Uploaded February 2026 | Updated September 2026, 12 hours ago
Principal Component Analysis (PCA) is a method that reduces the number of variables in a dataset by creating new variables (“principal components”) that are combinations of the original ones and capture the most variation in the data—often making the data easier to visualize, compress, or model.
► Principal Component Analysis Calculator
numiqo.com/statistics-calculator/factor-analysis/principal-component-analysis-calculator?example=pca_wine
► Example data
numiqo.com/statistics-calculator/factor-analysis/principal-component-analysis-calculator?example=pca_wine
► PCA Interactive
numiqo.com/lab/pca
► E-BOOK
numiqo.com/statistics-book
Principal Component Analysis (PCA) is a method that reduces the number of variables in a dataset by creating new variables (“principal components”) that are combinations of the original ones and capture the most variation in the data—often making the data easier to visualize, compress, or model.
► Principal Component Analysis Calculator
numiqo.com/statistics-calculator/factor-analysis/principal-component-analysis-calculator?example=pca_wine
► Example data
numiqo.com/statistics-calculator/factor-analysis/principal-component-analysis-calculator?example=pca_wine
► PCA Interactive
numiqo.com/lab/pca
► E-BOOK
numiqo.com/statistics-book
![Research Topic, Research Problem & Central Research Question [How to find and formulate]](https://i.ytimg.com/vi/b6HFvA_-v84/mqdefault.jpg "Research Topic, Research Problem & Central Research Question [How to find and formulate]
How do you find a suitable Research Topic? How can you formulate your research problem precisely? And what do you have to pay attention to so that you dont get stuck in the end? Well go over that in this video.
Linke to the Statistics Calculator:
https://numiqo.com/statistics-calculator/descriptive-statistics
Link to the Survey App
https://numiqo.com/survey/
00:00 Intro
00:31 Research Topic, Research Problem & Central Research Question
01:25 How to find a Research Topic
02:27 An easy way to find a Research Topic
03:11 Find Research Gap
04:25 From Research Topic to Research Problem
05:05 Example Research Topic and Research Problem
07:25 Central Research Question
08:22 Research Topic and Literature Research")
![Causality [Simply explained]](https://i.ytimg.com/vi/bm6V84Lgz2w/mqdefault.jpg "Causality [Simply explained]
In this video i will explain the similarities and differences between correlation, regression and causality. Causality means that there is a clear cause-effect relationship between two variables.
A common mistake in the interpretation of statistics is that when a correlation exists it is immediately assumed to be a causal relationship.
There are two prerequisites for causality:
First, there is a significant relationship, that is, a significant Correlation.
The second condition can be satisfied in two ways.
First, it is satisfied if there is a temporal ordering of the variables. So variable A was collected temporally before variable B.
Furthermore, the second condition can be fulfilled, if there is a theoretically founded and plausible theory in which direction the causal relationship goes.
If neither of the two is true, i.e. there is neither a temporal order nor can the causality be justified by a well-founded theory, then we can only speak of a relationship, but never of causality, i.e. it cannot be said that variable A influences variable B or vice versa.
More Information about Causality:
https://numiqo.com/tutorial/causality
Regression Analysis: An introduction to Linear and Logistic Regression
https://youtu.be/FLJ0yYetywE
Simple and Multiple Linear Regression
https://youtu.be/29rjWClT_3U
Assumptions of Linear Regression
https://youtu.be/sDrAoR17pNM
Logistic Regression: An Introduction
https://youtu.be/3tq4t41MsPc
Dummy Variables in Multiple Regression
https://youtu.be/bnjPzHQ04Ac
Regression with categorical independent variables
https://youtu.be/xVBwXqnWPyE
Multicollinearity
https://youtu.be/G1WX5GiFSWQ
Causality, Correlation and Regression
https://youtu.be/dhCnAO4UoiM")
