In the business world, "normalization" typically means that the range of values are "normalized to be from 0.0 to 1.0". "Standardization" typically means that the range of values are "standardized" to measure how many standard deviations the value is from its mean.
The more conventional terms are standardized (to achieve a mean of zero and SD of one) and normalized (to bring the range to the interval $ [0,1]$ or to rescale a vector norm to $1$).
I'm doing principal component analysis on my dataset and my professor told me that I should normalize the data before doing the analysis. Why? What would happen If I did PCA without normalization? ...
For non-negative economic quantities like sales and costs where spread might tend to be proportional to mean, it's often sensible to look at coefficient of variation, which is sd/mean. CV's are dimensionless (it doesn't matter if you measured in dollars or millions of dollars, nothing changes for CV). The above link gives some advantages and disadvantages. Sums of terms will tend to have lower ...
But while I was building my own artificial neural networks, I needed to transform the normalized output back to the original data to get good readable output for the graph.
Under what circumstances should the data be normalized/standardized when building a regression model. When i asked this question to a stats major, he gave me an ambiguous answer "depends on the dat...
9 I'm reading a scientific paper where they plot the variance of particle intensity normalized by the mean of particle intensity. I'm a bit confused and don't have an intuition for how this should be helping me. I'm used to seeing standard deviation and variance, both of which reflect dispersion.
I am trying to find the best-fit model from my observation and model predicated data. I came across these two different approach which have been used in the literature: Normalized Root Mean Square ...
Fold-change (or percentage change) is a perfectly reasonable way to want to interpret data, but indeed, just normalizing as you have done creates the issue you've noticed. It's actually worse than just visual interpretation - if you have a model that assumes additive errors, normalizing as you've done causes the errors to become multiplicative. This makes interpretation and statistics much ...
I am trying to adjust a hierarchical multiple regression model and no matter which transformations I use (z-transformation, sqrt, cuberoot, inv, inv sqrt ...), I do not manage to get the residuals
