Introduction to statistical data analysis for the life sciences / Claus Thorn Ekstrøm, Biostatistics, Department of Public Health, University of Copenhagen, Helle Sørensen, Department of Mathematical Sciences, University of Copenhagen.

Format:

Book

Author/Creator:

Ekstrøm, Claus Thorn, 1971-

Contributor:

Sørensen, Helle, 1971-

Language:

English

Subjects (All):

Mathematical statistics--Textbooks.

Mathematical statistics.

Life sciences--Statistical methods.

Life sciences.

Life sciences--Statistical methods--Textbooks.

Genre:

Textbooks.

Physical Description:

xii, 514 pages : illustrations ; 24 cm

Edition:

Second edition.

Place of Publication:

Boca Raton : CRC Press, Taylor & Francis, [2015]

Summary:

Expanded with over 100 more pages, Introduction to Statistical Data Analysis for the Life Sciences, Second Edition presents the right balance of data examples, statistical theory, and computing to learn introductory statistics. This popular book covers the mathematics underlying classical statistical analysis, the modeling aspects of statistical analysis and the biological interpretation of results, and the application of statistical software in analyzing real-world problems and datasets. New to the Second Edition, A new chapter on non-linear regression models, A new chapter that contains examples of complete data analyses, illustrating how a full-fledged statistical analysis is undertaken, Additional exercises in most chapters, A summary of statistical formulas related to the specific designs used to teach the statistical concepts This text provides a computational toolbox that enables students to analyze real datasets and gain the confidence and skills to undertake more sophisticated analyses. Although accessible with any statistical software, the text encourages a reliance on R. For those new to R, an introduction to the software is available in an appendix. The book also includes end-of-chapter exercises as well as an entire chapter of case exercises that help students apply their knowledge to larger datasets and learn more about approaches specific to the life sciences. Book jacket.

Contents:

1 Description of samples and populations 1

1.1 Data types 2

1.2 Visualizing categorical data 4

1.3 Visualizing quantitative data 6

1.4 Statistical summaries 7

1.5 What is a probability? 16

1.6 R 17

1.7 Exercises 21

2 Linear regression 27

2.1 Fitting a regression line 29

2.2 When is linear regression appropriate? 34

2.3 The correlation coefficient 38

2.4 Perspective 41

2.5 R 43

2.6 Exercises 46

3 Comparison of groups 51

3.1 Graphical and simple numerical comparison 51

3.2 Between-group variation and within-group variation 54

3.3 Populations, samples, and expected values 55

3.4 Least squares estimation and residuals 56

3.5 Paired and unpaired samples 58

3.6 Perspective 60

3.7 R 62

3.8 Exercises 65

4 The normal distribution 69

4.1 Properties 69

4.2 One sample 80

4.3 Are the data (approximately) normally distributed? 82

4.4 The central limit theorem 88

4.5 R 91

4.6 Exercises 93

5 Statistical models, estimation, and confidence intervals 101

5.1 Statistical models 101

5.2 Estimation 108

5.3 Confidence intervals 117

5.4 Unpaired samples with different standard deviations 129

5.5 R 131

5.6 Exercises 140

6 Hypothesis tests 149

6.1 Null hypotheses 153

6.2 f-tests 157

6.3 Tests in a one-way ANOVA 163

6.4 Hypothesis tests as comparison of nested models 170

6.5 Type I and type II errors 172

6.6 R 176

6.7 Exercises 181

7 Model validation and prediction 191

7.1 Model validation 191

7.2 Prediction 201

7.3 R 207

7.4 Exercises 209

8 Linear normal models 217

8.1 Multiple linear regression 217

8.2 Additive two-way analysis of variance 224

8.3 Linear models 234

8.4 Interactions between variables 243

8.5 R 255

8.6 Exercises 262

9 Non-linear regression 269

9.1 Non-linear regression models 270

9.2 Estimation, confidence intervals, and hypothesis tests 272

9.3 Model validation 277

9.4 R 283

9.5 Exercises 286

10 Probabilities 291

10.1 Outcomes, events, and probabilities 291

10.2 Conditional probabilities 295

10.3 Independence 298

10.4 Exercises 302

11 The binomial distribution 307

11.1 The independent trials model 307

11.2 The binomial distribution 308

11.3 Estimation, confidence intervals, and hypothesis tests 316

11.4 Differences between proportions 321

11.5 R 323

11.6 Exercises 326

12 Analysis of count data 329

12.1 The chi-square test for goodness-of-fit 329

12.2 2x2 contingency table 334

12.3 Two-sided contingency tables 344

12.4 R 346

12.5 Exercises 350

13 Logistic regression 355

13.1 Odds and odds ratios 355

13.2 Logistic regression models 357

13.3 Estimation and confidence Intervals 362

13.4 Hypothesis tests 364

13.5 Model validation and prediction 367

13.6 R 371

13.7 Exercises 379

14 Statistical analysis examples 387

14.1 Water temperature and frequency of electric signals from electric eels 388

14.2 Association between listeria growth and RIP2 protein 393

14.3 Degradation of dioxin 400

14.4 Effect of an inhibitor on the chemical reaction rate 406

14.5 Birthday bulge on the Danish soccer team 413

14.6 Animal welfare 419

14.7 Monitoring herbicide efficacy 421

15 Case exercises 427

Case 1 Linear modeling 428

Case 2 Data transformations 430

Case 3 Two sample comparisons 432

Case 4 Linear regression with and without intercept 434

Case 5 Analysis of variance and test for linear trend 435

Case 6 Regression modeling and transformations 438

Case 7 Linear models 440

Case 8 Binary variables 442

Case 9 Agreement 446

Case 10 Logistic regression 449

Case 11 Non-linear regression 451

Case 12 Power and sample size calculations 452.

Notes:

"A Chapman & Hall book."

Includes bibliographical references (pages 501-506) and index.

ISBN:

9781482238938

1482238934

OCLC:

890127833