## Introduction to Nonparametric Statistics for the Biological Sciences Using RThis book contains a rich set of tools for nonparametric analyses, and the purpose of this text is to provide guidance to students and professional researchers on how R is used for nonparametric data analysis in the biological sciences: - To introduce when nonparametric approaches to data analysis are appropriate
- To introduce the leading nonparametric tests commonly used in biostatistics and how R is used to generate appropriate statistics for each test
- To introduce common figures typically associated with nonparametric data analysis and how R is used to generate appropriate figures in support of each data set
The book focuses on how R is used to distinguish between data that could be classified as nonparametric as opposed to data that could be classified as parametric, with both approaches to data classification covered extensively. Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses and tests using R to broadly compare differences between data sets and statistical approach. |

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### Contents

1 | |

2 Sign Test | 51 |

3 ChiSquare | 77 |

4 MannWhitney U Test | 103 |

5 Wilcoxon MatchedPairs SignedRanks Test | 133 |

6 KruskalWallis HTest for Oneway Analysis of Variance ANOVA by Ranks | 176 |

### Other editions - View all

Introduction to Nonparametric Statistics for the Biological Sciences Using R Thomas W. MacFarland,Jan M. Yates No preview available - 2016 |

### Common terms and phrases

Added Mineral Supplement Alfalfa Weevil Larvae analyses Anderson-Darling Test ask=TRUE associated attached packages axis barplot Bed Rest Bold boxplot breakout groups calculated p-value Chi-square Class_B Code Book col="red colostrum column comma-separated values Confirm all attached continuity correction correlation csv file dataframe DataFrame$ObjectName notation dataset density plot descriptive statistics distribution patterns Factor font format frequency distributions Friedman Test function Gender getwd Hispanic Histogram Identify Label lesson List all objects Load main="Density Plot main="Histogram Median Mean 3rd missing data na.rm=TRUE normal distribution Note Null Hypothesis object variable ordinal data organized outcomes output Pair par(ask=TRUE parametric Posttest measures Pounds Pretest and Posttest Protocol quality assurance R-based Race-Ethnicity recode Regular Feed sample savefont savelwd Scatter plot Select sessionInfo Set CRAN mirror Show the information side-by-side Sign Test Spearman’s rho statistically significant statistically significant difference subjects summary syntax Systolic Blood Pressure Treatment ewes visual Weight WeightLb