0, and potentially to a SD of 1, can be useful when further analysing the PCA output (e.g. in a MANOVA).