Mastering Probability and Statistics: A Comprehensive Guide to Learn Probability and Statistics

Table of contents :
1. The Significance of Probability and Statistics
1.1 Understanding Probability and Statistics in the Modern World
1.2 Historical Evolution and Influence on Decision Making
1.3 Probability and Statistics in Science, Business, and Research
2. Fundamentals of Probability Theory
2.1 Basics of Probability: Sample Space, Events, and Probability Axioms
2.2 Conditional Probability and Independence
2.3. Combinatorics and Counting Principles
3. Discrete Probability Distributions
3.1. Probability Mass Functions and Expected Values
3.2. Bernoulli, Binomial, and Poisson Distributions
3.3. Geometric and Negative Binomial Distributions
4. Continuous Probability Distributions
4.1. Probability Density Functions and Cumulative Distribution Functions
4.2. Normal Distribution and Standardization
4.3. Exponential and Uniform Distributions
5. Multivariate Probability Distributions
5.1. Joint, Marginal, and Conditional Distributions
5.2. Bivariate Normal Distribution
5.3. Copulas and Dependence Structures
6. Sampling and Sampling Distributions
6.1. Simple Random Sampling and Sampling Techniques
6.2. Sampling Distribution of Sample Mean and Central Limit Theorem
6.3. Estimation and Confidence Intervals
7. Hypothesis Testing
7.1. Null and Alternative Hypotheses
7.2. Type I and Type II Errors
7.3. Parametric and Nonparametric Tests
8. Linear Regression Analysis
8.1. Simple Linear Regression: Model and Estimation
8.2. Multiple Linear Regression: Model and Diagnostics
8.3. Regression Inference and Interpretation
9. Nonlinear and Generalized Linear Models
9.1. Polynomial Regression and Model Selection
9.2. Logistic Regression and Binary Classification
9.3. Poisson Regression and Count Data Modeling
10. Multivariate Descriptive Statistics
10.1. Multivariate Data and Data Visualization
10.2. Principal Component Analysis (PCA) and Dimensionality Reduction
10.3. Factor Analysis and Exploratory Data Analysis
11. Multivariate Inferential Statistics
11.1. Multivariate Analysis of Variance (MANOVA)
11.2. Multivariate Regression and Canonical Correlation Analysis
11.3. Clustering and Classification Techniques
12. Time Series Basics and Descriptive Methods
12.1. Time Series Data and Components
12.2. Smoothing Techniques and Moving Averages
12.3. Decomposition and Seasonal Decomposition
13. Time Series Forecasting
13.1. ARIMA Models and Box-Jenkins Methodology
13.2. Exponential Smoothing Methods
13.3. State Space Models and Forecast Accuracy Evaluation
14. Introduction to Bayesian Inference
14.1. Bayes' Theorem and Posterior Distribution
14.2. Bayesian Parameter Estimation and Credible Intervals
14.3. Bayesian Hypothesis Testing and Model Comparison
15. Markov Chain Monte Carlo (MCMC) Methods
15.1. Metropolis-Hastings Algorithm
15.2. Gibbs Sampling and Hamiltonian Monte Carlo
15.3. Practical Considerations and Convergence Diagnostics
16. Experimental Design and Analysis of Variance (ANOVA)
16.1. One-Way ANOVA and Post Hoc Tests
16.2. Factorial and Nested ANOVA Designs
16.3. Design of Experiments and Response Surface Methodology
17. Nonparametric Statistics and Robust Methods
17.1. Wilcoxon Rank-Sum and Signed-Rank Tests
17.2. Kruskal-Wallis and Friedman Tests
17.3. Robust Regression and Outlier Detection
18. Bayesian Networks and Causal Inference
18.1. Probabilistic Graphical Models and Bayesian Networks
18.2. Causal Inference and Counterfactuals
18.3. Applications of Bayesian Networks and Causal Inference in Health, Social Sciences, and Economics
19. Machine Learning and Statistics Integration
19.1. Synergies and Overlaps between Machine Learning and Statistics
19.2. Model Evaluation and Cross-Validation
19.3. Bias-Variance Trade-off and Model Selection
20. Statistics in Business and Economics
20.1. Descriptive Business Analytics
20.2. Demand Forecasting and Inventory Management
20.3. Regression Analysis in Marketing Research
21. Bistatistics and Medical Applications
21.1. Clinical Trials and Experimental Design
21.2. Survival Analysis and Cox Proportional Hazards Model
21.3. Epidemiological Studies and Public Health Analysis
22. Data Science and Big Data Analytics
22.1. Statistical Learning in Big Data Environments
22.2. Text Mining and Sentiment Analysis
22.3. Anomaly Detection and Fraud Analytics
23. Ethics in Data Analysis and Reporting
23.1. Data Privacy and Confidentiality
23.2. Avoiding Data Manipulation and Bias
23.3. Responsible Interpretation and Reporting
24. Emerging Trends and Future Directions
24.1. Bayesian Deep Learning and Probabilistic Graph Neural Networks
24.2. Interpretability and Explainable AI
24.3. Challenges and Opportunities in Advanced Analytics
25. Appendix
25.1. Statistical Tables and Formulas
25.2. Glossary of Probability and Statistics Terminology
25.3. Statistical Software and Programming Resources
25.4. Recommended Readings and Further Study
25.5. About the author