Agricolae Tutorial (Version 1.3-5) Preface
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Nonparametric Methods
Chapter 6 Nonparametric Methods CHAPTER VI; SECTION A: INTRODUCTION TO NONPARAMETRIC METHODS Purposes of Nonparametric Methods: Nonparametric methods are uniquely useful for testing nominal (categorical) and ordinal (ordered) scaled data--situations where parametric tests are not generally available. An important second use is when an underlying assumption for a parametric method has been violated. In this case, the interval/ratio scale data can be easily transformed into ordinal scale data and the counterpart nonparametric method can be used. Inferential and Descriptive Statistics: The nonparametric methods described in this chapter are used for both inferential and descriptive statistics. Inferential statistics use data to draw inferences (i.e., derive conclusions) or to make predictions. In this chapter, nonparametric inferential statistical methods are used to draw conclusions about one or more populations from which the data samples have been taken. Descriptive statistics aren’t used to make predictions but to describe the data. This is often best done using graphical methods. Examples: An analyst or engineer might be interested to assess the evidence regarding: 1. The difference between the mean/median accident rates of several marked and unmarked crosswalks (when parametric Student’s t test is invalid because sample distributions are not normal). 2. The differences between the absolute average errors between two types of models for forecasting traffic flow (when analysis of variance is invalid because distribution of errors is not normal). 3. The relationship between the airport site evaluation ordinal rankings of two sets of judges, i.e., citizens and airport professionals. 4. The differences between neighborhood districts in their use of a regional mall for purposes of planning transit routes. -
Scikit-Posthocs Documentation Release 0.6.6
scikit-posthocs Documentation Release 0.6.6 Maksim Terpilowski Sep 11, 2021 Documentation 1 Introduction 3 1.1 Background................................................3 1.2 Features..................................................4 2 Installation 7 2.1 Dependencies...............................................7 2.2 Bugs...................................................7 3 Tutorial 9 3.1 Parametric ANOVA with post hoc tests.................................9 3.2 Non-parametric ANOVA with post hoc tests.............................. 10 3.3 Block design............................................... 10 3.4 Data types................................................ 12 3.5 Significance plots............................................. 13 4 Global Tests API reference 15 4.1 scikit_posthocs.global_f_test....................................... 15 4.2 scikit_posthocs.global_simes_test.................................... 16 5 Omnibus API reference 17 5.1 scikit_posthocs.test_mackwolfe..................................... 17 5.2 scikit_posthocs.test_osrt......................................... 18 5.3 scikit_posthocs.test_durbin....................................... 19 6 Outliers API reference 21 6.1 scikit_posthocs.outliers_iqr....................................... 21 6.2 scikit_posthocs.outliers_gesd...................................... 22 6.3 scikit_posthocs.outliers_grubbs..................................... 23 6.4 scikit_posthocs.outliers_tietjen..................................... 24 7 Plotting API reference 27 7.1 scikit_posthocs.sign_array....................................... -
Practical Nonparametric Statistics
2008ÊÊÊ季季季 PPP母母母數數數統統統計計計講講講LLL ù福Ê 中山大.T用數.系 [email protected] PRACTICAL NONPARAMETRIC STATISTICS Third Edition (1999) W. J. Conover 2008-02-18 ∼ 2008-06-15 This page intentionally left blank CONTENTS i CONTENTS Preface iii 課程+紹 ........................................ iii ®¼ .......................................... iv b用網頁 ........................................ iv I Lecture Notes 1 1 Probability Theory 2 1.1 Counting ..................................... 2 1.2 Probability ................................... 5 1.3 Random variables ................................ 8 1.4 Some properties of random variables ..................... 12 1.5 Continuous random variables ......................... 19 1.6 Summary .................................... 24 2 Statistical Inference 32 2.1 Populations, samples, and statistics ...................... 32 2.2 Estimation .................................... 35 2.3 Hypothesis testing ............................... 42 2.4 Some properties of hypothesis tests ...................... 46 2.5 Some comments on nonparametric statistics ................. 50 2.6 Summary .................................... 53 3 Some Tests Based on the Binomial Distribution 57 3.1 The binomial test and estimation of p ..................... 58 3.2 The quantile test and estimation of xp .................... 63 3.3 Tolerance limits ................................. 69 3.4 The sign test .................................. 72 3.5 Some variations of the sign test ........................ 77 3.6 Summary ................................... -
Nonparametric Tests for Latin Squares
University of Wollongong Research Online Centre for Statistical & Survey Methodology Faculty of Engineering and Information Working Paper Series Sciences 2011 Nonparametric tests for latin squares John Best University of Newcastle John Rayner University of Wollongong Follow this and additional works at: https://ro.uow.edu.au/cssmwp Recommended Citation Best, John and Rayner, John, Nonparametric tests for latin squares, Centre for Statistical and Survey Methodology, University of Wollongong, Working Paper 11-11, 2011, 12. https://ro.uow.edu.au/cssmwp/83 Research Online is the open access institutional repository for the University of Wollongong. For further information contact the UOW Library: [email protected] Nonparametric Tests for Latin Squares D.J. Best School of Mathematical and Physical Sciences, University of Newcastle, NSW 2308, Australia [email protected] and J.C.W. Rayner Centre for Statistical and Survey Methodology, School of Mathematics and Applied Statistics, University of Wollongong, NSW 2522, Australia and School of Mathematical and Physical Sciences, University of Newcastle, NSW 2308, Australia [email protected] _____________________________________________________________________ Abstract A number of nonparametric tests for the Latin square are examined. The rank transform method has good test sizes and powers for the 5 × 5 Latin square for various parameter values and error distributions. Alignment procedures are also examined and their use illustrated using data for replicated Latin squares. _____________________________________________________________________ Keywords: Aligned data, Kruskal-Wallis statistic, Rank transform statistic, replicated Latin square. 1. Introduction Nonparametric tests for some of the simpler experimental designs are well known. Three of the best known are the Kruskal-Wallis test for the one-way layout, the Friedman test for randomised blocks and the Durbin test for the balanced incomplete block design.