Statistical Methods for Data Analysis | Research Techniques & Applications
“The sexy job in the next 10 years will be statisticians!” – Hal Varian, chief economist at Google, could not have been more correct when he said this sentence in the early 2000s.
The knowledge about statistical methods for the analysis of large data sets is becoming more and more important for a modern curriculum vitae.
On statisticsglobe.com, you can learn how to use the techniques that are currently up to date in the research fields of statistics and data science – and even more important – how to apply these methods with modern statistical software such as R or Python.
List of Statistics Tutorials
In the following, you can find a list of statistics and data science tutorials that I have published on statisticsglobe.com. At the moment, the tutorials are mainly covering the handling of missing data and related topics. However, in the near future I will add further topics to the list. In the tutorials, I am explaining the theoretical concepts and show some practical applications for the different methods.
Imputation Methods (Top 5 Popularity Ranking)
Which technique for the handling of my missing values should I use? A question that probably almost every data user already had...
Typical answer: You have to use missing data imputation...
Listwise Deletion for Missing Data (Is Complete Case Analysis Legit?)
Listwise deletion (also known as casewise deletion or complete case analysis) removes all observations from your data, which have a missing value in one...
Mean Imputation for Missing Data (Example in R & SPSS)
Let's be very clear on this: Mean imputation is awful!
Do you think about using mean imputation yourself? Stop it NOW!
Sorry for the drama, but you will find out soon, why...
Missing Value Imputation (Statistics) - How To Impute Incomplete Data
It’s an issue every data user knows:
Missing data occur in almost every data set and can lead to serious problems such as biased estimates or less efficiency due t...
Missing Values - Statistical Analysis & Handling of Incomplete Data
Missing Data Definition:
Missing data (or missing values) appear when no value is available in one or more variables of an individual.
Mode Imputation (How to Impute Categorical Variables Using R)
Mode imputation is easy to apply - but using it the wrong way might screw the quality of your data.
In the following article, I'm going to show you how and when to use mode...
Predictive Mean Matching Imputation (Theory & Example in R)
Predictive mean matching is the new gold standard of imputation methodology !
Forget about all these outdated and crappy methods such as mean substitution or regression imputation...
Regression Imputation (Stochastic vs. Deterministic & R Example)
Be careful: Flawed imputations can heavily reduce the quality of your data! Are you aware that a poor missing value imputation might destroy the correlations between your...
The Most Important Methods in Statistics & Data Science
Admittedly, the list of available statistical methods is huge. As a beginner, it therefore makes sense to learn some of the most important techniques first and the move on from there.
If you want to get a first overview about some of the most important statistical concepts, I can recommend the following video tutorial of the YouTube channel The Doctoral Journey. The speaker, Dr. Amanda J. Rockinson-Szapkiw, is explaining some basic descriptive and inferential methods.
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