Project 2
Project Summary: Wine Quality Analysis
This project involves the analysis of a wine dataset to explore various relationships and patterns related to wine quality, alcohol content, acidity, and other attributes. The key steps in the analysis are as follows:
Mean Density Comparison: We calculated the mean density of both red and white wines and performed a t-test to determine if there was a statistically significant difference in their densities.
Combining and Cleaning Datasets: The red and white wine datasets were combined, and any null values or outliers were removed to ensure the data was clean and ready for analysis.
Alcohol Content and Ratings: We examined whether wines with higher alcohol content received better ratings by calculating the mean quality for different alcohol content groups and performing statistical tests.
Citric Acid and pH Relationship: The correlation between citric acid content and pH was analyzed, and a regression model was fitted to understand their relationship better.
Acidity Level Classification: Wines were classified into different acidity levels based on pH, and the mean quality rating for each acidity level was calculated.
Wine Color and Quality: We explored whether a certain type of wine (red or white) is associated with higher quality by comparing the mean quality ratings and performing statistical tests.
Alcohol Content Segmentation: Samples were divided into groups based on whether their alcohol content was above or below the median, and the quality ratings for these groups were analyzed.
Sweetness and Ratings: The relationship between sweetness and wine ratings was explored using correlation analysis and linear regression to see if sweeter wines tend to receive higher ratings.
Counts by Rating and Color: We calculated the number of wines with each rating, grouped by their color (red or white), to see if one type of wine tends to receive certain ratings more frequently.
Density Comparison by Color: A t-test was conducted to assess whether the density of red wine is similar to that of white wine.
Proportion Differences: The project concluded with an analysis of the difference in proportions of red and white wines with a rating of 3, including a confidence interval to determine if the difference is statistically significant.
Overall, this project provides insights into various factors influencing wine quality, helping understand the characteristics that might contribute to higher wine ratings. Statistical methods were used throughout to ensure the findings were robust and significant.
Thanking you
Mohan Sahu