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
11. Interval Estimation
Chapter 11 discusses techniques such as the pivoting method for interval estimation. The central limit theorem is used to derive confidence intervals for the mean parameters of binomial, Poisson and normal distributions. For the normal distribution we also discuss the exact confidence interval using the t-distribution without actually deriving the sampling distribution of the t-statistic.
