ipsrdbs1. Introduction to Basic Statistics2. Getting Started with R3. Introduction to Probability4. Conditional Probability and Independence5. Random Variables and Their Probability Distributions6. Standard Discrete Distributions7. Standard Continuous Distributions8. Joint Distributions and the CLT9. Introduction to Statistical Inference10. Methods of Point Estimation11. Interval Estimation12. Hypothesis Testing13. Generating Functions14. Transformation and Transformed Distributions15. Multivariate Distributions16. Convergence of Estimators17. Simple Linear Regression Model18. Multiple Linear Regression Model19. Analysis of VarianceResources
ipsrdbs1. Introduction to Basic Statistics2. Getting Started with R3. Introduction to Probability4. Conditional Probability and Independence5. Random Variables and Their Probability Distributions6. Standard Discrete Distributions7. Standard Continuous Distributions8. Joint Distributions and the CLT9. Introduction to Statistical Inference10. Methods of Point Estimation11. Interval Estimation12. Hypothesis Testing13. Generating Functions14. Transformation and Transformed Distributions15. Multivariate Distributions16. Convergence of Estimators17. Simple Linear Regression Model18. Multiple Linear Regression Model19. Analysis of VarianceResources
9. Introduction to Statistical Inference
This chapter introduces the basic concepts of statistical inference and statistical modelling. It distinguishes between population distributions and sample statistics (quantities). The concepts of estimators and their sampling (probability) distributions are also introduced. The properties of bias and mean square errors of estimators and defined.
See the Chapter 9 code and output file for the R illustrations provided in this chapter.
