•随机向量及其概率分布 •随机向量的联合分布函数 •随机变量函数的分布

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

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

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

4.1 Introduction 4.2 Discrete Probability Distributions 4.3 Continuous Probability Distributions 4.4 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）

§8.1 假设检验 §8.2 正态总体均值的假设检验 §8.3 正态总体方差的假设检验 §8.6 分布拟合检验

§6.1 点估计的几种方法 §6.2 点估计的评价标准 §6.3 最小方差无偏估计 §6.4 贝叶斯估计 §6.5 区间估计

§7.1 点估计 §7.2 基于截尾样本的最大似然估计 §7.3 估计量的评选标准 §7.4 区间估计 §7.5 正态总体均值和方差的区间估计 §7.6 (0－1)分布参数的区间估计 §7.7 单侧置信区间