Skip to content

beapdk/PelletierDeKoninck-B-QLSC612

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

34 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

QLSC612 assignement for Brainhack school 2020

PelletierDeKoninck-B-QLSC612

This assignment holds the purpose of demonstrating how researchers can (easily) produce false positives or inflated prediction rates via p-hacking. See complete description with practical-assignment.md file.

Installation requirements

  • Python *(this was based on 3.7.6 version and used via miniconda). For Python installation tutorials, refer to either conda or pip

  • Jupyter NoteBook *Installation documentation

To install the packages required via conda follow these instructions based on this Documentation. For installing packages rather via pip you can refer to this instead.

For pandas : conda install pandas

(For a specific version install for any package via conda add =(version)), for example : conda install pandas=1.0.3

For scipy : conda install scipy

Packages needed to installmyanalysis.ipynb

Please refer to requirement.txt file for package installation needed or follow the list below:

  • pandas
  • numpy
  • random2
  • matplotlib.pyplot
  • statsmodels.formula.api
  • statsmodels.api

Analysis script and data

You can follow the myanalysis.ipynbrun by jupyter notebook for full analysis rundown. This file can be found in the PelletierDeKoninck-B-QLSC612/script/ folder of this repos. For the data file needed, the file brainsize.csv can be found in the folder PelletierDeKoninck-B-QLSC612/data/ of this repo.

Outputs expected

  • Descriptive statistic table of all variables (with the addition of two random seed variables 'partY' and 'partY2')
  • Multiple regression model (model_partY) results summary output for predicting partY by factors FSIQ, VIQ and PIQ
  • Plots of regression for each factors related to partY
  • Plots of residuals for the three independant variables FSIQ, VIQ and PIQ (factors)
  • Multiple regression model (model_partY2) result summary output for predicting partY2 by factors FSIQ, VIQ and PIQ

About

QLSC612 assignement for Brainhack school 2020

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Jupyter Notebook 100.0%