Descriptive Statistics:

Example: Mean, Median, Mode

Explanation: Descriptive statistics summarize and describe the main features of a dataset, such as the central tendency $($mean$),$ the middle value $($median$),$ and the most frequently occurring value $($mode$).$ These measures provide a quick overview of key data points and can help healthcare professionals understand patient demographics, resource utilization, and other relevant aspects.

Regression Analysis:

Example: Linear Regression

Explanation: Regression analysis examines the relationship between one dependent variable and one or more independent variables. In healthcare, linear regression can be used to predict outcomes based on specific factors, such as predicting patient readmission rates based on various patient characteristics. This method aids in identifying key variables influencing outcomes and supports evidence$-$based decision$-$making.

Time Series Analysis:

Example: Moving Averages

Explanation: Time series analysis involves studying data points collected over time. Moving averages, for instance, smooth out fluctuations to identify underlying trends. In healthcare, this method can be applied to track patient admission rates, monitor the effectiveness of treatment protocols over time, and predict future resource needs. Time series analysis contributes to proactive decision$-$making by anticipating trends and patterns.

Each of these statistical methods provides valuable insights for decision$-$making in healthcare by offering a comprehensive understanding of data patterns, relationships, and trends. This information can guide healthcare professionals in optimizing resource allocation, improving patient care, and enhancing overall operational efficiency.