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