Non-parametric methods are very important in common statistical analysis.

Therefore i share this document, which may be good and easy for a beginner for free

Just enjoy it!

Title:

Applied nonparametric statistical methods 3rd edit

by Sprent and Smeeton 1999

Contents:

1 Introducing nonparametric methods
1.1 Basic statistics
1.2 Samples and populations
1.3 Hypothesis tests
1.4 Estimation
1.5 Ethical issues
1.6 Computers and nonparametric methods
1.7 Further reading
Exercises

2 Centrality inference for single samples
2.1 Using measurement data
2.2 Inferences about medians based on ranks

2.3 The sign test
2.4 Transformation of ranks
2.5 Asymptotic results
2.6 Robustness
2.7 Fields of application
2.8 Summary
Exercises

3 Other single-sample inference
3.1 Inferences for dichotomous data
3.2 Tests related to the sign test
3.3 Matching samples to distributions
3.4 Angular data
3.5 A runs test for randomness
3.6 Fields of application
3.7 Summary
Exercises

4 Methods for paired samples
4.1 Comparisons in pairs
4.2 A less obvious use of the sign test
4.3 Power and sample size
4.4 Fields of application

4.5 Summary
Exercises

5 Methods for two independent samples
5.1 Centrality tests and estimates
5.2 Rank based tests
5.3 The median test
5.4 Normal scores
5.5 Tests for survival data
5.6 Asymptotic approximations
5.7 Power and sample size
5.8 Tests for equality of variance
5.9 Tests for a common distribution
5.10 Fields of application
5.11 Summary
Exercises

6 Three or more samples
6.1 Compaarisons with parametric methods
6.2 Centrality tests for independent samples
6.3 Centrality tests for related samples
6.4 More detailed treatment comparisons
6.5 Tests for heterogeneity of variance
6.6 Some miscellaneous considerations

6.7 Fields of application
6.8 Summary
Exercises
7 Correlation and concordance
7.1 Correlation and bivariate data
7.2 Ranked data for several variables
7.3 Agreement
7.4 Fields of application
7.5 Summary
Exercises

8 Regression
8.1 Bivariate linear regression
8.2 Multiple regression
8.3 Nonparametric regression models
8.4 Other multivariate data problems
8.5 Fields of application
8.6 Summary
Exercises

9 Categorical data
9.1 Categories and counts
9.2 Nominal attribute categories
9.3 Ordered categorical data
9.4 Goodness-of-fit tests for discrete data
9.5 Extension of McNemar's test
9,6 Fields of application
9.7 Summary
Exercises
10 Association in categorical data
10.1 The analysis of association
10.2 Some models for contingency tables
10.3 Combining and partitioning of tables
10.4 Power
10.5 Fields of application
10.6 Summary
Exercises
11 Robust estimation
11.1 When assumptions break down
11.2 Outliers and influence
11.3 The bootstrap
11.4 M-estimators and other robust estimators

11.5 Fields of application
11.6 Summary
Exercises

Appendix

References

Solutions to odd-numbered exercises