Uploaded October 2021 | Updated September 2026, 31 minutes ago
Exploratory factor analysis (EFA) is a method that aims to uncover structures in large variable sets. If you have a data set with many variables, it is possible that some of them are interrelated, i.e. correlate with each other. These correlations are the basis of factor analysis.
The aim of the factor analysis is to divide the variables into groups. The aim is to separate those variables that correlate highly from those that correlate less strongly.
In Statistics Exploratory Factor Analysis is also called Principal Component Analysis (PCA)
More Information about Exploratory Factor Analysis
numiqo.com/tutorial/exploratory-factor-analysis
Here you can find the Factor Analysis Calculator
numiqo.com/statistics-calculator/factor-analysis
And the Data:
numiqo.com/assets/examples/PCA_Example.xlsx
Exploratory factor analysis (EFA) is a method that aims to uncover structures in large variable sets. If you have a data set with many variables, it is possible that some of them are interrelated, i.e. correlate with each other. These correlations are the basis of factor analysis.
The aim of the factor analysis is to divide the variables into groups. The aim is to separate those variables that correlate highly from those that correlate less strongly.
In Statistics Exploratory Factor Analysis is also called Principal Component Analysis (PCA)
More Information about Exploratory Factor Analysis
numiqo.com/tutorial/exploratory-factor-analysis
Here you can find the Factor Analysis Calculator
numiqo.com/statistics-calculator/factor-analysis
And the Data:
numiqo.com/assets/examples/PCA_Example.xlsx
![Kendalls Tau [Easily explained]](https://i.ytimg.com/vi/Pm8KV5f3JM0/mqdefault.jpg "Kendalls Tau [Easily explained]
Kendalls tau is a correlation coefficient and is therefore a measure of the relationship between two variables.
But what is the difference between the Pearson correlation and the Spearman rank correlation? In contrast to the Pearson correlation, the Kendalls rank correlation is a non-parametric test procedure. For the calculation of Kendalls Tau, the data does not have to be normally distributed and the two variables only have to have an ordinal scale level. Kendalls Tau is very similar to Spearmans rank correlation coefficient. However, Kendalls Tau should be preferred over Spearmans correlation when there is very little data with many rank ties!
► Online calculator Kendalls Tau
https://numiqo.com/statistics-calculator/correlation
► Tutorial Kendalls Tau
https://numiqo.com/tutorial/kendalls-tau
► EBOOK
https://numiqo.com/statistics-book")
![Simple Linear Regression vs. Multiple Linear Regression [in 60 sec.] #shorts](https://i.ytimg.com/vi/QDIcHw5mEMo/mqdefault.jpg "Simple Linear Regression vs. Multiple Linear Regression [in 60 sec.] #shorts
Simple Linear Regression vs. Multiple Linear Regression
#statistics #datascience #dataanalysis #dataanalytics #research #student #thesis #stats #researcher #science #datatab #SPSS #machinelearning")
![Violin Plot [Simply explained]](https://i.ytimg.com/vi/Rw00VmP--qk/mqdefault.jpg "Violin Plot [Simply explained]
A violin plot is a method of plotting numeric data and can be understood as a combination of a box plot and a kernel density plot. It provides a visualization of data distribution.
But how do we interpret a violin plot?
Kernel Density Estimation:
This is the outer layer of the violin plot and displays the density of the data at different values. The width of the plot at different values indicates the density of the data: a wider section suggests a higher density (more data points), whereas a narrower section indicates lower density (fewer data points). The violin plot is typically symmetrical, meaning it mirrors the density estimation on either side of its axis.
Central Box Plot which is Optional:
Inside the violin, there is often a miniature box plot which provides additional details about the datas distribution. The central line in this box plot represents the median of the data, while the edges of the box represent the interquartile range, giving insights into the datas spread.
Whiskers which are Optional:
Like in a box plot, whiskers might extend from the box, indicating variability outside the upper and lower quartiles. They can provide a visual cue for identifying outliers.
► Make a Violin Plot online
https://numiqo.com/statistics-calculator/charts/violin-plot
► E-BOOK
https://numiqo.com/statistics-book")
