Martin Mölder
Principal component analysis (PCA)

Vali tegevus
Klipi teostus: UTTV 31.01.2020 4578 vaatamist


„Data Science and Increasing the Value of Data for Social Scientists“
"SHRG.03.024 Quantitative Methods (6 ECTS)"

Seotud videod

Martin Mölder
Introduction
31.01.20
Martin Mölder
Working with R and Rstudio
31.01.20
Martin Mölder
Data and types of data objects R
31.01.20
Martin Mölder
Loading and saving
31.01.20
Martin Mölder
Preparing data for analysis
31.01.20
Martin Mölder
Ggplot and basic plots
31.01.20
Martin Mölder
Modifying plots
31.01.20
Martin Mölder
Hypothesis testing
31.01.20
Martin Mölder
Comparing means 
31.01.20
Martin Mölder
Crosstables
31.01.20
Martin Mölder
Correlation
31.01.20
Martin Mölder
Regression
31.01.20
Martin Mölder
Regression with dummy variables
31.01.20
Martin Mölder
Regression and non-linear associations
31.01.20
Martin Mölder
Interactions in linear regression
31.01.20
Martin Mölder
Plots and regression tables
31.01.20
Martin Mölder
Logistic regression
31.01.20
Martin Mölder
Model fit for logistic regression
31.01.20
Martin Mölder
Interpreting and presenting logistic regression results
31.01.20
Martin Mölder
Factor analysis
31.01.20
Martin Mölder
Web scraping
31.01.20
Martin Mölder
Textual data in R
31.01.20
Martin Mölder
Sentiment analysis
31.01.20
Martin Mölder
Text scaling
31.01.20
Martin Mölder
Topic modelling
31.01.20
Dmytro Fishman
Overview of Machine Learning
31.01.20
Dmytro Fishman
Supervised Learning: Classification
31.01.20
Dmytro Fishman
Supervised Learning: Regression
31.01.20
Martin Mölder
Shiny applications
31.01.20
Martin Mölder
Andmekaeve internetist
29.07.21