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A statistical analysis project that explores how wine ratings relate to factors like price and acidity using hypothesis testing techniques such as the Kruskal-Wallis and Shapiro-Wilk tests.
In this project, a regression-based performance prediction model was developed to estimate building energy consumption based on simplified façade attribute information and weather conditions.
Gain hands-on experience with ANOVA analysis, understanding its assumptions, and applying it to real-world datasets to understand differences among group means.
Performed rigorous preprocessing, and data cleaning, and conducted exploratory data analysis to identify trends, patterns, and outliers, leading to valuable insights. Employed various statistical methods concepts to get insights about the data for prediction.
Gain hands-on experience with ANOVA analysis, understanding its assumptions, and applying it to real-world datasets to understand differences among group means.
This project applies MANOVA to heart disease data, examining the impact of chest pain type (cp), cholesterol (chol), and heart rate (thalach). It includes normality tests, outlier detection, post-hoc analysis, and cross-validation for model evaluation.
A Python-based utility for testing and visualizing data transformations. Includes normality tests (Shapiro-Wilk and Lilliefors), a variety of transformation methods, and visual analysis tools for histogram and Q-Q plots.