Descriptive Analysis:
Objective:
To describe the main features of a dataset.
Techniques:
Summary statistics (mean, median, mode), frequency distributions, data visualization (graphs, charts).
Exploratory Analysis (EDA):
Objective:
To explore data to find patterns, relationships, or anomalies without having a predetermined hypothesis.
Techniques:
Data visualization, correlation analysis, clustering.
Inferential Analysis:
Objective:
To make inferences about a population based on a sample of data.
Techniques:
Hypothesis testing, confidence intervals, regression analysis.
Predictive Analysis:
Objective:
To predict future outcomes based on historical data.
Techniques:
Machine learning models (linear regression, decision trees, neural networks), time series analysis.
Causal Analysis:
Objective: To determine the cause-and-effect relationships between variables.
Techniques: Experiments, randomized controlled trials, quasi-experiments.
Mechanistic Analysis:
Objective: To understand the underlying mechanisms and how variables interact with each other.
Techniques: System dynamics modeling, agent-based modeling.
Diagnostic Analysis:
Objective: To identify reasons behind past performance and understand why events occurred.
Techniques: Drill-down analysis, data mining, root cause analysis.
Prescriptive Analysis:
Objective: To provide recommendations for actions to achieve desired outcomes.
Techniques: Optimization algorithms, simulation, decision analysis.