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El. knyga: Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences

(University of Bath, UK; National University of Ireland (NUI), Galway, Ireland)
  • Formatas: PDF+DRM
  • Išleidimo metai: 04-Apr-2022
  • Leidėjas: Wiley-Blackwell
  • Kalba: eng
  • ISBN-13: 9781119437673
Kitos knygos pagal šią temą:
Experimental Design and Statistical Analysis for Pharmacology and the Biomedical SciencesDidesnis paveikslėlis
  • Formatas: PDF+DRM
  • Išleidimo metai: 04-Apr-2022
  • Leidėjas: Wiley-Blackwell
  • Kalba: eng
  • ISBN-13: 9781119437673
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Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences A practical guide to the use of basic principles of experimental design and statistical analysis in pharmacology

Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences provides clear instructions on applying statistical analysis techniques to pharmacological data. Written by an experimental pharmacologist with decades of experience teaching statistics and designing preclinical experiments, this reader-friendly volume explains the variety of statistical tests that researchers require to analyze data and draw correct conclusions.

Detailed, yet accessible, chapters explain how to determine the appropriate statistical tool for a particular type of data, run the statistical test, and analyze and interpret the results. By first introducing basic principles of experimental design and statistical analysis, the author then guides readers through descriptive and inferential statistics, analysis of variance, correlation and regression analysis, general linear modelling, and more. Lastly, throughout the textbook are numerous examples from molecular, cellular, in vitro, and in vivo pharmacology which highlight the importance of rigorous statistical analysis in real-world pharmacological and biomedical research.

This textbook also:





Describes the rigorous statistical approach needed for publication in scientific journals Covers a wide range of statistical concepts and methods, such as standard normal distribution, data confidence intervals, and post hoc and a priori analysis Discusses practical aspects of data collection, identification, and presentation Features images of the output from common statistical packages, including GraphPad Prism, Invivo Stat, MiniTab and SPSS

Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences is an invaluable reference and guide for undergraduate and graduate students, post-doctoral researchers, and lecturers in pharmacology and allied subjects in the life sciences.
Acknowledgements ix
Foreword xi
1 Introduction 2(4)
2 So, what are data? 6(2)
3 Numbers; counting and measuring, precision, and accuracy 8(6)
4 Data collection: sampling and populations, different types of data, data distributions 14(6)
5 Descriptive statistics; measures to describe and summarise data sets 20 (10)
6 Testing for normality and transforming skewed data sets 30(8)
7 The Standard Normal Distribution 38(4)
8 Non-parametric descriptive statistics 42(4)
9 Summary of descriptive statistics: so, what values may I use to describe my data? 46(12)
10 Introduction to inferential statistics 58(8)
11 Comparing two sets of data - independent t-test 66(8)
12 Comparing two sets of data - Paired t-test 74(4)
13 Comparing two sets of data - independent non-parametric data 78(6)
14 Comparing two sets of data - paired non-parametric data 84(6)
15 Parametric one-way analysis of variance 90(14)
16 Repeated measure analysis of variance 104(10)
17 Complex Analysis of Variance Models 114(22)
18 Non-parametric ANOVA 136(16)
19 Correlation analysis 152(14)
20 Regression analysis 166(12)
21 Chi-square analysis 178(12)
22 Confidence intervals 190(4)
23 Permutation test of exact inference 194(2)
24 General Linear Model 196(4)
A Data distribution: probability mass function and probability density functions 200(8)
A.1 Binomial distribution: Probability mass function
200(2)
A.2 Exponential distribution: Probability density function
202(1)
A.3 Normal distribution: Probability density function
203(1)
A.4 Chi-square distribution: Probability density function
204(1)
A.5 Student t-distribution: Probability density function
205(1)
A.6 F distribution: Probability density function
206(2)
B Standard normal probabilities 208(4)
C Critical values of the t-distribution 212(2)
D Critical values of the Mann-Whitney U-statistic 214(4)
E Critical values of the Fdistribution 218(4)
F Critical values of chi-square distribution 222(2)
G Critical z values for multiple non-parametric pairwise comparisons 224(2)
H Critical values of correlation coefficients 226(4)
Index 230
Dr Paul J. Mitchell is Senior Lecturer and Associate Professor in the Department of Pharmacy and Pharmacology, University of Bath, UK, and Adjunct Lecturer in the Department of Pharmacology and Therapeutics, National University of Ireland (NUI), Galway, Ireland. He has more than 45 years experience in experimental pharmacology, experimental design, and statistical analysis. For the last 25 years Dr Mitchell has collaborated with colleagues to develop a coherent strategy to teach experimental design and statistical analysis to undergraduate and graduate students across subject areas.
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