Data Analysis with JASP
New | Created by Sebastian Pintea | Last updated 11/2021
Duration: 3h 7m | 6 sections | 21 lectures | Video: 1280x720, 44 KHz | 1.1 GB
Genre: eLearning | Language: English + Sub
From basic to advanced procedures

What you'll learn
To handle databases and results in JASP
To perform data analysis with JASP, from basic to advanced
To construct an validate complex predictive models with JASP
To find patterns in the data with JASP
Requirements
Basic knowledge of arithmetic and algebra
Basic knowledge of statistics is an advantage
Description
JASP is a free multi-platform open-source statistics package, developed and continually updated by a group of researchers at the University of Amsterdam. It has a simple drag and drop interface, easy access menus, intuitive analysis with real-time computation, and a display of all results. JASP can perform from basic data analysis (descriptive statistics such as central tendency, dispersion, histograms, scatter plots, box-plots, raincloud plots, comparisons between groups and between measures such as t-test, Analysis of Variance, non-parametric tests such as Mann-Whitney, Wilcoxon, Kruskal-Wallis, Friedman, parametric and non-parametric correlations, simple and multiple regressions, linear mixed models, generalized linear mixed models) to more advanced data analysis such as factorial analysis, networks analysis, meta-analysis, structural equation modeling, visual modeling, machine learning and many more.

I think that everybody who is interested in data analysis deserves to know about JASP and to learn how to use it.

I invite you to join me on this learning journey. At this moment, there are several analytical modules from JASP for which I have already generated learning tutorials, but this is an ongoing process, and this course is growing from one week to another.

I need a companion on this journey. So what are you waiting for? Let's go!

Who this course is for:
Students from a large diversity of sciences (data science, engineering, sociology, psychology, anthropology, economics, marketing, business etc))
Managers and entrepreneurs who want to gain valuable information by analyzing data in their domains
Professionals who want to take a big step further in their careers by adding new cognitive tools to their belt
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