<P>Non-parametric methods are very important in common statistical analysis.</P>
<P>Therefore i share this document, which may be good and easy for a beginner for free</P>
<P>Just enjoy it! </P>

<P>Title:</P>
<P>Applied nonparametric statistical methods 3rd edit</P>
<P>by Sprent and Smeeton 1999</P>
<P>Contents:</P>
<P>1 Introducing nonparametric methods<BR>1.1 Basic statistics<BR>1.2 Samples and populations<BR>1.3 Hypothesis tests<BR>1.4 Estimation<BR>1.5 Ethical issues<BR>1.6 Computers and nonparametric methods<BR>1.7 Further reading<BR>Exercises<BR></P>
<P>2 Centrality inference for single samples<BR>2.1 Using measurement data<BR>2.2 Inferences about medians based on ranks</P>
<P>2.3 The sign test<BR>2.4 Transformation of ranks<BR>2.5 Asymptotic results<BR>2.6 Robustness<BR>2.7 Fields of application<BR>2.8 Summary<BR>Exercises<BR></P>
<P>3 Other single-sample inference<BR>3.1 Inferences for dichotomous data<BR>3.2 Tests related to the sign test<BR>3.3 Matching samples to distributions<BR>3.4 Angular data<BR>3.5 A runs test for randomness<BR>3.6 Fields of application<BR>3.7 Summary<BR>Exercises<BR></P>
<P>4 Methods for paired samples<BR>4.1 Comparisons in pairs<BR>4.2 A less obvious use of the sign test<BR>4.3 Power and sample size<BR>4.4 Fields of application</P>
<P>4.5 Summary<BR>Exercises</P>
<P>5 Methods for two independent samples<BR>5.1 Centrality tests and estimates<BR>5.2 Rank based tests<BR>5.3 The median test<BR>5.4 Normal scores<BR>5.5 Tests for survival data<BR>5.6 Asymptotic approximations<BR>5.7 Power and sample size<BR>5.8 Tests for equality of variance<BR>5.9 Tests for a common distribution<BR>5.10 Fields of application<BR>5.11 Summary<BR>Exercises<BR></P>
<P>6 Three or more samples<BR>6.1 Compaarisons with parametric methods<BR>6.2 Centrality tests for independent samples<BR>6.3 Centrality tests for related samples<BR>6.4 More detailed treatment comparisons<BR>6.5 Tests for heterogeneity of variance<BR>6.6 Some miscellaneous considerations</P>
<P>6.7 Fields of application<BR>6.8 Summary<BR>Exercises<BR>7 Correlation and concordance<BR>7.1 Correlation and bivariate data<BR>7.2 Ranked data for several variables<BR>7.3 Agreement<BR>7.4 Fields of application<BR>7.5 Summary<BR>Exercises</P>
<P><BR>8 Regression<BR>8.1 Bivariate linear regression<BR>8.2 Multiple regression<BR>8.3 Nonparametric regression models<BR>8.4 Other multivariate data problems<BR>8.5 Fields of application<BR>8.6 Summary<BR>Exercises</P>
<P><BR>9 Categorical data<BR>9.1 Categories and counts<BR>9.2 Nominal attribute categories<BR>9.3 Ordered categorical data<BR>9.4 Goodness-of-fit tests for discrete data<BR>9.5 Extension of McNemar's test<BR>9,6 Fields of application<BR>9.7 Summary<BR>Exercises<BR>10 Association in categorical data<BR>10.1 The analysis of association<BR>10.2 Some models for contingency tables<BR>10.3 Combining and partitioning of tables<BR>10.4 Power<BR>10.5 Fields of application<BR>10.6 Summary<BR>Exercises<BR>11 Robust estimation<BR>11.1 When assumptions break down<BR>11.2 Outliers and influence<BR>11.3 The bootstrap<BR>11.4 M-estimators and other robust estimators</P>
<P>11.5 Fields of application<BR>11.6 Summary<BR>Exercises</P>
<P><BR>Appendix</P>
<P><BR>References</P>
<P><BR>Solutions to odd-numbered exercises</P>

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