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E-raamat: Introduction to Nonparametric Statistics for the Biological Sciences Using R

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 06-Jul-2016
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319306346
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Introduction to Nonparametric Statistics for the Biological Sciences Using RSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 06-Jul-2016
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319306346
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This book contains a rich set of tools for nonparametric analyses, and the purpose of this supplemental text is to provide guidance to students and professional researchers onhow R is used for nonparametric data analysis in the biological sciences:To introduce when nonparametricapproaches to data analysis are appropriateTo introduce the leadingnonparametric tests commonly used in biostatistics and how R is used togenerate appropriate statistics for each testTo introduce common figurestypically associated with nonparametric data analysis and how R is used togenerate appropriate figures in support of each data setThe book focuses on how R is used todistinguish between data that could be classified as nonparametric as opposedto 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-conta

ined lessons on various analyses and tests using R to broadly compare differences between data sets and statistical approach.This supplemental text is intended for:Upper-level undergraduate and graduate students majoring in the biological sciences, specifically those in agriculture, biology, and health science - both students in lecture-type courses and also those engaged in research projects, such as a master"s thesis or a doctoral dissertationAnd biological researchers at the professional level without a nonparametric statistics background but who regularly work with data more suitable to a nonparametric approach to data analysis
1 Nonparametric Statistics for the Biological Sciences
1(50)
1.1 Background on This Lesson
1(1)
1.2 Data Types
2(3)
1.2.1 Nominal Data
3(1)
1.2.2 Ordinal Data
4(1)
1.2.3 Interval Data
4(1)
1.2.4 Ratio Data
5(1)
1.3 How R Syntax, R Output, and Graphics Show in This Text
5(1)
1.4 Graphical Presentation of Populations
6(5)
1.4.1 Samples that Exhibit Normal Distribution
7(2)
1.4.2 Samples That Fail to Exhibit Normal Distribution
9(2)
1.5 Rand Nonparametric Analyses
11(12)
1.5.1 Precision of Scales: Ordinal vs Interval
11(1)
1.5.2 Deviation from Normal Distribution
12(5)
1.5.3 Sample Size and Possible Issues with Representation
17(6)
1.6 Definition of Nonparametric Analysis
23(2)
1.7 Statistical Tests and Graphics Associated with Normal Distribution
25(5)
1.8 Addendum: Data Distribution and Sampling
30(20)
1.9 Prepare to Exit, Save, and Later Retrieve This R Session
50(1)
2 Sign Test
51(26)
2.1 Background on This Lesson
51(3)
2.1.1 Description of the Data
51(3)
2.1.2 Null Hypothesis (Ho)
54(1)
2.2 Data Entry by Copying Directly into a R Session
54(3)
2.3 Organize the Data and Display the Code Book
57(3)
2.4 Conduct a Visual Data Check
60(3)
2.5 Descriptive Analysis of the Data
63(10)
2.6 Conduct the Statistical Analysis
73(1)
2.7 Summary
74(2)
2.8 Prepare to Exit, Save, and Later Retrieve This R Session
76(1)
3 Chi-Square
77(26)
3.1 Background on This Lesson
77(3)
3.1.1 Description of the Data
78(2)
3.1.2 Null Hypothesis (Ho)
80(1)
3.2 Data Import of a .csv Spreadsheet-Type Data File into R
80(2)
3.3 Organize the Data and Display the Code Book
82(2)
3.4 Conduct a Visual Data Check
84(6)
3.5 Descriptive Analysis of the Data
90(2)
3.6 Conduct the Statistical Analysis
92(5)
3.7 Summary
97(3)
3.8 Addendum: Calculate the Chi-Square Statistic from Contingency Tables
100(2)
3.9 Prepare to Exit, Save, and Later Retrieve This R Session
102(1)
4 Mann--Whitney U Test
103(30)
4.1 Background on this Lesson
103(3)
4.1.1 Description of the Data
104(2)
4.1.2 Null Hypothesis (Ho)
106(1)
4.2 Data Import of a .csv Spreadsheet-Type Data File into R
106(2)
4.3 Organize the Data and Display the Code Book
108(3)
4.4 Conduct a Visual Data Check
111(7)
4.5 Descriptive Analysis of the Data
118(7)
4.6 Conduct the Statistical Analysis
125(3)
4.7 Summary
128(1)
4.8 Addendum: Stacked Data vs Unstacked Data
129(3)
4.9 Prepare to Exit, Save, and Later Retrieve this R Session
132(1)
5 Wilcoxon Matched-Pairs Signed-Ranks Test
133(44)
5.1 Background on this Lesson
134(3)
5.1.1 Description of the Data
134(2)
5.1.2 Null Hypothesis (Ho)
136(1)
5.2 Data Import of a .csv Spreadsheet-Type Data File into R
137(2)
5.3 Organize the Data and Display the Code Book
139(2)
5.4 Conduct a Visual Data Check
141(9)
5.5 Descriptive Analysis of the Data
150(8)
5.6 Conduct the Statistical Analysis
158(2)
5.7 Summary
160(3)
5.8 Addendum 1: Stacked Data and the Wilcoxon Matched-Pairs Signed-Ranks Test
163(4)
5.9 Addendum 2: Similar Functions from Different Packages
167(5)
5.10 Addendum 3: Nonparametric vs Parametric Confirmation of Outcomes
172(2)
5.11 Prepare to Exit, Save, and Later Retrieve this R Session
174(3)
6 Kruskal--Wallis H-Test for Oneway Analysis of Variance (ANOVA) by Ranks
177(36)
6.1 Background on this Lesson
178(3)
6.1.1 Description of the Data
178(3)
6.1.2 Null Hypothesis (Ho)
181(1)
6.2 Data Import of a .csv Spreadsheet-Type Data File into R
181(2)
6.3 Organize the Data and Display the Code Book
183(7)
6.4 Conduct a Visual Data Check
190(7)
6.5 Descriptive Analysis of the Data
197(9)
6.6 Conduct the Statistical Analysis
206(1)
6.7 Summary
207(1)
6.8 Addendum: Comparison of Kruskal--Wallis Test Differences by Multiple Breakout Groups
208(3)
6.9 Prepare to Exit, Save, and Later Retrieve this R Session
211(2)
7 Friedman Twoway Analysis of Variance (ANOVA) by Ranks
213(36)
7.1 Background on This Lesson
214(4)
7.1.1 Description of the Data
214(4)
7.1.2 Null Hypothesis (Ho)
218(1)
7.2 Data Import of a .csv Spreadsheet-Type Data File into R
218(2)
7.3 Organize the Data and Display the Code Book
220(3)
7.4 Conduct a Visual Data Check
223(7)
7.5 Descriptive Analysis of the Data
230(6)
7.6 Conduct the Statistical Analysis
236(3)
7.7 Summary
239(1)
7.8 Addendum: Similar Functions from External Packages
240(7)
7.9 Prepare to Exit, Save, and Later Retrieve This R Session
247(2)
8 Spearman's Rank-Difference Coefficient of Correlation
249(50)
8.1 Background on This Lesson
250(3)
8.1.1 Description of the Data
250(3)
8.1.2 Null Hypothesis (Ho)
253(1)
8.2 Data Import of a .csv Spreadsheet-Type Data File into R
253(1)
8.3 Organize the Data and Display the Code Book
254(7)
8.4 Conduct a Visual Data Check
261(14)
8.4.1 Use of the Graphics Package
262(7)
8.4.2 Use of the Lattice Package
269(3)
8.4.3 Use of the ggplot2 Package
272(3)
8.5 Descriptive Analysis of the Data
275(7)
8.6 Conduct the Statistical Analysis
282(12)
8.7 Summary
294(1)
8.8 Addendum: Kendall's Tau
295(2)
8.9 Prepare to Exit, Save, and Later Retrieve This R Session
297(2)
9 Other Nonparametric Tests for the Biological Sciences
299(28)
9.1 Binomial Test
300(3)
9.2 Walsh Test for Two Related Samples of Interval Data
303(5)
9.3 Kolmogorov-Smirnov (K-S) Two-Sample Test
308(4)
9.4 Binomial Logistic Regression
312(12)
9.5 Prepare to Exit, Save, and Later Retrieve This R Session
324(1)
9.6 Future Applications of Nonparametric Statistics
325(1)
9.7 Contact the Authors
326(1)
Index 327
Thomas W. MacFarland, Ed.D., is Associate Professor (Computer Technology) at Nova Southeastern University in Fort Lauderdale, Florida.  He joined the Graduate School of Computer and Information Sciences in 1988 and provides consulting services to the university community on research methods and statistical design as well as individual research on institutional concerns and assessment of student learning.  Dr. MacFarland's areas of research include institutional research, assessment of student learning outcomes, federal data resources, and K-12 computer science education. Jan Yates, Ph.D., is Associate Professor of Educational Media and Computer Science Education at Nova Southeastern University's Abraham S. Fischler College of Education in Fort Lauderdale, Florida. Since 2001, she has worked in the areas of curriculum development, program assessment and review, and accreditation.
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