https://github.com/saniyaabushakimova/rigorous-modeling-techniques-for-estimating-student-reaction-times

This project uses the Reaction Time Survey dataset to develop a linear regression model for accurately predicting student reaction times based on various predictors. Tech: R (RStudio)
https://github.com/saniyaabushakimova/rigorous-modeling-techniques-for-estimating-student-reaction-times

exploratory-data-analysis hypothesis-testing lasso model-diagnostics multiple-linear-regression r vif

Last synced: 8 months ago
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This project uses the Reaction Time Survey dataset to develop a linear regression model for accurately predicting student reaction times based on various predictors. Tech: R (RStudio)

• Host: GitHub
• URL: https://github.com/saniyaabushakimova/rigorous-modeling-techniques-for-estimating-student-reaction-times
• Owner: SaniyaAbushakimova
• Created: 2024-07-12T19:30:46.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-07-16T13:29:50.000Z (over 1 year ago)
• Last Synced: 2024-07-16T16:12:39.648Z (over 1 year ago)
• Topics: exploratory-data-analysis, hypothesis-testing, lasso, model-diagnostics, multiple-linear-regression, r, vif
• Homepage: https://humanbenchmark.com/tests/reactiontime
• Size: 1.96 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
Project completed on May 16, 2024.

 

## Project description

Using Reaction Time Survey dataset conduct a rigorous regression modeling and analysis to estimate student reaction times. The project outline is as follows (more details in `project_report.pdf`):

1. Abstract

2. Exploratory Data Analysis (EDA) \

  2.1. Data Understanding \

  2.2. Data Insights \

  2.3. Data Pre-processing 

3. Model Building \

  3.1. Variable Selection and Model Fitting \

  3.2. Diagnostics and Remedies \

       a) Unusual observations \

       b) Error assumptions \

       c) Structure assumptions 

5. Model Comparison and Selection \

  4.1. A model with an interaction term \

  4.2. LASSO Regression 

6. Discussion of Results and Conclusion \

  5.1. Summary \

  5.2. Challenges and Next Steps \

  5.3. Reflection on Lessons Learned 

## Regression Analysis tools used in this project

- Adjusted R^2

- VIF

- Pearson correlation

- ANOVA

- Cramer's V association

- Forward variable selection

- Lasso Regression

- Diagnostics/Remedies

  - Mahalanobis Distance

  - Studentized Residual Test

  - Cook's Distance

  - Q-Q plot / Shapiro-Wilk Test

  - Residuals vs Fitted plot / Breusch-Pagan Test

  - Residuals vs Index plot / Durbin-Watson Test

  - Added-Variable plots

  - Box-Cox transformation

## Other details

`survey.csv` -- raw dataset \

`survey_postEDA.csv` -- dataset after cleaning and preprocessing