Principal Component Analysis (PCA)
PCA is a data mining method that can be done using the XLSTAT software in Excel. It is a multivariate method that is used to examine multidimensional datasets that have quantitative variables. PCA is extensively used in a myriad of fields like marketing, biostatistics, sociology, etc. XLSTAT offers researchers a comprehensive and dynamic PCA feature that they can use to explore their data using Excel. This feature allows you to filter out variables, perform VARIMAX, use different criteria to optimize map readability, supplement variables or observations, etc.
Sometimes, submitting a large or small group of individuals for further analysis can be useful. Doing this can shorten the time you will spend on computation and also helps in the process of validation. If you know the distribution of your sample, it is easy to come up with a large or small set that is in line with the same distribution. XLSTAT can empirically estimate your theoretical distribution parameters from the dataset you have provided. Its distribution sampling module can generate random data while regarding a theoretical empirical distribution. Some of the distributions provided in XLSTAT include Arcsine, Beta, Binomial, Bernoulli, Chi-square, Gamma, Fisher, Erlang, Exponential, etc.
Coding By Ranks
The coding by ranks module in XLSTAT allows you to transform numerical data values with important order into ranks. It can be used to convert a continuous quantitative variables table into discrete quantitative variables. However, it should only be done if the order value and not the value themselves are important. You can also use the coding by ranks module if you want to limit the influence outliers have on your data
A Fourier Transform is a tool that transforms a real variable’s complex-valued function to another. It is usually used to convert a signal or time series to its Fourier coordinates. In other words, we can say that Fourier Transforms performs an inverse transformation. XLSTAT is preferred to Excel for this because it is not constrained to the powers of two.