9.1 Introduction to Hypothesis Testing 9.2 Neyman-Pearson Lemma 9.3 Wald Test 9.4 Lagrangian Multiplier (LM) Test 9.5 Likelihood Ratio Test 9.6 Illustrative Examples 9.7 Conclusion

8.1 Population and Distribution Model 8.2 Maximum Likelihood Estimation 8.3 Asymptotic Properties of MLE 8.4 Method of Moments and Generalized Method of Moments 8.5 Asymptotic Properties of GMM 8.6 Mean Squared Error Criterion 8.7 Best Unbiased Estimators 8.8 Cramer-Rao Lower Bound 8.9 Conclusion

7.1 Limits and Orders of Magnitude: A Review 7.2 Motivation for Convergence Concepts 7.3 Convergence in Quadratic Mean and 𝑳𝑳𝒑𝒑-Convergence 7.4 Convergence in Probability 7.5 Almost Sure Convergence 7.6 Convergence in Distribution 7.7 Central Limit Theorems 7.8 Conclusion

6.1 Population and Random Sample 6.2 Sampling Distribution of Sample Mean 6.3 Sampling Distribution of Sample Variance 6.4 Student’s t-Distribution 6.5 Snedecor's F Distribution 6.6 Sufficient Statistics 6.7 Conclusion

5.1 Random Vectors and Joint Probability Distributions 5.2 Marginal Distributions 5.3 Conditional Distributions 5.4 Independence 5.5 Bivariate Transformation 5.6 Bivariate Normal Distribution 5.7 Expectations and Covariance 5.8 Joint Moment Generating Function 5.9 Implications of Independence on Expectations 5.10 Conditional Expectations 5.11 Conclusion

10.1 Introduction 10.2 Empirical Studies and Statistical Inference 10.3 Important Features of Big Data 10.4 Big Data Analysis and Statistics 10.5 Machine Learning and Statistics 10.6 Conclusion

3.1 Random Variables 3.2 Cumulative Distribution Function 3.3 Discrete Random Variables(DRV) 3.4 Continuous Random Variables 3.5 Functions of a Random Variable 3.6 Mathematical Expectation 3.7 Moments 3.8 Quantiles 3.9 Moment Generating Function (MGF) 3.10 Characteristic Function 3.11 Conclusion

2.1 Random Experiments 2.2 Basic Concepts of Probability 2.3 Review of Set Theory 2.4 Fundamental Probability Laws 2.5 Methods of Counting 2.6 Conditional Probability 2.7 Bayes' Theorem 2.8 Independence 2.9 Conclusion

厦门大学：《概率论与数理统计 Probability and Statistics for Economists》课程教学资源（教学大纲，主讲：洪永淼）

4.1 Introduction to Asymptotic Theory 4.2 Framework and Assumptions 4.3 Consistency of OLS 4.4 Asymptotic Normality of OLS 4.5 Asymptotic Variance Estimation 4.6 Hypothesis Testing 4.7 Testing for Conditional Homoskedasticity 4.8 Conclusion