Principal Component Analysis (PCA) is a powerful tool when you have many variables and you want to look into things that these variables can explain.
In this website, I use R to show some examples of how you can run statistical tests. I assume you can install R in your machine, and you know ...
Missing: url | Show results with:url
Correspondence Analysis is a similar concept to Principal Component Analysis (PCA), so you can think that Correspondence Analysis is a categorical data version ...
Data transformation is a powerful tool when the data don't look like forming a normal distribution. The idea of data transformation is that you convert your ...
Missing: pca | Show results with:pca
Now, we are going to do a logistic regression. The format is very similar to linear regression, and you basically just need to use glm() function. First we use ...
Mar 27, 2016 · Reference materials that I have used are: http://yatani.jp/teaching/doku.php?id=hcistats:pca and https://www.youtube.com/watch?v= ...
Aug 14, 2014 · So, we hope that we can find a smaller number of new variables which explain your data well. In this sense, it sounds very similar to PCA.
Kruskal-Wallis is basically a non-parametric version of ANOVA. Thus, if you have the data which contain more than two groups to compare, and your data are ...
Missing: pca | Show results with:pca
jp/teaching/doku.php?id=hcistats:PCA. 이를 Sage ... url = 'https://media. ... Math for Big Data, Lecture 12, Principal Componant Analysis 1 (PCA), https://youtu.
Mar 14, 2016 · Principal Component Analysis (PCA) is a popular technique for reducing the size of a dataset. Let's assume that the dataset is structured ...
Missing: url | Show results with:url