Maestro

1. Basic Tools
2.1. Goals of inference
2.2.1. Population or process?
2.2.2. Probability samples
2.2.3. Sampling weights
2.2.4. Design effects.2.2. An introduction to the data
2.3.5. Real surveys
2.3.6. Populations
2.3. Obtaining the software
2.4.7. Obtaining R
2.4.8. Obtaining the survey package2.4. Using R
2.5.9. Reading plain text data
2.5.10. Reading data from other packages
2.5.11. Simple computations

2. Simple and Stratified sampling3.5. Analysing simple random samples
3.6.12. Confidence intervals
3.6.13. Describing the sample to R
3.6. Stratified sampling
3.7. Replicate weights3.8.14. Specifying replicate weights to R
3.8.15. Creating replicate weights in R
3.8. Other population summaries
3.9.16. Quantiles
3.9.17. Contingencytables
3.9. Estimates in subpopulations
3.10. Design of stratified samples

3. Cluster sampling
4.11. Introduction
4.12.18. Why clusters: the NHANES II design4.12.19. Single-stage and multistage designs
4.12. Describing multistage designs to R
4.13.20. Strata with only one PSU
4.13.21. How good is the single-stageapproximation?
4.13.22. Replicate weights for multistage samples
4.13. Sampling by size
4.14.23. Loss of information from sampling clusters
4.14. Repeated measurements4. Graphics 57
5.15. Why is survey data different?
5.16. Plotting a table
5.17. One continuous variable
5.18.24. Graphs based on the distribution function5.18.25. Graphs based on the density
5.18. Two continuous variables
5.19.26. Scatterplots
5.19.27. Aggregation and smoothing
5.19.28. Scatterplot...