Econometrics and Data Analysis

1. Fundamental Concepts
   1. Economic Variables and Models
      1. Types of Economic Variables
         1. Dependent and Independent Variables
         2. Continuous and Discrete Variables
         3. Categorical Variables
            1. Nominal Variables
            2. Ordinal Variables
      2. Modeling Economic Relationships
         1. Theoretical Models
            1. Structural Models
            2. Reduced-Form Models
         2. Statistical Models
            1. Interpretation of Model Coefficients
            2. Estimation Accuracy and Bias
         3. Model Selection Criteria
            1. Akaike Information Criterion (AIC)
            2. Bayesian Information Criterion (BIC)
            3. Adjusted R-Squared
   2. Hypothesis Testing
      1. Formulation of Hypotheses
         1. Null Hypothesis (H0)
         2. Alternative Hypothesis (H1)
      2. Types of Hypothesis Tests
         1. t-test
         2. F-test
         3. Chi-square test
         4. ANOVA
      3. Statistical Significance
         1. p-values
         2. Confidence Intervals
         3. Significance Levels
      4. Type I and Type II Errors
         1. Definitions and Examples
         2. Consequences in Economic Research
      5. Power of a Test
         1. Factors Influencing Power
         2. Techniques for Increasing Power
   3. Causality and Correlation
      1. Understanding Causality
         1. Causal Inference
            1. Potential Outcomes Framework
            2. Counterfactual Thinking
         2. Establishing Causation
            1. Experimental Designs
            2. Quasi-Experimental Designs
            3. Randomized Controlled Trials (RCTs)
      2. Correlation Analysis
         1. Measuring Correlation
            1. Pearson Correlation Coefficient
            2. Spearman's Rank Correlation
         2. Interpreting Correlation Results
            1. Strength and Direction of Relationship
            2. Limitations of Correlation Studies
      3. Distinguishing Between Correlation and Causation
         1. Confounding Variables
         2. Coincidence and Spurious Relationships
         3. Methods to Establish Causality
            1. Instrumental Variables
            2. Granger Causality
            3. Path Analysis and Structural Equation Modeling (SEM)