here’s a brief description for each common analysis method:
- Descriptive Analysis: Describes the basic features of data in the study, providing simple summaries about the sample and measures of the study variables.
- Inferential Analysis: Makes inferences or predictions about a larger population based on a sample of data taken from that population, using statistical techniques like hypothesis testing and regression analysis.
- Predictive Analysis: Uses historical data to make predictions about future events or outcomes, employing techniques like regression analysis, time series analysis, and machine learning algorithms.
- Diagnostic Analysis: Examines data to understand the causes of past outcomes or events, identifying patterns, correlations, or anomalies to diagnose problems or opportunities.
- Prescriptive Analysis: Provides recommendations or suggestions for actions to take based on the insights gained from descriptive, inferential, predictive, and diagnostic analyses, helping organizations make informed decisions.
- Exploratory Data Analysis (EDA): Investigates and visualizes data sets to discover patterns, trends, relationships, or anomalies that can guide further analysis or hypothesis generation.
- Causal Analysis: Determines cause-and-effect relationships between variables, often through experimental design or advanced statistical modeling techniques like causal inference methods.
- Correlation Analysis: Measures the strength and direction of the relationship between two or more variables, helping identify associations or dependencies in the data.
- Time Series Analysis: Analyzes time-ordered data points to understand patterns, trends, and seasonal variations over time, commonly used in forecasting and trend analysis.
- Cluster Analysis: Groups similar data points together into clusters or segments based on their characteristics, helping identify patterns or segments within the data.
- Regression Analysis: Examines the relationship between one dependent variable and one or more independent variables, estimating the strength and direction of the relationships and making predictions based on those relationships.
- Factor Analysis: Reduces the dimensionality of data sets by identifying underlying factors or latent variables that explain the observed correlations among variables.
- Machine Learning Algorithms: Utilizes various algorithms and techniques to enable computers to learn from and make predictions or decisions based on data, including supervised learning, unsupervised learning, and reinforcement learning methods.