生物統計学ガイド(第4版)<br>Biomeasurement : A Student's Guide to Biological Statistics (4 CSM)

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生物統計学ガイド(第4版)
Biomeasurement : A Student's Guide to Biological Statistics (4 CSM)

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  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 358 p.
  • 言語 ENG
  • 商品コード 9780198807483
  • DDC分類 570

Full Description


Statistical analysis allows us to attach meaning to data that we have collected; it helps us to understand what experimental results really mean, and to assess whether we can trust what experiments seem to be telling us. Yet, despite being a collection of the most valuable and important tools available to bioscientists, statistics is often the aspect of study most feared by students. Biomeasurement offers a refreshing, student-focused introduction to the use of statistics in the study of the biosciences. With an emphasis on why statistical techniques are essential tools for bioscientists, the book develops students' confidence to use and further explore the key techniques for themselves. Beginning by placing the role of data analysis in the context of the wider scientific method and introducing the student to the key terms and concepts common to all statistical tools, the book then guides the student through descriptive statistics, and on to inferential statistics, explaining how and why each type of technique is used, and what each can tell us in order to better understand our data. It goes on to present the key statistical tests, walking the student step-wise through the useof each, with carefully-integrated examples and plentiful opportunities for hands-on practice. The book closes with an overview of choosing the right test to suit your data, and tools for presenting data and their statistical analyses. Written by a talented educator, whose teaching has won praise from the UK's Quality Assurance Agency for Higher Education, Biomeasurement is sure to engage even the most wary of students, demonstrating the power and importance of statistics throughout the study of bioscience. Online resources: The online resources to accompany Biomeasurement include:For students:* Screencast walkthroughs for SPSS and R.* Online glossary and flashcard glossary.* Data sets, for use in statistical analysis software packages.* Help sheets offering concise guidance on key techniques and the use of statistical analysis software packages.* Interactive calculation sheets to help students carry out key statistical tests quickly and easily in Excel, without the need for other software.* Full-text versions of Literature Link articles from OUP Journals. For registered adopters of the book:* Additional exercises, to supplement those in the book, and suggested tutorial assignments.* Figures from the book, available for download.* PowerPoint presentation outlines for each chapter.

Table of Contents

Preface                                            vii
Acknowledgements ix
Using this book xxii
1 Why am I reading this book? 1 (11)
Book and Chapter Aims 1 (1)
1.1 My lecturer is a sadist! 1 (2)
1.2 Doing science: the big picture 3 (3)
1.2.1 Descriptive questions 3 (1)
1.2.2 Questions answered using a 4 (1)
research hypothesis
Stage 1 Developing research hypotheses 4 (1)
Stage 2 Generating predictions 5 (1)
Stage 3 Testing predictions 6 (1)
1.3 The process in practice 6 (1)
1.4 Essential skills for doing science 7 (2)
Analysing data 7 (1)
Developing hypotheses and predictions 7 (1)
Experimental design 8 (1)
Taking measurements 8 (1)
Critical evaluation 8 (1)
Health, safety, and ethical assessment 9 (1)
1.5 Types of data analysis 9 (1)
Checklist of key points 10 (1)
Self-help questions 11 (1)
2 Getting to grips with the basics 12 (18)
Chapter Aims 12 (1)
2.1 Populations and samples 12 (5)
2.1.1 The sampling process 13 (1)
Sample size 14 (1)
Replication and pseudoreplication 14 (1)
Random sampling and bias 15 (1)
2.1.2 Sample error 15 (2)
2.2 Variation and variables 17 (4)
2.2.1 Identifying variables 18 (1)
2.2.2 Dependent and independent 18 (1)
variables
2.2.3 Relationships and differences 19 (1)
2.2.4 Manipulated versus natural 20 (1)
variation in independent variables
2.2.5 Lack of independence between 21 (1)
variables
2.3 Understanding data 21 (5)
2.3.1 Differences: related and 22 (2)
unrelated data
2.3.2 Levels of measurement 24 (1)
Nominal (categories) 24 (1)
Ordinal (ranks) 24 (1)
Scale (counts and measures) 25 (1)
2.4 Demystifying formulae 26 (2)
2.4.1 Squiggles, lines, and letters 26 (1)
2.4.2 Doing things in order 27 (1)
Checklist of key points 28 (1)
Self-help questions 29 (1)
3 Describing a single sample 30 (29)
Chapter Aims 30 (1)
3.1 The single sample 30 (1)
3.2 Descriptive statistics 31 (6)
3.2.1 Central tendency 31 (1)
Mean (y) 31 (1)
Median 32 (1)
Mode 33 (1)
3.2.2 Variability 33 (1)
Range 33 (1)
Interquartile range 34 (1)
Variance (s2) 34 (3)
Standard deviation (s) 37 (1)
3.3 Frequency distributions 37 (4)
3.3.1 For nominal, ordinal, and 38 (1)
discrete scale data
3.3.2 For continuous scale data 39 (2)
3.4 The normal, and other, theoretical 41 (2)
distributions
3.4.1 Characteristics of the normal 42 (1)
distribution
3.4.2 Other distributions: binomial and 43 (1)
Poisson
3.5 Pies, boxes, and errors 43 (1)
35.1 Pie charts as alternatives to 43 (3)
frequency-distribution charts
3.5.2 Understanding boxplots 44 (1)
3.5.3 Introducing error bars 44 (2)
3.6 Example data: ranger patrol tusk 46 (1)
records
3.7 Worked example: using SPSS 47 (10)
3.7.1 Descriptive statistics and 47 (1)
frequency distributions
For nominal, ordinal, and discrete 48 (1)
scale data
For continuous scale data 49 (4)
3.7.2 Pie charts 53 (2)
3.7.3 Boxplots 55 (2)
Checklist of key points 57 (1)
Self-help questions 58 (1)
4 Inferring and estimating 59 (14)
Chapter Aims 59 (1)
4.1 Overview of inferential statistics 59 (2)
4.1.1 Why we need inferential 59 (1)
statistics---a reminder
4.1.2 Uncertainty and probability 60 (1)
4.2 Inferring through estimation 61 (5)
4.2.1 Standard error (of the mean, S-y) 62 (1)
4.2.2 Confidence intervals {of the mean) 63 (1)
4.2.3 Error bars revisited 64 (1)
4.2.4 Comparing samples 65 (1)
4.3 Example data: ground squirrels 66 (1)
4.4 Worked example: using SPSS 67 (4)
4.4.2 Errorplots 68 (3)
Checklist of key points 71 (1)
Self-help questions 72 (1)
5 Choosing the right test and graph 73 (22)
Chapter Aims 73 (1)
5.1 Using graphs 74 (2)
5.2 NHST and other options 76 (2)
5.3 Which NHST? 78 (3)
5.3.1 Tests of frequencies 79 (1)
5.3.2 Tests of relationship 80 (1)
5.3.3 Tests of difference 80 (1)
5.4 Worked examples: graphs with two 81 (12)
variables using SPSS
5.4.1 Frequency distributions 82 (1)
5.4.2 Pie charts 83 (2)
5.4.3 Scatterplots 85 (1)
5.4.4 Boxplots 86 (1)
Related samples 87 (1)
Unrelated samples 88 (2)
5.4.5 Errorplots 90 (1)
Related samples 90 (1)
Unrelated samples 91 (2)
Checklist of key points 93 (1)
Self-help questions 93 (2)
6 Overview of null hypothesis significance 95 (16)
testing
Chapter Aims 95 (1)
6.1 Four steps of null hypothesis 95 (5)
significance testing
6.1.1 Step 1: construct a (statistical) 96 (1)
null hypothesis (H0)
6.1.2 Step 2: decide on a critical 97 (1)
significance level (α)
6.1.3 Step 3: calculate your statistic 97 (1)
6.1.4 Step 4: reject or accept the null 98 (1)
hypothesis
Step 4 Using critical value tables 98 (1)
Step 4 Using P values on computer output 98 (2)
6.2 Parametric and nonparametric 100 (2)
6.2.1 Comparison of parametric and 100 (1)
nonparametric
6.2.2 Checking criteria for parametric 101 (1)
tests using the normal distribution
6.2.3 Choosing between parametric and 101 (1)
nonparametric
6.2.4 Transformation 102 (1)
6.3 One-and two-tailed tests 102 (1)
6.4 Effect sizes and their confidence 103 (2)
intervals
6.4.1 Uses of effect size 103 (1)
6.4.2 Ways of measuring effect size 104 (1)
6.5 Error and power 105 (3)
6.5.1 Type I and type II error 105 (1)
6.5.2 Statistical power 106 (1)
6.5.3 Power analyses: a priori and post 106 (1)
hoc
6.5.4 Implications for interpreting 107 (1)
your results
6.6 Criticism of NHST 108 (1)
Checklist of key points 109 (1)
Self-help questions 110 (1)
7 Tests on frequencies 111 (32)
Chapter Aims 111 (1)
Notes on symbols 111 (1)
7.1 Introduction to chi-square tests 111 (5)
7.1.1 Only use frequency data 112 (1)
7.1.2 Types of chi-square test 112 (2)
7.1.3 Sample size considerations 114 (1)
7.1.4 When to use chi-square with 114 (1)
caution
7.1.5 Alternatives to chi-square tests 115 (1)
7.2 Example data 116 (3)
7.2.1 One-way: Mendel's peas 116 (1)
7.2.2 Two way: Mikumi's elephants 117 (2)
7.3 One-way chi-square test 119 (11)
7.3.1 When to use 119 (1)
7.3.2 Four steps 120 (1)
Using critical value tables 120 (1)
Using P values on computer output 120 (1)
7.3.3 Worked example: by hand 121 (1)
With expected according to a 1:1:1:1 121 (1)
Mendelian ratio (test of homogeneity)
With expected according to a 9:3:3:1 122 (1)
Mendelian ratio
7.3.4 Worked example: using SPSS 123 (2)
With expected according to a 1:1:1:1 125 (2)
Mendelian ratio (test of homogeneity)
With expected according to a 9:3:3:1 127 (2)
Mendelian ratio
7.3.5 Literature link: weary lettuces 129 (1)
7.4 Two-way chi-square test 130 (10)
7.4.1 When to use 130 (1)
7.4.2 Tour steps 131 (1)
Using critical value tables 132 (1)
Using P values on computer output 132 (1)
7.4.3 Worked example: by hand 132 (2)
7.4.4 Worked example: using SPSS 134 (3)
7.4.3 Literature link: treatment 137 (3)
alliance
Checklist of key points 140 (1)
Self-help questions 141 (2)
8 Tests of difference: two unrelated samples 143 (23)
Chapter Aims 143 (1)
8.1 Introduction to the t- and 143 (3)
Mann-Whitney U tests
8.1.1 Variables and levels of 143 (1)
measurement needed
8.1.2 Comparison of t- and Mann-Whitney 144 (1)
U tests
8.1.3 The t-test and the parametric 144 (1)
criteria
8.1.4 Sample size considerations 145 (1)
8.1.5 Alternatives to t-and 146 (1)
Mann-Whitney U tests
8.2 Example data: dem bones 146 (2)
8.3 t-Test 148 (7)
8.3.1 When to use 148 (1)
8.3.2 Four steps of a t-test 149 (1)
Using critical value tables 149 (1)
Using P values on computer output 150 (1)
8.3.3 Worked example: by hand 150 (2)
8.3.4 Worked example: using SPSS 152 (2)
8.3.5 Literature link: silicon and 154 (1)
sorghum
8.4 Mann-Whitney U test 155 (8)
8.4.1 When to use 155 (1)
8.4.2 Four steps of a Mann-Whitney U 156 (1)
test
Using critical value tables 156 (1)
Using P values on computer output 157 (1)
8.4.3 Worked example: by hand 157 (1)
8.4.4 Worked example: using SPSS 158 (4)
8.4.5 Literature link: Alzheimer's 162 (1)
disease
Checklist of key points 163 (1)
Self-help questions 164 (2)
9 Tests of difference: two related samples 166 (23)
Chapter Aims 166 (1)
9.1 Introduction to paired t- and 166 (3)
Wilcoxon signed-rank tests
9.1.1 Variables and levels of 167 (1)
measurement needed
9.1.2 Comparison of paired t- and 167 (1)
Wilcoxon signed-rank tests
9.1.3 The paired t-test and the 168 (1)
parametric criteria
9.1.4 Sample size considerations 168 (1)
9.1.5 Alternatives to and extensions of 169 (1)
the paired t- and Wilcoxon signed-rank
tests
9.2 Example data; bighorn ewes 169 (3)
9.3 Paired t-test 172 (7)
9.3.1 When to use 172 (1)
9.3.2 Four steps of a paired t-test 172 (1)
Using critical value tables 173 (1)
Using P values on computer output 173 (1)
9.3.3 Worked example: by hand 173 (3)
9.3.4 Worked example: using SPSS 176 (1)
9.3.5 Literature link: slug slime 177 (2)
9.4 Wilcoxon signed-rank test 179 (8)
9.4.1 When to use 179 (1)
9.4.2 Four steps of a Wilcoxon 180 (1)
signed-rank test
Using critical value tables 181 (1)
Using P values on computer output 181 (1)
9.4.3 Worked example: by hand 182 (1)
9.4.4 Worked example: using SPSS 183 (2)
9.4.5 Literature link: head injuries 185 (2)
Checklist of key points 187 (1)
Self-help questions 187 (2)
10 Tests of difference: more than two 189 (22)
samples
Chapter Aims 189 (1)
10.1 Introduction to one-way and 189 (5)
Kruskal--Wallis tests
10.1.1 Variables and levels of 190 (1)
measurement needed
10.1.2 Comparison of one-way and 191 (1)
Kruskal--Wallis tests
10.1.3 One-way Anova and the parametric 191 (1)
criteria
10.1.4 Sample size considerations 192 (1)
10.1.5 Alternatives to and extensions 193 (1)
of one-way and Kruskal--Wallis tests
10.1.6 The language of Anova 193 (1)
10.1.7 Multiple comparisons 194 (1)
10.2 Example data: nitrogen levels in 194 (2)
reeds
10.3 One-way Anova test 196 (1)
10.3.1 When to use 197 (1)
10.32 Four steps of a one-way Anova 197 (5)
Using critical value tables 199 (1)
Using P values on computer output 199 (1)
10.3.3 Worked example: using SPSS 199 (2)
10.3.4 Literature link: running rats 201 (1)
10.4 Kruskal--Wallis test 202 (6)
10.4.1 When to use 203 (1)
10.4.2 Four steps of a Kruskal--Wallis 203 (1)
test
Using critical value tables 204 (1)
Using P values on computer output 204 (1)
10.4.3 Worked example: using, SPSS 205 (2)
10.4.4 Literature link: cooperating 207 (1)
long tailed tits
10.5 Model I and model II Anova 208 (1)
Checklist of key points 208 (1)
Self-help questions 209 (2)
11 Tests of relationship: regression 211 (20)
Chapter Aims 211 (1)
11.1 Introduction to bivariate linear 211 (3)
regression
11.1.1 Variables arid levels of 212 (1)
measurement needed
11.1.2 Linear model: scary-not! 213 (1)
11.13 The three regression questions 214 (3)
11.1.4 Added extras: how much is 214 (1)
explained, and prediction
11.1.5 Regression and the parametric 215 (1)
criteria
11.1.6 Sample size considerations 216 (1)
11.1.7 Alternatives to and extensions 217 (1)
of bivariate linear regression and Anova
11.2 Example data: species richness 217 (1)
11.3 Regression test 218 (1)
11.3.1 When to use 219 (1)
11.3.2 Four steps of a regression test 219 (1)
Using critical value tables 220 (1)
Using P values on computer output 220 (1)
11.3.3 Worked example: using SPSS for a 221 (2)
regression test
11.3.4 Worked example: using SPSS to 223 (2)
get the added extras
11.3.5 Reporting bivariate linear 225 (1)
regression results
11.3.6 Literature link: nodules 226 (1)
11.4 Model I and model II regression 227 (1)
Checklist of key points 228 (1)
Self-help questions 229 (2)
12 Tests of relationship: correlation 231 (22)
Chapter Aims 231 (1)
12.1 Introduction to the Pearson and 231 (6)
Spearman correlation tests
12.1.1 Variables and levels of 232 (2)
measurement needed
12.1.2 Comparison of Pearson's and 234 (1)
Spearman's tests
12.1.3 Pearson and the parametric 235 (1)
criteria
12.1.4 Sample size considerations 235 (1)
12.1.5 The correlation coefficient 236 (1)
12.1.6 Partial, multiple, and 236 (1)
multivariate correlation
12.2 Example data: eyeballs 237 (1)
12.3 Pearson correlation test 238 (6)
12.3.1 When to use 238 (2)
12.3.2 Four steps of a Pearson 240 (1)
correlation test
Using critical value tables 241 (1)
Using P values on computer output 241 (1)
12.3.3 Worked example: using SPSS 241 (2)
12.3.4 Literature link: male sacrifice 243 (1)
12.4 Spearman correlation test 244 (5)
12.4.1 When to use 245 (1)
12.4.2 Four steps of a Spearman 245 (1)
correlation test
Using critical value tables 246 (1)
Using P values on computer output 246 (1)
12.4.3 Worked example: using SPSS 246 (2)
12.4.4 Literature link: defoliating 248 (1)
ryegrass
12.5 Comparison of correlation and 249 (1)
regression
Checklist of key points 250 (1)
Self-help questions 251 (2)
13 Introducing the generalized linear 253 (37)
model: general linear model
Chapter Aims 253 (1)
Notes on terminology 253 (1)
13.1 Introduction to the general linear 254 (9)
model
13.1.1 Variables and levels of 255 (1)
measurement
13.1.2 The linear model revisited 256 (3)
13.1.3 The language of GLMs 259 (1)
13.1.4 Questions and extras 259 (1)
13.1.5 Types of sums of squares 260 (1)
13.1.6 Model assumptions and the 261 (1)
parametric criteria
Normality of error 261 (1)
Homogeneity of variance 261 (1)
Linearity 261 (2)
13.2 Example data: watered willows 263 (2)
13.3 General linear model 265 (14)
13.3.1 When to use 265 (1)
13.3.2 GLM and the four steps 266 (1)
13.3.3 Worked example: using SPSS 267 (1)
Looking at the model overall (answering 267 (2)
question 2a, Section 13.1.4)
Looking at individual explanatory 269 (2)
variables (answering question 2b,
Section 13.1.4)
Looking at R: (answering question 3, 271 (1)
Section 13.1.4)
Using coefficients (answering question 272 (2)
1, Section 13.1.4)
Using coefficients (making predictions) 274 (1)
Checking model assumptions 274 (1)
13.3.4 Reporting results from GLM 275 (3)
13.3.5 Literature link: brains and booze 278 (1)
13.4 Interaction 279 (3)
13.4.1 Worked example: using SPSS, 281 (1)
interaction
13.5 Random factors and mixed models 282 (1)
13.6 The multiple-model approach 283 (2)
13.6.1 Finding the best model 284 (1)
13.6.2 Reporting results from multiple 284 (1)
models
13.7 The general and generalized linear 285 (1)
models compared
Checklist of key points 285 (3)
Self-help questions 288 (2)
14 More on the generalized linear model: 290 (32)
logistic and loglinear models
Chapter Aims 290 (1)
14.1 Introduction to the logistic and 290 (4)
loglinear models
14.1.1 Variables and levels of 291 (1)
measurement
14.1.2 Link functions revisited 292 (1)
14.1.3 Questions and extras 293 (1)
14.1.4 Model assumptions and 293 (1)
overdispersion
14.2 Example data: urban birds 294 (1)
14.3 The binary logistic model 295 (12)
14.3.1 When to use 295 (1)
14.3.2 Binary logistic models and the 296 (1)
four steps
14.3.3 Worked example: using SPSS 296 (6)
Answering question 3 How good is the 302 (1)
model?
Answering question 2a Is the model 302 (1)
significant overall?
Answering question 2b Are individual 303 (1)
explanatory variables significant?
Answering question 1 What is the model? 304 (1)
Effect size 305 (1)
Checking for overdispersion 306 (1)
14.3.4 Literature link: death by AMI 306 (1)
14.4 The loglinear model 307 (11)
14.4.1 When to use 307 (1)
14.4.2 Loglinear models and the four 308 (1)
steps
14.4.3 Worked example: using SPSS 308 (5)
Answering question 3 How good is the 313 (1)
model?
Answering question 2a Is the model 313 (1)
significant overall?
Answering question 2b Are individual 314 (1)
explanatory variables significant?
Answering question 1 What is the model? 315 (1)
Effect size 316 (1)
Checking for overdispersion 317 (1)
14.4.4 Literature link: sea cows 317 (1)
14.5 The general, binary logistic, and 318 (1)
loglinear models compared
14.6 Alternatives and extensions 318 (2)
Checklist of key points 320 (1)
Self-help questions 321 (1)
Answers to self-help questions 322 (5)
Chapter 1 322 (1)
Chapter 2 322 (1)
Chapter 3 322 (1)
Chapter 4 323 (1)
Chapter 5 323 (1)
Chapter 6 324 (1)
Chapter 7 324 (1)
Chapter 8 324 (1)
Chapter 9 325 (1)
Chapter 10 325 (1)
Chapter 11 325 (1)
Chapter 12 325 (1)
Chapter 13 325 (1)
Chapter 14 326 (1)
Appendix I How to enter data into SPSS 327 (3)
Name 327 (1)
Type, width, and decimals 327 (1)
Label 327 (1)
Values 327 (1)
Missing 328 (1)
Columns 328 (1)
Align 328 (1)
Measure 328 (1)
Role 329 (1)
Appendix II Statistical tables of critical 330 (10)
values
Χ2 330 (1)
t 331 (1)
U 332 (2)
T 334 (2)
F 336 (1)
H 337 (1)
r 338 (1)
r5 339 (1)
Appendix III Summary guidance on reporting 340 (3)
statistical results
Descriptive statistics 340 (1)
Confidence intervals 340 (1)
Statistical tests 340 (2)
Effect size 342 (1)
Appendix IV Statistics and experimental design 343 (3)
Designs with control groups 343 (1)
Balanced and unbalanced design 343 (1)
Completely randomized designs 343 (1)
One-way or one-factor designs 343 (1)
Multi-way or multi-factor designs 344 (1)
Fully crossed designs 344 (1)
Incomplete designs 344 (1)
Blocking 344 (1)
Paired design 344 (1)
Covariate 344 (1)
Within-subject designs 345 (1)
Split-plot designs 345 (1)
Selected further reading 346 (3)
Next steps ... 346 (1)
For when you are feeling stronger ... 347 (1)
Online ... 348 (1)
References 349 (4)
Index 353