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Applications of regression models in public health / / Erick Suarez [and three others]
| Applications of regression models in public health / / Erick Suarez [and three others] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2017 |
| Descrizione fisica | 1 online resource (212 pages) : illustrations, tables |
| Disciplina | 610.2/1 |
| Collana | THEi Wiley ebooks |
| Soggetto topico |
Medical statistics
Regression analysis Public health |
| ISBN |
1-119-21250-2
1-119-21251-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910162801703321 |
| Hoboken, New Jersey : , : Wiley, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Applications of regression models in public health / / Erick Suarez [and three others]
| Applications of regression models in public health / / Erick Suarez [and three others] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , 2017 |
| Descrizione fisica | 1 online resource (212 pages) : illustrations, tables |
| Disciplina | 610.2/1 |
| Collana | THEi Wiley ebooks |
| Soggetto topico |
Medical statistics
Regression analysis Public health |
| ISBN |
1-119-21250-2
1-119-21251-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910807945103321 |
| Hoboken, New Jersey : , : Wiley, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Applied Statistics in Biomedicine and Clinical Trials Design : Selected Papers from 2013 ICSA/ISBS Joint Statistical Meetings / / edited by Zhen Chen, Aiyi Liu, Yongming Qu, Larry Tang, Naitee Ting, Yi Tsong
| Applied Statistics in Biomedicine and Clinical Trials Design : Selected Papers from 2013 ICSA/ISBS Joint Statistical Meetings / / edited by Zhen Chen, Aiyi Liu, Yongming Qu, Larry Tang, Naitee Ting, Yi Tsong |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (550 p.) |
| Disciplina |
610.2/1
610.21 |
| Collana | ICSA Book Series in Statistics |
| Soggetto topico |
Biometry
Statistics Medicine - Research Biology - Research Biostatistics Statistical Theory and Methods Biomedical Research |
| ISBN | 3-319-12694-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Bayesian Methods in Biomedical Research -- Diagnostic Medicine and Classification -- Innovative Clinical Trials Design and Analysis -- Modeling and Data Analysis -- Personalized Medicine -- Statistical Genomics and High-Dimensional Data Analysis. |
| Record Nr. | UNINA-9910299763103321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Optimized Predictive Models in Health Care Using Machine Learning
| Optimized Predictive Models in Health Care Using Machine Learning |
| Autore | Kumar Sandeep |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Newark : , : John Wiley & Sons, Incorporated, , 2024 |
| Descrizione fisica | 1 online resource (385 pages) |
| Disciplina | 610.2/1 |
| Altri autori (Persone) |
SharmaAnuj
KaurNavneet PawarLokesh BajajRohit |
| Soggetto topico |
Medical statistics
Medical technology Machine learning Artificial intelligence - Medical applications |
| ISBN |
1-394-17537-X
1-394-17536-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Impact of Technology on Daily Food Habits and Their Effects on Health -- 1.1 Introduction -- 1.1.1 Impacts of Food on Health -- 1.1.2 Impact of Technology on Our Eating Habits -- 1.2 Technologies, Foodies, and Consciousness -- 1.3 Government Programs to Encourage Healthy Choices -- 1.4 Technology's Impact on Our Food Consumption -- 1.5 Customized Food is the Future of Food -- 1.6 Impact of Food Technology and Innovation on Nutrition and Health -- 1.7 Top Prominent and Emerging Food Technology Trends -- 1.8 Discussion -- 1.9 Conclusions -- References -- Chapter 2 Issues in Healthcare and the Role of Machine Learning in Healthcare -- 2.1 Introduction -- 2.2 Issues in Healthcare -- 2.2.1 Increase in Volume of Data -- 2.2.1.1 Data Management -- 2.2.1.2 Economic Difficulties -- 2.2.2 Data Privacy Issues -- 2.2.2.1 Cyber Attack and Hacking -- 2.2.2.2 Data Sharing Trust in the Third Party -- 2.2.2.3 Data Breaching -- 2.2.2.4 Lack of Policy and Constitutional Limitations -- 2.2.2.5 Doctor-Patient Relationship -- 2.2.2.6 Data Storage and Management -- 2.2.3 Disease-Centric Database -- 2.2.4 Data Utilization -- 2.2.5 Lack of Technology and Infrastructure -- 2.3 Factors Affecting the Health -- 2.4 Machine Learning in Healthcare -- 2.4.1 Clinical Decision Support Systems in Healthcare -- 2.4.2 Use of Machine Learning in Public Health -- 2.5 Conclusion -- References -- Chapter 3 Improving Accuracy in Predicting Stress Levels of Working Women Using Convolutional Neural Networks -- 3.1 Introduction -- 3.2 Literature Survey -- 3.3 Proposed Methodology -- 3.3.1 Pre-Processing of Data -- 3.3.2 Features Extraction -- 3.3.3 Selection of Features -- 3.3.4 Classification -- 3.4 Result and Discussion -- 3.5 Conclusion and Future Scope -- References.
Chapter 4 Analysis of Smart Technologies in Healthcare -- 4.1 Introduction -- 4.2 Emerging Technologies in Healthcare -- 4.2.1 Internet of Things -- 4.2.2 Blockchain -- 4.2.3 Machine Learning -- 4.2.4 Deep Learning -- 4.2.5 Federated Learning -- 4.3 Literature Review -- 4.4 Risks and Challenges -- 4.5 Conclusion -- References -- Chapter 5 Enhanced Neural Network Ensemble Classification for the Diagnosis of Lung Cancer Disease -- 5.1 Introduction -- 5.2 Algorithm for Classification of Proposed Weight-Optimized Neural Network Ensembles -- 5.2.1 Enhanced Raphson's Most Likelihood and Minimum Redundancy Preprocessing -- 5.2.2 Maximum Likelihood Boosting in a Weighted Optimized Neural Network -- 5.3 Experimental Work and Results -- 5.4 Conclusion -- References -- Chapter 6 Feature Selection for Breast Cancer Detection -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 Design and Implementation -- 6.3.1 Feature Selection -- 6.4 Conclusion -- References -- Chapter 7 An Optimized Feature-Based Prediction Model for Grouping the Liver Patients -- 7.1 Introduction -- 7.2 Literature Review -- 7.3 Proposed Methodology -- 7.4 Results and Discussions -- 7.5 Conclusion -- References -- Chapter 8 A Robust Machine Learning Model for Breast Cancer Prediction -- 8.1 Introduction -- 8.2 Literature Review -- 8.2.1 Comparative Analysis -- 8.3 Proposed Mythology -- 8.4 Result and Discussion -- 8.4.1 Accuracy -- 8.4.2 Error -- 8.4.3 TP Rate -- 8.4.4 FP Rate -- 8.4.5 F-Measure -- 8.5 Concluding Remarks and Future Scope -- References -- Chapter 9 Revolutionizing Pneumonia Diagnosis and Prediction Through Deep Neural Networks -- 9.1 Introduction -- 9.2 Literature Work -- 9.3 Proposed Section -- 9.3.1 Input Image -- 9.3.2 Pre-Processing -- 9.3.3 Identification and Classification Using ResNet50 -- 9.4 Result Analysis -- 9.5 Conclusion and Future Scope -- References. Chapter 10 Optimizing Prediction of Liver Disease Using Machine Learning Algorithms -- 10.1 Introduction -- 10.2 Related Works -- 10.3 Proposed Methodology -- 10.4 Result and Discussions -- 10.5 Conclusion -- References -- Chapter 11 Optimized Ensembled Model to Predict Diabetes Using Machine Learning -- 11.1 Introduction -- 11.2 Literature Review -- 11.3 Proposed Methodology -- 11.3.1 Missing Value Imputation (MVI) -- 11.3.2 Feature Selection -- 11.3.3 K-Fold Cross-Validation -- 11.3.4 ML Classifiers -- 11.3.5 Evaluation Metrics -- 11.4 Results and Discussion -- 11.5 Concluding Remarks and Future Scope -- References -- Chapter 12 Wearable Gait Authentication: A Framework for Secure User Identification in Healthcare -- 12.1 Introduction -- 12.2 Literature Survey -- 12.3 Proposed System -- 12.3.1 Walking Detection -- 12.3.2 Experimental Setup -- 12.4 Results and Discussion -- 12.4.1 Dataset Used -- 12.4.2 Results -- 12.4.3 Comparison Used Techniques -- 12.5 Conclusion and Future Scope -- References -- Chapter 13 NLP-Based Speech Analysis Using K-Neighbor Classifier -- 13.1 Introduction -- 13.2 Supervised Machine Learning for NLP and Text Analytics -- 13.2.1 Categorization and Classification -- 13.3 Unsupervised Machine Learning for NLP and Text Analytics -- 13.4 Experiments and Results -- 13.5 Conclusion -- References -- Chapter 14 Fusion of Various Machine Learning Algorithms for Early Heart Attack Prediction -- 14.1 Introduction -- 14.2 Literature Review -- 14.3 Materials and Methods -- 14.3.1 Dataset -- 14.3.2 EDA -- 14.3.3 Machine Learning Model Implemented -- 14.4 Result Analysis -- 14.5 Conclusion -- References -- Chapter 15 Machine Learning-Based Approaches for Improving Healthcare Services and Quality of Life (QoL): Opportunities, Issues and Challenges -- 15.1 Introduction. 15.2 Core Areas of Deep Learning and ML-Modeling in Medical Healthcare -- 15.3 Use Cases of Machine Learning Modelling in Healthcare Informatics -- 15.3.1 Breast Cancer Detection Using Machine Learning -- 15.3.2 COVID-19 Disease Detection Modelling Using Chest X-Ray Images with Machine and Transfer Learning Framework -- 15.4 Improving the Quality of Services During the Diagnosing and Treatment Processes of Chronicle Diseases -- 15.4.1 Evolution of New Diagnosing Methods and Tools -- 15.4.2 Improving Medical Care -- 15.4.3 Visualization of Biomedical Data -- 15.4.4 Improved Diagnosis and Disease Identification -- 15.4.5 More Accurate Health Records -- 15.4.6 Ethics of Machine Learning in Healthcare -- 15.5 Limitations and Challenges of ML, DL Modelling in Healthcare Systems -- 15.5.1 Dealing With the Shortage of Knowledgeable-ML-Data Scientists and Engineers -- 15.5.2 Handling of the Bias in ML Modelling of Healthcare Information -- 15.5.3 Accuracy of Data Attenuation -- 15.5.4 Lack of Data Quality -- 15.5.5 Tuning of Hyper-Parameters for Improving the Modelling of Healthcare -- 15.6 Conclusion -- References -- Chapter 16 Developing a Cognitive Learning and Intelligent Data Analysis-Based Framework for Early Disease Detection and Prevention in Younger Adults with Fatigue -- 16.1 Introduction -- 16.2 Proposed Framework "Cognitive-Intelligent Fatigue Detection and Prevention Framework (CIFDPF)" -- 16.2.1 Framework Components -- 16.2.2 Learning Module -- 16.2.3 System Design -- 16.2.4 Tools and Usage -- 16.2.5 Architecture -- 16.2.6 Architecture of CNN-RNN -- 16.2.7 Fatigue Detection Methods and Techniques -- 16.3 Potential Impact -- 16.3.1 Claims for the Accurate Detection of Fatigue -- 16.3.2 Similar Study and Results Analysis -- 16.3.3 Application and Results -- 16.4 Discussion and Limitations -- 16.5 Future Work. 16.5.1 Incorporation of More Physiological Signals -- 16.5.2 Long-Term Monitoring of Fatigue in Real-World Scenarios -- 16.5.3 Integration with Wearable Devices for Continuous Monitoring -- 16.6 Conclusion -- References -- Chapter 17 Machine Learning Approach to Predicting Reliability in Healthcare Using Knowledge Engineering -- 17.1 Introduction -- 17.2 Literature Review -- 17.3 Proposed Methodology -- 17.3.1 Data Analysis (Findings) -- 17.3.2 General Procedures -- 17.3.3 Reviewed Algorithms -- 17.3.4 Benefits of Machine Learning -- 17.3.5 Drawbacks of Machine Learning -- 17.4 Implications -- 17.4.1 Prerequisites and Considerations -- 17.4.2 Implementation Strategy -- 17.4.3 Recommendations -- 17.5 Conclusion -- 17.6 Limitations and Scope of Future Work -- References -- Chapter 18 TPLSTM-Based Deep ANN with Feature Matching Prediction of Lung Cancer -- 18.1 Introduction -- 18.2 Proposed TP-LSTM-Based Neural Network with Feature Matching for Prediction of Lung Cancer -- 18.3 Experimental Work and Comparison Analysis -- 18.4 Conclusion -- References -- Chapter 19 Analysis of Business Intelligence in Healthcare Using Machine Learning -- 19.1 Introduction -- 19.2 Data Gathering -- 19.2.1 Data Integration -- 19.2.2 Data Storage -- 19.2.3 Data Analysis -- 19.2.4 Data Distribution -- 19.2.5 Data-Driven Decisions on Generated Insights -- 19.3 Literature Review -- 19.4 Research Methodology -- 19.5 Implementation -- 19.6 Eligibility Criteria -- 19.7 Results -- 19.8 Conclusion and Future Scope -- References -- Chapter 20 StressDetect: ML for Mental Stress Prediction -- 20.1 Introduction -- 20.2 Related Work -- 20.3 Materials and Methods -- 20.4 Results -- 20.5 Discussion & -- Conclusions -- References -- Index. |
| Record Nr. | UNINA-9910877034203321 |
Kumar Sandeep
|
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| Newark : , : John Wiley & Sons, Incorporated, , 2024 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
A practical approach to using statistics in health research : from planning to reporting / / Adam Mackridge, Philip Rowe
| A practical approach to using statistics in health research : from planning to reporting / / Adam Mackridge, Philip Rowe |
| Autore | Mackridge Adam |
| Pubbl/distr/stampa | Hoboken, NJ : , : Wiley, , 2018 |
| Descrizione fisica | 1 online resource (237 pages) |
| Disciplina | 610.2/1 |
| Collana | THEi Wiley ebooks |
| Soggetto topico |
Medical statistics
Medicine - Research - Statistical methods |
| ISBN |
1-119-38361-7
1-119-38359-5 1-119-38362-5 |
| Classificazione | MED028000MAT029000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910270919903321 |
|
Mackridge Adam
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| Hoboken, NJ : , : Wiley, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
A practical approach to using statistics in health research : from planning to reporting / / Adam Mackridge, Philip Rowe
| A practical approach to using statistics in health research : from planning to reporting / / Adam Mackridge, Philip Rowe |
| Autore | Mackridge Adam |
| Pubbl/distr/stampa | Hoboken, NJ : , : Wiley, , 2018 |
| Descrizione fisica | 1 online resource (237 pages) |
| Disciplina | 610.2/1 |
| Collana | THEi Wiley ebooks |
| Soggetto topico |
Medical statistics
Medicine - Research - Statistical methods |
| ISBN |
1-119-38361-7
1-119-38359-5 1-119-38362-5 |
| Classificazione | MED028000MAT029000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910809014503321 |
|
Mackridge Adam
|
||
| Hoboken, NJ : , : Wiley, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Starting out in Statistics [[electronic resource] ] : An Introduction for Students of Human Health, Disease, and Psychology
| Starting out in Statistics [[electronic resource] ] : An Introduction for Students of Human Health, Disease, and Psychology |
| Autore | De Winter Patricia <1968-> |
| Pubbl/distr/stampa | Somerset, : Wiley, 2014 |
| Descrizione fisica | 1 online resource (312 p.) |
| Disciplina | 610.2/1 |
| Altri autori (Persone) | CahusacPeter M. B |
| Soggetto topico |
Medical statistics -- Textbooks
Medical statistics Health Care Evaluation Mechanisms Medicine Methods Mathematics Research Epidemiologic Methods Environment and Public Health Health Investigative Techniques Natural Science Disciplines Science Population Characteristics Quality of Health Care Health Occupations Health Care Health Care Quality, Access, and Evaluation Analytical, Diagnostic and Therapeutic Techniques and Equipment Disciplines and Occupations Public Health Statistics as Topic Research Design Health & Biological Sciences Medical Statistics |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-118-92055-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Starting Out in Statistics; Contents; Introduction - What's the Point of Statistics?; Reference; Basic Maths for Stats Revision; Statistical Software Packages; About the Companion Website; 1 Introducing Variables, Populations and Samples - 'Variability is the Law of Life'; 1.1 Aims; 1.2 Biological data vary; 1.3 Variables; 1.4 Types of qualitative variables; 1.4.1 Nominal variables; 1.4.2 Multiple response variables; 1.4.3 Preference variables; 1.5 Types of quantitative variables; 1.5.1 Discrete variables; 1.5.2 Continuous variables; 1.5.3 Ordinal variables - a moot point
1.6 Samples and populations1.7 Summary; Reference; 2 Study Design and Sampling - 'Design is Everything. Everything!'; 2.1 Aims; 2.2 Introduction; 2.3 One sample; 2.4 Related samples; 2.5 Independent samples; 2.6 Factorial designs; 2.7 Observational study designs; 2.7.1 Cross-sectional design; 2.7.2 Case-control design; 2.7.3 Longitudinal studies; 2.7.4 Surveys; 2.8 Sampling; 2.9 Reliability and validity; 2.10 Summary; References; 3 Probability - 'Probability ... So True in General'; 3.1 Aims; 3.2 What is probability?; 3.3 Frequentist probability; 3.4 Bayesian probability 3.5 The likelihood approach3.6 Summary; References; 4 Summarising Data - 'Transforming Data into Information'; 4.1 Aims; 4.2 Why summarise?; 4.3 Summarising data numerically - descriptive statistics; 4.3.1 Measures of central location; 4.3.2 Measures of dispersion; 4.4 Summarising data graphically; 4.5 Graphs for summarising group data; 4.5.1 The bar graph; 4.5.2 The error plot; 4.5.3 The box-and-whisker plot; 4.5.4 Comparison of graphs for group data; 4.5.5 A little discussion on error bars; 4.6 Graphs for displaying relationships between variables; 4.6.1 The scatter diagram or plot 4.6.2 The line graph4.7 Displaying complex (multidimensional) data; 4.8 Displaying proportions or percentages; 4.8.1 The pie chart; 4.8.2 Tabulation; 4.9 Summary; References; 5 Statistical Power - '. . . Find out the Cause of this Effect'; 5.1 Aims; 5.2 Power; 5.3 From doormats to aortic valves; 5.4 More on the normal distribution; 5.4.1 The central limit theorem; 5.5 How is power useful?; 5.5.1 Calculating the power; 5.5.2 Calculating the sample size; 5.6 The problem with p values; 5.7 Confidence intervals and power; 5.8 When to stop collecting data 5.9 Likelihood versus null hypothesis testing5.10 Summary; References; 6 Comparing Groups using t-Tests and ANOVA - 'To Compare is not to Prove'; 6.1 Aims; 6.2 Are men taller than women?; 6.3 The central limit theorem revisited; 6.4 Student's t-test; 6.4.1 Calculation of the pooled standard deviation; 6.4.2 Calculation of the t statistic; 6.4.3 Tables and tails; 6.5 Assumptions of the t-test; 6.6 Dependent t-test; 6.7 What type of data can be tested using t-tests?; 6.8 Data transformations; 6.9 Proof is not the answer; 6.10 The problem of multiple testing 6.11 Comparing multiple means - the principles of analysis of variance |
| Record Nr. | UNINA-9910467556703321 |
|
De Winter Patricia <1968->
|
||
| Somerset, : Wiley, 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Starting out in Statistics [[electronic resource] ] : An Introduction for Students of Human Health, Disease, and Psychology
| Starting out in Statistics [[electronic resource] ] : An Introduction for Students of Human Health, Disease, and Psychology |
| Autore | De Winter Patricia <1968-> |
| Pubbl/distr/stampa | Somerset, : Wiley, 2014 |
| Descrizione fisica | 1 online resource (312 p.) |
| Disciplina | 610.2/1 |
| Altri autori (Persone) | CahusacPeter <1957-> |
| Collana | New York Academy of Sciences |
| Soggetto topico |
Medical statistics -- Textbooks
Medical statistics Health Care Evaluation Mechanisms Medicine Methods Mathematics Research Epidemiologic Methods Environment and Public Health Health Investigative Techniques Natural Science Disciplines Science Population Characteristics Quality of Health Care Health Occupations Health Care Health Care Quality, Access, and Evaluation Public Health Statistics as Topic Research Design Health & Biological Sciences Medical Statistics |
| ISBN | 1-118-92055-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Starting Out in Statistics; Contents; Introduction - What's the Point of Statistics?; Reference; Basic Maths for Stats Revision; Statistical Software Packages; About the Companion Website; 1 Introducing Variables, Populations and Samples - 'Variability is the Law of Life'; 1.1 Aims; 1.2 Biological data vary; 1.3 Variables; 1.4 Types of qualitative variables; 1.4.1 Nominal variables; 1.4.2 Multiple response variables; 1.4.3 Preference variables; 1.5 Types of quantitative variables; 1.5.1 Discrete variables; 1.5.2 Continuous variables; 1.5.3 Ordinal variables - a moot point
1.6 Samples and populations1.7 Summary; Reference; 2 Study Design and Sampling - 'Design is Everything. Everything!'; 2.1 Aims; 2.2 Introduction; 2.3 One sample; 2.4 Related samples; 2.5 Independent samples; 2.6 Factorial designs; 2.7 Observational study designs; 2.7.1 Cross-sectional design; 2.7.2 Case-control design; 2.7.3 Longitudinal studies; 2.7.4 Surveys; 2.8 Sampling; 2.9 Reliability and validity; 2.10 Summary; References; 3 Probability - 'Probability ... So True in General'; 3.1 Aims; 3.2 What is probability?; 3.3 Frequentist probability; 3.4 Bayesian probability 3.5 The likelihood approach3.6 Summary; References; 4 Summarising Data - 'Transforming Data into Information'; 4.1 Aims; 4.2 Why summarise?; 4.3 Summarising data numerically - descriptive statistics; 4.3.1 Measures of central location; 4.3.2 Measures of dispersion; 4.4 Summarising data graphically; 4.5 Graphs for summarising group data; 4.5.1 The bar graph; 4.5.2 The error plot; 4.5.3 The box-and-whisker plot; 4.5.4 Comparison of graphs for group data; 4.5.5 A little discussion on error bars; 4.6 Graphs for displaying relationships between variables; 4.6.1 The scatter diagram or plot 4.6.2 The line graph4.7 Displaying complex (multidimensional) data; 4.8 Displaying proportions or percentages; 4.8.1 The pie chart; 4.8.2 Tabulation; 4.9 Summary; References; 5 Statistical Power - '. . . Find out the Cause of this Effect'; 5.1 Aims; 5.2 Power; 5.3 From doormats to aortic valves; 5.4 More on the normal distribution; 5.4.1 The central limit theorem; 5.5 How is power useful?; 5.5.1 Calculating the power; 5.5.2 Calculating the sample size; 5.6 The problem with p values; 5.7 Confidence intervals and power; 5.8 When to stop collecting data 5.9 Likelihood versus null hypothesis testing5.10 Summary; References; 6 Comparing Groups using t-Tests and ANOVA - 'To Compare is not to Prove'; 6.1 Aims; 6.2 Are men taller than women?; 6.3 The central limit theorem revisited; 6.4 Student's t-test; 6.4.1 Calculation of the pooled standard deviation; 6.4.2 Calculation of the t statistic; 6.4.3 Tables and tails; 6.5 Assumptions of the t-test; 6.6 Dependent t-test; 6.7 What type of data can be tested using t-tests?; 6.8 Data transformations; 6.9 Proof is not the answer; 6.10 The problem of multiple testing 6.11 Comparing multiple means - the principles of analysis of variance |
| Record Nr. | UNINA-9910796569703321 |
|
De Winter Patricia <1968->
|
||
| Somerset, : Wiley, 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Starting out in Statistics : An Introduction for Students of Human Health, Disease, and Psychology
| Starting out in Statistics : An Introduction for Students of Human Health, Disease, and Psychology |
| Autore | De Winter Patricia <1968-> |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Somerset, : Wiley, 2014 |
| Descrizione fisica | 1 online resource (312 p.) |
| Disciplina | 610.2/1 |
| Altri autori (Persone) | CahusacPeter <1957-> |
| Collana | New York Academy of Sciences |
| Soggetto topico |
Medical statistics -- Textbooks
Medical statistics Health Care Evaluation Mechanisms Medicine Methods Mathematics Research Epidemiologic Methods Environment and Public Health Health Investigative Techniques Natural Science Disciplines Science Population Characteristics Quality of Health Care Health Occupations Health Care Health Care Quality, Access, and Evaluation Public Health Statistics as Topic Research Design Health & Biological Sciences Medical Statistics |
| ISBN | 1-118-92055-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Starting Out in Statistics; Contents; Introduction - What's the Point of Statistics?; Reference; Basic Maths for Stats Revision; Statistical Software Packages; About the Companion Website; 1 Introducing Variables, Populations and Samples - 'Variability is the Law of Life'; 1.1 Aims; 1.2 Biological data vary; 1.3 Variables; 1.4 Types of qualitative variables; 1.4.1 Nominal variables; 1.4.2 Multiple response variables; 1.4.3 Preference variables; 1.5 Types of quantitative variables; 1.5.1 Discrete variables; 1.5.2 Continuous variables; 1.5.3 Ordinal variables - a moot point
1.6 Samples and populations1.7 Summary; Reference; 2 Study Design and Sampling - 'Design is Everything. Everything!'; 2.1 Aims; 2.2 Introduction; 2.3 One sample; 2.4 Related samples; 2.5 Independent samples; 2.6 Factorial designs; 2.7 Observational study designs; 2.7.1 Cross-sectional design; 2.7.2 Case-control design; 2.7.3 Longitudinal studies; 2.7.4 Surveys; 2.8 Sampling; 2.9 Reliability and validity; 2.10 Summary; References; 3 Probability - 'Probability ... So True in General'; 3.1 Aims; 3.2 What is probability?; 3.3 Frequentist probability; 3.4 Bayesian probability 3.5 The likelihood approach3.6 Summary; References; 4 Summarising Data - 'Transforming Data into Information'; 4.1 Aims; 4.2 Why summarise?; 4.3 Summarising data numerically - descriptive statistics; 4.3.1 Measures of central location; 4.3.2 Measures of dispersion; 4.4 Summarising data graphically; 4.5 Graphs for summarising group data; 4.5.1 The bar graph; 4.5.2 The error plot; 4.5.3 The box-and-whisker plot; 4.5.4 Comparison of graphs for group data; 4.5.5 A little discussion on error bars; 4.6 Graphs for displaying relationships between variables; 4.6.1 The scatter diagram or plot 4.6.2 The line graph4.7 Displaying complex (multidimensional) data; 4.8 Displaying proportions or percentages; 4.8.1 The pie chart; 4.8.2 Tabulation; 4.9 Summary; References; 5 Statistical Power - '. . . Find out the Cause of this Effect'; 5.1 Aims; 5.2 Power; 5.3 From doormats to aortic valves; 5.4 More on the normal distribution; 5.4.1 The central limit theorem; 5.5 How is power useful?; 5.5.1 Calculating the power; 5.5.2 Calculating the sample size; 5.6 The problem with p values; 5.7 Confidence intervals and power; 5.8 When to stop collecting data 5.9 Likelihood versus null hypothesis testing5.10 Summary; References; 6 Comparing Groups using t-Tests and ANOVA - 'To Compare is not to Prove'; 6.1 Aims; 6.2 Are men taller than women?; 6.3 The central limit theorem revisited; 6.4 Student's t-test; 6.4.1 Calculation of the pooled standard deviation; 6.4.2 Calculation of the t statistic; 6.4.3 Tables and tails; 6.5 Assumptions of the t-test; 6.6 Dependent t-test; 6.7 What type of data can be tested using t-tests?; 6.8 Data transformations; 6.9 Proof is not the answer; 6.10 The problem of multiple testing 6.11 Comparing multiple means - the principles of analysis of variance |
| Record Nr. | UNINA-9910816086703321 |
|
De Winter Patricia <1968->
|
||
| Somerset, : Wiley, 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistics at square two [[electronic resource] ] : understanding modern statistical applications in medicine / / Michael J. Campbell
| Statistics at square two [[electronic resource] ] : understanding modern statistical applications in medicine / / Michael J. Campbell |
| Autore | Campbell Michael J., PhD. |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Malden, Mass. ; ; Oxford, : BMJ Books/Blackwell, 2006 |
| Descrizione fisica | 1 online resource (146 p.) |
| Disciplina | 610.2/1 |
| Soggetto topico |
Medical statistics
Statistics |
| ISBN |
1-118-70980-2
1-281-32049-8 9786611320492 0-470-75583-0 0-470-75502-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Statistics at Square Two: Understanding modern statistical applications in medicine; Contents; Preface; Chapter 1: Models, tests and data; 1.1 Basics; 1.2 Models; 1.3 Types of data; 1.4 Significance tests; 1.5 Confidence intervals; 1.6 Statistical tests using models; 1.7 Model fitting and analysis: confirmatory and exploratory analyses; 1.8 Computer-intensive methods; 1.9 Bayesian methods; 1.10 Missing values; 1.11 Reporting statistical results in the literature; 1.12 Reading statistics in the literature; Chapter 2: Multiple linear regression; 2.1 The model; 2.2 Uses of multiple regression
2.3 Two independent variables 2.4 Interpreting a computer output; 2.5 Multiple regression in action; 2.6 Assumptions underlying the models; 2.7 Model sensitivity; 2.8 Stepwise regression; 2.9 Reporting the results of a multiple regression; 2.10 Reading the results of a multiple regression; Chapter 3: Logistic regression; 3.1 The model; 3.2 Uses of logistic regression; 3.3 Interpreting a computer output: grouped analysis; 3.4 Logistic regression in action; 3.5 Model checking; 3.6 Interpreting computer output: ungrouped analysis; 3.7 Case-control studies 3.8 Interpreting computer output: unmatched case-control study 3.9 Matched case-control studies; 3.10 Interpreting computer output: matched case-control study; 3.11 Conditional logistic regression in action; 3.12 Reporting the results of logistic regression; 3.13 Reading about logistic regression; Chapter 4: Survival analysis; 4.1 Introduction; 4.2 The model; 4.3 Uses of Cox regression; 4.4 Interpreting a computer output; 4.5 Survival analysis in action; 4.6 Interpretation of the model; 4.7 Generalisations of the model; 4.8 Model checking; 4.9 Reporting the results of a survival analysis 4.10 Reading about the results of a survival analysis Chapter 5: Random effects models; 5.1 Introduction; 5.2 Models for random effects; 5.3 Random vs fixed effects; 5.4 Use of random effects models; 5.5 Random effects models in action; 5.6 Ordinary least squares at the group level; 5.7 Computer analysis; 5.8 Model checking; 5.9 Reporting the results of random effects analysis; 5.10 Reading about the results of random effects analysis; Chapter 6: Other models; 6.1 Poisson regression; 6.2 Ordinal regression; 6.3 Time series regression 6.4 Reporting Poisson, ordinal or time series regression in the literature 6.5 Reading about the results of Poisson, ordinal or time series regression in the literature; Appendix 1: Exponentials and logarithms; A1.1 Logarithms; Appendix 2: Maximum likelihood and significance tests; A2.1 Binomial models and likelihood; A2.2 Poisson model; A2.3 Normal model; A2.4 Hypothesis testing: LR test; A2.5 Wald test; A2.6 Score test; A2.7 Which method to choose?; A2.8 Confidence intervals; Appendix 3: Bootstrapping and variance robust standard errors; A3.1 Computer analysis; A3.2 The bootstrap in action A3.3 Robust or sandwich estimate SE |
| Record Nr. | UNINA-9910145557403321 |
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Campbell Michael J., PhD.
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| Malden, Mass. ; ; Oxford, : BMJ Books/Blackwell, 2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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