Chapter 6 Introduction to Sampling Theory and Statistics 6.1 Population and Random Sample 6.2 The Sampling Distribution of the Sample Mean 6.3 The Sampling Distribution of the Sample Variance 6.4 Student's t Distribution 6.5 Snedecor's F Distribution 6.6 Sufficient Statistics Chapter 7 Convergence Concepts and Limit Theories 7.1 Limits and Orders of Magnitude:A Review 7.2 Motivation for Convergence Concepts 7.3 Convergence in Quadratic Mean and Lp-convergence 7.4 Convergence in Probability 7.5 Almost Sure Convergence 7.6 Convergence in Distribution Chapter 8 Parameter Estimation and Evaluation 8.1 Population and Distribution Model 8.2 Maximum Likelihood Estimation 8.3 Method of Moments and Generalized Method of Moments 8.4 Mean Squared Error Criterion 8.5 Best Unbiased Estimators Chapter 9 Hypothesis Testing 9.1 Introduction to Hypothesis Testing 9.2 The Wald Test 9.3 The Lagrangian Multiplier Test 9.4 The Likelihood Ratio Test 9.5 A Simple Example Chapter 10 Big data,Machine Learning and Statistics 10.1 Features of Big Data 10.2 Machine Learning and Its Nature 10.3 Big Data and Sampling Theory 10.4 Statistical Significance and Economic Importance 10.5 Big Data,Machine Learning and Model Uncertainty 4Chapter 6 Introduction to Sampling Theory and Statistics 6.1 Population and Random Sample 6.2 The Sampling Distribution of the Sample Mean 6.3 The Sampling Distribution of the Sample Variance 6.4 Student’s t Distribution 6.5 Snedecor’s F Distribution 6.6 Sufficient Statistics Chapter 7 Convergence Concepts and Limit Theories 7.1 Limits and Orders of Magnitude: A Review 7.2 Motivation for Convergence Concepts 7.3 Convergence in Quadratic Mean and Lp-convergence 7.4 Convergence in Probability 7.5 Almost Sure Convergence 7.6 Convergence in Distribution Chapter 8 Parameter Estimation and Evaluation 8.1 Population and Distribution Model 8.2 Maximum Likelihood Estimation 8.3 Method of Moments and Generalized Method of Moments 8.4 Mean Squared Error Criterion 8.5 Best Unbiased Estimators Chapter 9 Hypothesis Testing 9.1 Introduction to Hypothesis Testing 9.2 The Wald Test 9.3 The Lagrangian Multiplier Test 9.4 The Likelihood Ratio Test 9.5 A Simple Example Chapter 10 Big data, Machine Learning and Statistics 10.1 Features of Big Data 10.2 Machine Learning and Its Nature 10.3 Big Data and Sampling Theory 10.4 Statistical Significance and Economic Importance 10.5 Big Data, Machine Learning and Model Uncertainty 4