BIOS 9131 - Advanced Statistical Theory for Biostatistics I

This introduction to statistics is aimed at students and researchers without statistical background. It should enable them to read result sections of research articles and to understand terms like "p-value", "repeated-measures design" or "Latin Square Design". For a list of introductions to the use of test statistics and the use of the software package R, see: http://experimentalfieldlinguistics.wordpress.com/readings/statistics/

Delhi Journal of Ophthalmology

Statistics is that branch of science which deals with the collection, organization, analysis, and interpretation of numerical data. It is the study of the methods of collection, analysis, interpretation, presentation, organization, summarizing and drawing conclusions from data. It has applications in everyday life because the access to data has increased tremendously. Medical students are frequently overwhelmed with numbers. However, to make sense of the ubiquitous journal clubs, recent advances and for making decisions in clinical practice it is essential to be conversant with the techniques, their interpretations and their limitations in statistics. This article briefly covers the ground zero and aims at removing the fear of the unknown.

Purpose At the end of the course the student should be able to handle problems involving probability distributions of a discrete or a continuous random variable. Objectives By the end of this course the student should be able to; (1) Define the probability mass, density and distribution functions, and to use these to determine expectation, variance, percentiles and mode for a given distribution (2) Appreciate the form of the probability mass functions for the binomial, geometric, hypergeometic and Poisson distributions, and the probability density functions for the uniform, exponential gamma , beta and normal, functions, and their applications (3) Apply the moment generating function and transformation of variable techniques (4) Apply the principles of statistical inference for one sample problems. DESCRIPTION Random variables: discrete and continuous, probability mass, density and distribution functions, expectation, variance, percentiles and mode. Moments and moment generating function. Moment generating function and transformation Change of variable technique for univariate distribution. Probability distributions: hypergeometric, binomial, Poisson, uniform, normal, beta and gamma. Statistical inference including one sample normal and t tests.

Data -Numerical information.

We wish you all a happy new year. May this year fill with new learning and knowledge. This issue carries a perspective article on intraocular lens implantation in children-a topic in which much debate goes on about the best technique, the best IOL and so on. The accommodative spasm article describes two different presentations and discusses the entity in detail. An intriguing muscle puzzle follows. The continuing series on biostatistics presents yet another chapter. A technology update on the Allegro Biograph concludes the issue.

2017

This module is intended to facilitate teaching and learning of Statistics courses in colleges and universities at degree level. It is specially tailored for students of science, engineering and technology. This book provides examples and exercises that present important ideas of statistics in a realistic setting to show connections between theory and application in industry and scientific research. The materials in this module also integrate well with computer software packages especially in the chapters on descriptive statistics, hypothesis testing, analysis of variance and regression models. The use of Microsoft Excel is emphasised in this module.

This book contains a collection of results relating to the normal distribution. It is a compendium of properties, and problems of analysis and proof are not covered. The aim of the authors has been to list results which will be useful to theoretical and applied researchers in statistics as well as to students. Distributional properties are emphasized, both for the normal law itself and for statistics based on samples from normal populations. The book covers the early historical development of the normal law (Chapter 1); basic distributional properties, including references to tables and to algorithms suitable for computers (Chapters 2 and 3); properties of sampling distributions, including order statistics (Chapters 5 and 8), Wiener and Gaussian processes (Chapter 9); and the bivariate normal distribution (Chapter 10). Chapters 4 and 6 cover characterizations of the normal law and central limit theorems, respectively; these chapters may be more useful to theoretical statisticians. A collection of results showing how other distributions may be approximated by the normal law completes the coverage of the book (Chapter 7). Several important subjects are not covered. There are no tables of distributions in this book, because excellent tables are available elsewhere; these are listed, however, with the accuracy and coverage in the sources. The multivariate normal distribution other than the bivariate case is not discussed; the general linear v vi Preface model and regression models based on normality have been amply documented elsewhere; and the applications of normality in the methodology of statistical inference and decision theory would provide material for another volume on their own.