Conduct hypothesis testing using bootstrap methods, implement resampling techniques, and compute confidence intervals. The assignment will incorporate a project developed

Background

Conduct hypothesis testing using bootstrap methods, implement resampling techniques, and compute confidence intervals. The assignment will incorporate a project developed in R, a report presenting the results. It will also incorporate a research review on the current state of Bootstrapping techniques utilization in Data Science.   

Instructions

Using this dataset contains physicochemical properties and quality ratings of red and white variants of the Portuguese "Vinho Verde" wine. Features include fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free sulfur dioxide, total sulfur dioxide, density, pH, sulphates, alcohol content, and a final quality rating from 0 (very bad) to 10 (very excellent). 

Source: The UCI Machine Learning Repository - Wine Quality Dataset (https://archive.ics.uci.edu/ml/datasets/wine+quality) 

1. Setup and Data Preparation 
   1. Install and load necessary R packages: tidyverse for data manipulation and visualization, boot for bootstrap analysis.
   2. Download the Wine Quality Dataset.
   3. Read the data into R using read.csv() and perform initial data exploration with functions like summary() and head(). 
2. Exploratory Data Analysis (EDA) 
   1. Visualize the distribution of wine quality ratings for both red and white wine samples.
   2. Explore relationships between physicochemical properties and wine quality using scatter plots and correlation analysis. 
3. Formulate a Hypothesis 
   1. Example hypothesis: "The average alcohol content of high-quality wine (rating >= 7) is significantly higher than that of lower-quality wine (rating < 7)." 
4. Bootstrap Resampling for Hypothesis Testing 
   1. Implement bootstrap resampling to estimate the difference in mean alcohol content between high-quality and low-quality wines.
   2. Draw many resamples with replacement from the observed dataset, compute the mean alcohol content for high-quality and low-quality wines in each resample, and calculate the difference. 
5. Compute Confidence Intervals 
   1. Use the bootstrap samples to compute a 95% confidence interval for the mean difference in alcohol content.
   2. Interpret the confidence interval in the context of the hypothesis. 
6. Perform Hypothesis Testing 
   1. Determine whether the observed difference in means is statistically significant based on the bootstrap confidence interval.
   2. Discuss the p-value interpretation and whether the null hypothesis can be rejected. 
7. Report Writing 
   1. Introduction: Briefly introduce the project, dataset, and hypothesis.
   2. Methods: Describe the bootstrap resampling technique, hypothesis testing approach, and confidence interval computation.
   3. Results: Present the findings from the bootstrap analysis, including visualizations of the confidence interval and the conclusion regarding the hypothesis.
   4. Discussion: Interpret the results, discuss potential limitations of the study, and suggest future research directions.
   5. References: Cite all sources and R packages used. 

Submit: 

• R Script (.R file): Containing all the code used for data preparation, EDA, bootstrap analysis, hypothesis testing, and confidence interval computation. 
• Report (.docx): A comprehensive report detailing the project's objective, methodology, results, and conclusions. 

Length: This assignment must be 5-8 pages (excluding the title and reference page). 

References: Include 3 scholarly resources.