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Record Nr. |
UNINA9910467556703321 |
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Autore |
De Winter Patricia <1968-> |
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Titolo |
Starting out in Statistics [[electronic resource] ] : An Introduction for Students of Human Health, Disease, and Psychology |
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Pubbl/distr/stampa |
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ISBN |
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Descrizione fisica |
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1 online resource (312 p.) |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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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 |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di contenuto |
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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 |
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Sommario/riassunto |
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To form a strong grounding in human-related sciences it is essential for students to grasp the fundamental concepts of statistical analysis, rather than simply learning to use statistical software. Although the software is useful, it does not arm a student with the skills necessary to formulate the experimental design and analysis of a research project in later years of study or indeed, if working in research. This textbook deftly covers a topic that many students find difficult. With an engaging and accessible style it provides the necessary background and tools for |
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2. |
Record Nr. |
UNINA9910254627203321 |
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Autore |
Haywood Raphaëlle D |
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Titolo |
Radial-velocity Searches for Planets Around Active Stars / / by Raphaëlle D. Haywood |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
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ISBN |
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Edizione |
[1st ed. 2016.] |
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Descrizione fisica |
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1 online resource (XV, 140 p. 60 illus., 57 illus. in color.) |
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Collana |
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Springer Theses, Recognizing Outstanding Ph.D. Research, , 2190-5053 |
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Disciplina |
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Soggetti |
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Astronomy |
Astronomy—Observations |
Astrophysics |
Space sciences |
Exobiology |
Astronomy, Observations and Techniques |
Astrophysics and Astroparticles |
Space Sciences (including Extraterrestrial Physics, Space Exploration and Astronautics) |
Astrobiology |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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"Doctoral thesis accepted by the University of St. Andrews, UK." |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters and index. |
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Nota di contenuto |
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Introduction: the Hunt for Extra-solar Planets -- A Toolkit to Detect Planets Around Active Stars -- Application to Observations of Planet-hosting Stars -- An Exploration into the Radial-velocity Variability of the Sun -- Conclusion. |
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Sommario/riassunto |
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This thesis develops new and powerful methods for identifying planetary signals in the presence of “noise” generated by stellar activity, and explores the physical origin of stellar intrinsic variability, using unique observations of the Sun seen as a star. In particular, it |
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establishes that the intrinsic stellar radial-velocity variations mainly arise from suppression of photospheric convection by magnetic fields. With the advent of powerful telescopes and instruments we are now on the verge of discovering real Earth twins in orbit around other stars. The intrinsic variability of the host stars themselves, however, currently remains the main obstacle to determining the masses of such small planets. The methods developed here combine Gaussian-process regression for modeling the correlated signals arising from evolving active regions on a rotating star, and Bayesian model selection methods for distinguishing genuine planetary signals from false positives produced by stellar magnetic activity. The findings of this thesis represent a significant step towards determining the masses of potentially habitable planets orbiting Sun-like stars. . |
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