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Essential Statistics for the Pharmaceutical Sciences is targeted at all those involved in research in pharmacology, pharmacy or other areas of pharmaceutical science; everybody from undergraduate project students to experienced researchers should find the material they need.
This book will guide all those who are not specialist statisticians in using sound statistical principles throughout the whole journey of a research project - designing the work, selecting appropriate statistical methodology and correctly interpreting the results. It deliberately avoids detailed calculation methodology. Its key features are friendliness and clarity. All methods are illustrated with realistic examples from within pharmaceutical science.
This edition now includes expanded coverage of some of the topics included in the first edition and adds some new topics relevant to pharmaceutical research.
* a clear, accessible introduction to the key statistical techniques used within the pharmaceutical sciences
* all examples set in relevant pharmaceutical contexts.
* key points emphasised in summary boxes and warnings of potential abuses in 'pirate boxes'.
* supplementary material - full data sets and detailed instructions for carrying out analyses using packages such as SPSS or Minitab - provided at [...]
An invaluable introduction to statistics for any science student and an essential text for all those involved in pharmaceutical research at whatever level.
This book will guide all those who are not specialist statisticians in using sound statistical principles throughout the whole journey of a research project - designing the work, selecting appropriate statistical methodology and correctly interpreting the results. It deliberately avoids detailed calculation methodology. Its key features are friendliness and clarity. All methods are illustrated with realistic examples from within pharmaceutical science.
This edition now includes expanded coverage of some of the topics included in the first edition and adds some new topics relevant to pharmaceutical research.
* a clear, accessible introduction to the key statistical techniques used within the pharmaceutical sciences
* all examples set in relevant pharmaceutical contexts.
* key points emphasised in summary boxes and warnings of potential abuses in 'pirate boxes'.
* supplementary material - full data sets and detailed instructions for carrying out analyses using packages such as SPSS or Minitab - provided at [...]
An invaluable introduction to statistics for any science student and an essential text for all those involved in pharmaceutical research at whatever level.
Essential Statistics for the Pharmaceutical Sciences is targeted at all those involved in research in pharmacology, pharmacy or other areas of pharmaceutical science; everybody from undergraduate project students to experienced researchers should find the material they need.
This book will guide all those who are not specialist statisticians in using sound statistical principles throughout the whole journey of a research project - designing the work, selecting appropriate statistical methodology and correctly interpreting the results. It deliberately avoids detailed calculation methodology. Its key features are friendliness and clarity. All methods are illustrated with realistic examples from within pharmaceutical science.
This edition now includes expanded coverage of some of the topics included in the first edition and adds some new topics relevant to pharmaceutical research.
* a clear, accessible introduction to the key statistical techniques used within the pharmaceutical sciences
* all examples set in relevant pharmaceutical contexts.
* key points emphasised in summary boxes and warnings of potential abuses in 'pirate boxes'.
* supplementary material - full data sets and detailed instructions for carrying out analyses using packages such as SPSS or Minitab - provided at [...]
An invaluable introduction to statistics for any science student and an essential text for all those involved in pharmaceutical research at whatever level.
This book will guide all those who are not specialist statisticians in using sound statistical principles throughout the whole journey of a research project - designing the work, selecting appropriate statistical methodology and correctly interpreting the results. It deliberately avoids detailed calculation methodology. Its key features are friendliness and clarity. All methods are illustrated with realistic examples from within pharmaceutical science.
This edition now includes expanded coverage of some of the topics included in the first edition and adds some new topics relevant to pharmaceutical research.
* a clear, accessible introduction to the key statistical techniques used within the pharmaceutical sciences
* all examples set in relevant pharmaceutical contexts.
* key points emphasised in summary boxes and warnings of potential abuses in 'pirate boxes'.
* supplementary material - full data sets and detailed instructions for carrying out analyses using packages such as SPSS or Minitab - provided at [...]
An invaluable introduction to statistics for any science student and an essential text for all those involved in pharmaceutical research at whatever level.
Inhaltsverzeichnis
Preface xiii
Statistical packages xix
About the Website xxi
PART 1: PRESENTING DATA 1
1 Data types 3
1.1 Does it really matter? 3
1.2 Interval scale data 4
1.3 Ordinal scale data 4
1.4 Nominal scale data 5
1.5 Structure of this book 6
1.6 Chapter summary 6
2 Data presentation 7
2.1 Numerical tables 8
2.2 Bar charts and histograms 9
2.3 Pie charts 14
2.4 Scatter plots 16
2.5 Pictorial symbols 21
2.6 Chapter summary 22
PART 2: INTERVAL-SCALE DATA 23
3 Descriptive statistics for interval scale data 25
3.1 Summarising data sets 25
3.2 Indicators of central tendency: Mean, median and mode 26
3.3 Describing variability - Standard deviation and coefficient of variation 33
3.4 Quartiles - Another way to describe data 36
3.5 Describing ordinal data 40
3.6 Using computer packages to generate descriptive statistics 43
3.7 Chapter summary 45
4 The normal distribution 47
4.1 What is a normal distribution? 47
4.2 Identifying data that are not normally distributed 48
4.3 Proportions of individuals within 1SD or 2SD of the mean 52
4.4 Skewness and kurtosis 54
4.5 Chapter summary 57
4.6 Appendix: Power, sample size and the problem of attempting to test for a normal distribution 58
5 Sampling from populations. The standard error of the mean 63
5.1 Samples and populations 63
5.2 From sample to population 65
5.3 Types of sampling error 65
5.4 What factors control the extent of random sampling error when estimating a population mean? 68
5.5 Estimating likely sampling error - The SEM 70
5.6 Offsetting sample size against SD 74
5.7 Chapter summary 75
6 95% Confidence Interval for the Mean and Data Transformation 77
6.1 What is a confidence interval? 78
6.2 How wide should the interval be? 78
6.3 What do we mean by '95%' confidence? 79
6.4 Calculating the interval width 80
6.5 A long series of samples and 95% C.I.s 81
6.6 How sensitive is the width of the C.I. to changes in the SD, the sample size or the required level of confidence? 82
6.7 Two statements 85
6.8 One-sided 95% C.I.s 85
6.9 The 95% C.I. for the difference between two treatments 88
6.10 The need for data to follow a normal distribution and data transformation 90
6.11 Chapter summary 94
7 The two-sample t-test (1): Introducing hypothesis tests 95
7.1 The two-sample t-test - an example of an hypothesis test 96
7.2 Significance 103
7.3 The risk of a false positive finding 104
7.4 What aspects of the data will influence whether or not we obtain a significant outcome? 106
7.5 Requirements for applying a two-sample t-test 108
7.6 Performing and reporting the test 109
7.7 Chapter summary 110
8 The two?]sample t-test (2): The dreaded P value 111
8.1 Measuring how significant a result is 111
8.2 P values 112
8.3 Two ways to define significance? 113
8.4 Obtaining the P value 113
8.5 P values or 95% confidence intervals? 114
8.6 Chapter summary 115
9 The two-sample t-test (3): False negatives, power and necessary sample sizes 117
9.1 What else could possibly go wrong? 118
9.2 Power 119
9.3 Calculating necessary sample size 122
9.4 Chapter summary 130
10
Statistical packages xix
About the Website xxi
PART 1: PRESENTING DATA 1
1 Data types 3
1.1 Does it really matter? 3
1.2 Interval scale data 4
1.3 Ordinal scale data 4
1.4 Nominal scale data 5
1.5 Structure of this book 6
1.6 Chapter summary 6
2 Data presentation 7
2.1 Numerical tables 8
2.2 Bar charts and histograms 9
2.3 Pie charts 14
2.4 Scatter plots 16
2.5 Pictorial symbols 21
2.6 Chapter summary 22
PART 2: INTERVAL-SCALE DATA 23
3 Descriptive statistics for interval scale data 25
3.1 Summarising data sets 25
3.2 Indicators of central tendency: Mean, median and mode 26
3.3 Describing variability - Standard deviation and coefficient of variation 33
3.4 Quartiles - Another way to describe data 36
3.5 Describing ordinal data 40
3.6 Using computer packages to generate descriptive statistics 43
3.7 Chapter summary 45
4 The normal distribution 47
4.1 What is a normal distribution? 47
4.2 Identifying data that are not normally distributed 48
4.3 Proportions of individuals within 1SD or 2SD of the mean 52
4.4 Skewness and kurtosis 54
4.5 Chapter summary 57
4.6 Appendix: Power, sample size and the problem of attempting to test for a normal distribution 58
5 Sampling from populations. The standard error of the mean 63
5.1 Samples and populations 63
5.2 From sample to population 65
5.3 Types of sampling error 65
5.4 What factors control the extent of random sampling error when estimating a population mean? 68
5.5 Estimating likely sampling error - The SEM 70
5.6 Offsetting sample size against SD 74
5.7 Chapter summary 75
6 95% Confidence Interval for the Mean and Data Transformation 77
6.1 What is a confidence interval? 78
6.2 How wide should the interval be? 78
6.3 What do we mean by '95%' confidence? 79
6.4 Calculating the interval width 80
6.5 A long series of samples and 95% C.I.s 81
6.6 How sensitive is the width of the C.I. to changes in the SD, the sample size or the required level of confidence? 82
6.7 Two statements 85
6.8 One-sided 95% C.I.s 85
6.9 The 95% C.I. for the difference between two treatments 88
6.10 The need for data to follow a normal distribution and data transformation 90
6.11 Chapter summary 94
7 The two-sample t-test (1): Introducing hypothesis tests 95
7.1 The two-sample t-test - an example of an hypothesis test 96
7.2 Significance 103
7.3 The risk of a false positive finding 104
7.4 What aspects of the data will influence whether or not we obtain a significant outcome? 106
7.5 Requirements for applying a two-sample t-test 108
7.6 Performing and reporting the test 109
7.7 Chapter summary 110
8 The two?]sample t-test (2): The dreaded P value 111
8.1 Measuring how significant a result is 111
8.2 P values 112
8.3 Two ways to define significance? 113
8.4 Obtaining the P value 113
8.5 P values or 95% confidence intervals? 114
8.6 Chapter summary 115
9 The two-sample t-test (3): False negatives, power and necessary sample sizes 117
9.1 What else could possibly go wrong? 118
9.2 Power 119
9.3 Calculating necessary sample size 122
9.4 Chapter summary 130
10
Details
| Erscheinungsjahr: | 2015 |
|---|---|
| Genre: | Chemie |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: | 432 S. |
| ISBN-13: | 9781118913390 |
| ISBN-10: | 1118913396 |
| Sprache: | Englisch |
| Herstellernummer: | 1W118913390 |
| Autor: | Rowe, Philip |
| Auflage: | 2. Aufl. |
| Hersteller: | Wiley & Sons |
| Maße: | 244 x 172 x 20 mm |
| Von/Mit: | Philip Rowe |
| Erscheinungsdatum: | 02.10.2015 |
| Gewicht: | 0,652 kg |
Inhaltsverzeichnis
Preface xiii
Statistical packages xix
About the Website xxi
PART 1: PRESENTING DATA 1
1 Data types 3
1.1 Does it really matter? 3
1.2 Interval scale data 4
1.3 Ordinal scale data 4
1.4 Nominal scale data 5
1.5 Structure of this book 6
1.6 Chapter summary 6
2 Data presentation 7
2.1 Numerical tables 8
2.2 Bar charts and histograms 9
2.3 Pie charts 14
2.4 Scatter plots 16
2.5 Pictorial symbols 21
2.6 Chapter summary 22
PART 2: INTERVAL-SCALE DATA 23
3 Descriptive statistics for interval scale data 25
3.1 Summarising data sets 25
3.2 Indicators of central tendency: Mean, median and mode 26
3.3 Describing variability - Standard deviation and coefficient of variation 33
3.4 Quartiles - Another way to describe data 36
3.5 Describing ordinal data 40
3.6 Using computer packages to generate descriptive statistics 43
3.7 Chapter summary 45
4 The normal distribution 47
4.1 What is a normal distribution? 47
4.2 Identifying data that are not normally distributed 48
4.3 Proportions of individuals within 1SD or 2SD of the mean 52
4.4 Skewness and kurtosis 54
4.5 Chapter summary 57
4.6 Appendix: Power, sample size and the problem of attempting to test for a normal distribution 58
5 Sampling from populations. The standard error of the mean 63
5.1 Samples and populations 63
5.2 From sample to population 65
5.3 Types of sampling error 65
5.4 What factors control the extent of random sampling error when estimating a population mean? 68
5.5 Estimating likely sampling error - The SEM 70
5.6 Offsetting sample size against SD 74
5.7 Chapter summary 75
6 95% Confidence Interval for the Mean and Data Transformation 77
6.1 What is a confidence interval? 78
6.2 How wide should the interval be? 78
6.3 What do we mean by '95%' confidence? 79
6.4 Calculating the interval width 80
6.5 A long series of samples and 95% C.I.s 81
6.6 How sensitive is the width of the C.I. to changes in the SD, the sample size or the required level of confidence? 82
6.7 Two statements 85
6.8 One-sided 95% C.I.s 85
6.9 The 95% C.I. for the difference between two treatments 88
6.10 The need for data to follow a normal distribution and data transformation 90
6.11 Chapter summary 94
7 The two-sample t-test (1): Introducing hypothesis tests 95
7.1 The two-sample t-test - an example of an hypothesis test 96
7.2 Significance 103
7.3 The risk of a false positive finding 104
7.4 What aspects of the data will influence whether or not we obtain a significant outcome? 106
7.5 Requirements for applying a two-sample t-test 108
7.6 Performing and reporting the test 109
7.7 Chapter summary 110
8 The two?]sample t-test (2): The dreaded P value 111
8.1 Measuring how significant a result is 111
8.2 P values 112
8.3 Two ways to define significance? 113
8.4 Obtaining the P value 113
8.5 P values or 95% confidence intervals? 114
8.6 Chapter summary 115
9 The two-sample t-test (3): False negatives, power and necessary sample sizes 117
9.1 What else could possibly go wrong? 118
9.2 Power 119
9.3 Calculating necessary sample size 122
9.4 Chapter summary 130
10
Statistical packages xix
About the Website xxi
PART 1: PRESENTING DATA 1
1 Data types 3
1.1 Does it really matter? 3
1.2 Interval scale data 4
1.3 Ordinal scale data 4
1.4 Nominal scale data 5
1.5 Structure of this book 6
1.6 Chapter summary 6
2 Data presentation 7
2.1 Numerical tables 8
2.2 Bar charts and histograms 9
2.3 Pie charts 14
2.4 Scatter plots 16
2.5 Pictorial symbols 21
2.6 Chapter summary 22
PART 2: INTERVAL-SCALE DATA 23
3 Descriptive statistics for interval scale data 25
3.1 Summarising data sets 25
3.2 Indicators of central tendency: Mean, median and mode 26
3.3 Describing variability - Standard deviation and coefficient of variation 33
3.4 Quartiles - Another way to describe data 36
3.5 Describing ordinal data 40
3.6 Using computer packages to generate descriptive statistics 43
3.7 Chapter summary 45
4 The normal distribution 47
4.1 What is a normal distribution? 47
4.2 Identifying data that are not normally distributed 48
4.3 Proportions of individuals within 1SD or 2SD of the mean 52
4.4 Skewness and kurtosis 54
4.5 Chapter summary 57
4.6 Appendix: Power, sample size and the problem of attempting to test for a normal distribution 58
5 Sampling from populations. The standard error of the mean 63
5.1 Samples and populations 63
5.2 From sample to population 65
5.3 Types of sampling error 65
5.4 What factors control the extent of random sampling error when estimating a population mean? 68
5.5 Estimating likely sampling error - The SEM 70
5.6 Offsetting sample size against SD 74
5.7 Chapter summary 75
6 95% Confidence Interval for the Mean and Data Transformation 77
6.1 What is a confidence interval? 78
6.2 How wide should the interval be? 78
6.3 What do we mean by '95%' confidence? 79
6.4 Calculating the interval width 80
6.5 A long series of samples and 95% C.I.s 81
6.6 How sensitive is the width of the C.I. to changes in the SD, the sample size or the required level of confidence? 82
6.7 Two statements 85
6.8 One-sided 95% C.I.s 85
6.9 The 95% C.I. for the difference between two treatments 88
6.10 The need for data to follow a normal distribution and data transformation 90
6.11 Chapter summary 94
7 The two-sample t-test (1): Introducing hypothesis tests 95
7.1 The two-sample t-test - an example of an hypothesis test 96
7.2 Significance 103
7.3 The risk of a false positive finding 104
7.4 What aspects of the data will influence whether or not we obtain a significant outcome? 106
7.5 Requirements for applying a two-sample t-test 108
7.6 Performing and reporting the test 109
7.7 Chapter summary 110
8 The two?]sample t-test (2): The dreaded P value 111
8.1 Measuring how significant a result is 111
8.2 P values 112
8.3 Two ways to define significance? 113
8.4 Obtaining the P value 113
8.5 P values or 95% confidence intervals? 114
8.6 Chapter summary 115
9 The two-sample t-test (3): False negatives, power and necessary sample sizes 117
9.1 What else could possibly go wrong? 118
9.2 Power 119
9.3 Calculating necessary sample size 122
9.4 Chapter summary 130
10
Details
| Erscheinungsjahr: | 2015 |
|---|---|
| Genre: | Chemie |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: | 432 S. |
| ISBN-13: | 9781118913390 |
| ISBN-10: | 1118913396 |
| Sprache: | Englisch |
| Herstellernummer: | 1W118913390 |
| Autor: | Rowe, Philip |
| Auflage: | 2. Aufl. |
| Hersteller: | Wiley & Sons |
| Maße: | 244 x 172 x 20 mm |
| Von/Mit: | Philip Rowe |
| Erscheinungsdatum: | 02.10.2015 |
| Gewicht: | 0,652 kg |
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