https://github.com/rmodi6/statistical-analysis-using-r
Conduct Statistical Significance testing using ANOVA, MANOVA and T-Test in R programming language
https://github.com/rmodi6/statistical-analysis-using-r
anova human-computer-interaction manova pairwise-testing r significance-testing t-tests
Last synced: about 1 year ago
JSON representation
Conduct Statistical Significance testing using ANOVA, MANOVA and T-Test in R programming language
- Host: GitHub
- URL: https://github.com/rmodi6/statistical-analysis-using-r
- Owner: rmodi6
- License: mit
- Created: 2019-11-10T00:26:05.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-12-13T23:31:18.000Z (about 6 years ago)
- Last Synced: 2025-01-11T08:08:57.637Z (about 1 year ago)
- Topics: anova, human-computer-interaction, manova, pairwise-testing, r, significance-testing, t-tests
- Language: R
- Size: 67.4 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# statistical-analysis-using-R
Conduct Statistical Significance testing using ANOVA, MANOVA and T-Test in R programming language on 2 sample datasets.
Additional information regarding the experiment related to the datasets can be found in the following [paper](https://dl.acm.org/citation.cfm?id=1096742).
> François Guimbretière, Andrew Martin, and Terry Winograd, Benefits of Merging Command Selection and Direct Manipulation.
> Transactions on Human-Computer Interaction, 12(3), pp 460 – 476, 2005
The goal of the project is to carry out statistical significance tests to analyse the effect of independent variable menu on
dependent variables time and error i.e. to figure out if menu types have a significant effect on time and error. Menu types are 4 different types of menu (controlmenu, flowmenu, toolpalette and toolglass) that were tested on a group of users and their
time to use the menu as well as any errors while using the menu were noted. Conducting significance tests like ANOVA, MANOVA
and T-Tests on this dataset helps us to determine if menu type has a significant effect on a variable and which menu type has
the most significant effect. Also perform visualizations to understand the results better.