The tm (text mining) package in R is widely used for effective text analysis. It provides functions for cleaning, preprocessing, and analyzing text data, making it suitable for tasks like sentiment analysis. While other packages may have text-related functions, tm is specifically designed for text mining tasks.

A Sparse Matrix data structure is commonly used in big data applications to efficiently handle sparse data sets, where most of the elements are zero. It helps in saving memory and computational resources.

The Central Limit Theorem states that the distribution of sample means approaches a normal distribution, regardless of the shape of the original population distribution. It's a key concept in statistics and probability theory.

To prioritize tasks effectively, one must differentiate between urgent and important tasks. This distinction helps in focusing on tasks that contribute significantly to goals and objectives, leading to better time management and productivity.

In R, the forecast package is commonly used for advanced time series analysis, providing tools for forecasting future values based on historical data. While packages like zoo and xts handle time series data, forecast is specifically designed for forecasting in the context of time series analysis.

A Stacked Area chart is often used to display changes over time for two or more related groups that make up one whole category. It allows for easy comparison of the overall trend as well as the contribution of each group to the whole.

The Quick Sort algorithm is used for sorting elements. It is highly efficient for large datasets due to its divide-and-conquer approach, which minimizes the number of comparisons needed. Merge Sort also uses a divide-and-conquer approach, but Quick Sort is known for its efficiency in practice.

In Excel, Pivot tables are used to analyze and summarize data sets. They provide a dynamic way to organize and present information, making it easier to draw insights from large datasets.

Heatmaps are effective for presenting complex relationships, making them suitable for displaying patient demographic data alongside treatment outcomes. Stacked Bar Charts and Scatter Plots may not provide the same level of clarity in this scenario, and Dual-Axis Charts are generally used for comparing two different scales.

Z-score normalization is a robust technique for handling outliers by scaling the data based on its mean and standard deviation. It identifies and mitigates the impact of outliers on the dataset.