Essential statistics for the pharmaceutical sciences / Philip Rowe.
By: Rowe, Philip.
Material type:
BookPublisher: Chichester, England ; Hoboken, NJ : John Wiley & Sons, c2007Description: xx, 287 p. : ill. ; 25 cm.ISBN: 9780470034682; 9780470034705.Subject(s): Drugs -- Research -- Statistical methods | Pharmacy -- Statistical methods | Pharmacology -- Statistical methods | Pharmacology -- methods | Health and Wellbeing | Statistics
| Medical equipment & techniques | Medical specialties, branches of medicine | Mathematics | Probability & statistics | Chemistry | Biology, life sciences | Epidemiology & medical statistics | PharmacologyDDC classification: 615.1072 ROW | Item type | Current library | Call number | Copy number | Status | Barcode | |
|---|---|---|---|---|---|---|
| General lending | MTU Kerry North Campus Library First Floor Main | 615.1072 ROW (Browse shelf(Opens below)) | 1 | Available | 38888000785430 |
Enhanced descriptions from Syndetics:
"... this text takes a novel approach... The style... is not asdry as other statistics texts, and so should not be intimidatingeven to a relative newcomer to the subject... The layout is easy tonavigate, there are chapter aims, summaries and ?key pointboxes? throughout." -The Pharmaceutical Journal, 2008
This text is a clear, accessible introduction to the keystatistical techniques employed for the analysis of data withinthis subject area. Written in a concise and logical manner, thebook explains why statistics are necessary and discusses the issuesthat experimentalists need to consider. The reader is carefullytaken through the whole process, from planning an experiment tointerpreting the results, avoiding unnecessary calculationmethodology. The most commonly used statistical methods aredescribed in terms of their purpose, when they should be used andwhat they mean once they have been performed.
Numerous examples are provided throughout the text, all within apharmaceutical context, with key points highlighted in summaryboxes to aid student understanding.
Essential Statistics for the Pharmaceutical Sciences takes a new and innovative approach to statistics with an informalstyle that will appeal to the reader who finds statistics achallenge!
This book is an invaluable introduction to statistics for anyscience student. It is an essential text for students takingbiomedical or pharmaceutical-based science degrees and also auseful guide for researchers.
Includes bibliographical references and index.
Pt. 1. Data types -- 1. Data types -- Pt. 2. Interval-scale data -- 2. Descriptive statistics -- 3. The normal distribution -- 4. Sampling from populations -- the SEM -- 5. Ninety-five per cent confidence interval for the mean -- 6. The two-sample t-test (1). Introducing hypothesis tests -- 7. The two-sample t-test (2). The dreaded P value -- 8. The two-sample t-test (3). False negatives, power and necessary sample sizes -- 9. The two-sample t-test (4). Statistical significance, practical significance and equivalence -- 10. The two-sample t-test (5). One-sided testing -- 11. What does a statistically significant result really tell us? -- 12. The paired t-test -- comparing two related sets of measurements -- 13. Analyses of variance -- going beyond t-tests -- 14. Correlation and regression -- relationships between measured values -- Pt. 3. Nominal-scale data -- 15. Describing categorized data -- 16. Comparing observed proportions -- the contingency chi-square test -- Pt. 4. Ordinal-scale data -- 17. Ordinal and non-normally distributed data. Transformations and non-parametric tests -- Pt. 5. Some challenges from the real world -- 18. Multiple testing -- 19. Questionnaires -- Pt. 6. Conclusions -- 20. Conclusions.
A clear and accessible introduction to the key statistical techniques employed for the analysis of data within the pharamceutical sciences, this text explains why statistics are necessary and discusses the issues that experimentalists need to consider.
Table of contents provided by Syndetics
- Preface
- Statistical packages
- Part 1 Data Types
- 1 Data types
- 1.1 Does it really matter?
- 1.2 Interval scale data
- 1.3 Ordinal scale data
- 1.4 Nominal scale data
- 1.5 Structure of this book
- 1.6 Chapter summary
- Part 2 Interval-Scale Data
- 2 Descriptive statistics
- 2.1 Summarizing data sets
- 2.2 Indicators of central tendency. mean, median and mode
- 2.3 Describing variability. standard deviation and coefficient of variation
- 2.4 Quartiles. another way to describe data
- 2.5 Using computer packages to generate descriptive statistics
- 2.6 Chapter summary
- 3 The normal distribution
- 3.1 What is a normal distribution?
- 3.2 Identifying data that are not normally distributed
- 3.3 Proportions of individuals within one or two standard deviations of the mean
- 3.4 Chapter summary
- 4 Sampling from populations. the SEM
- 4.1 Samples and populations
- 4.2 From sample to population
- 4.3 Types of sampling error
- 4.4 What factors control the extent of random sampling error?
- 4.5 Estimating likely sampling error. The SEM
- 4.6 Offsetting sample size against standard deviation
- 4.7 Chapter summary
- 5 Ninety-five per cent confidence interval for the mean
- 5.1 What is a confidence interval?
- 5.2 How wide should the interval be?
- 5.3 What do we mean by '95 per cent' confidence?
- 5.4 Calculating the interval width
- 5.5 A long series of samples and 95 per cent confidence intervals
- 5.6 How sensitive is the width of the confidence interval to changes in the SD, the sample size or the required level of confidence?
- 5.7 Two statements
- 5.8 One-sided 95 per cent confidence intervals
- 5.9 The 95 per cent confidence interval for the difference between two treatments
- 5.10 The need for data to follow a normal distribution and data transformation
- 5.11 Chapter summary
- 6 The two-sample t-test(1).Introducing hypothesis tests
- 6.1 The two-sample t-test. an example of a hypothesis test
- 6.2 'Significance'
- 6.3 The risk of a false positive finding
- 6.4 What factors will influence whether or not we obtain a significant outcome?
- 6.5 Requirements for applying a two-sample t-test
- 6.6 Chapter summary
- 7 The two-sample t-test(2).The dreaded P value
- 7.1 Measuring how significant a result is
- 7.2 P values
- 7.3 Two ways to define significance?
- 7.4 Obtaining the P value
- 7.5 P values or 95 per cent confidence intervals?
- 7.6 Chapter summary
- 8 The two-sample t-test(3).False negatives, power and necessary sample sizes
- 8.1 What else could possibly go wrong?
- 8.2 Power
- 8.3 Calculating necessary sample size
- 8.4 Chapter summary
- 9 The two-sample t-test(4).Statistical significance, practical significance and equivalence
- 9.1 Practical significance. is the difference big enough to matter?
- 9.2 Equivalence testing
- 9.3 Non-inferiority testing
- 9.4 P values are less informative and can be positively misleading
- 9.5 Setting equivalence limits prior to experimentation
- 9.6 Chapter summary
- 10 The two-sample t-test(5).One-sided testing
- 10.1 Looking for a change in a specified direction
- 10.2 Protection against false positives
- 10.3 Temptation!
- 10.4 Using a computer package to carry out a one-sided test
- 10.5 Should one-sided tests be used more commonly?
- 10.5 Chapter summary
- 11 What does a statistically significant result really tell us?
- 11.1 Interpreting statistical significance
- 11.2 Starting from extreme scepticism
- 11.3 Chapter summary
- 12 The paired t-test. comparing two related s
Author notes provided by Syndetics
Dr Philip Rowe. Reader in Pharmaceutical Computing, School of Pharmacy and Chemistry, Liverpool,?UK
In addition to Dr Rowe's teaching and research at LJMU he also works on a consultancy basis offering advice and assistance with pharmacokinetic or general data analysis problems for the pharmaceutical industry, professional organizations and hospitals. He has recently secured a post delivering statistics training for the Institute of Clinical Research.