This study investigates the application of Linear Mixed Models (LMM) to analyze clustered data, where traditional independence assumptions are violated. By distinguishing between fixed and random effects, we aim to capture the inherent variability within clusters, such as schools or companies. This approach enhances our understanding of how predictors influence outcomes while accounting for inter-cluster differences, ultimately providing deeper insights into complex datasets. The findings underscore the importance of LMM in transforming data into actionable insights in various fields.

Comments: https://docs.google.com/document/d/1BTG-z2JVJBzUXnY6IKjZKSZVDbXPAWcAroNiFtnXHPM/edit?usp=sharing

Info from first term including work books

This analysis explores the Haberman dataset, which contains data from patients who have undergone surgery for breast cancer. We will perform Exploratory Data Analysis (EDA) and construct a neural network model to predict survival status.

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This article has three sections: analysis of store sales, time series analysis, and the development of a dashboard in PowerBI. In the store sales analysis, I test for hypotheses and create a regression model. In the time series section, I forecast store sales from historical data. Finally, I create a dashboard for the retail store to inform managers of developments in the business.