R is one of the most powerful and flexible statistical software packages available, and enables the user to apply a wide variety of statistical methods ranging from simple regression to generalized linear modelling. Statistics: An Introduction using R is a clear and concise introductory textbook to statistical analysis using this powerful and free software, and follows on from the success of the author's previous best-selling title Statistical Computing. * Features step-by-step instructions that assume no mathematics, statistics or programming background, helping the non-statistician to fully understand the methodology. * Uses a series of realistic examples, developing step-wise from the simplest cases, with the emphasis on checking the assumptions (e.g. constancy of variance and normality of errors) and the adequacy of the model chosen to fit the data. * The emphasis throughout is on estimation of effect sizes and confidence intervals, rather than on hypothesis testing. * Covers the full range of statistical techniques likely to be need to analyse the data from research projects, including elementary material like t-tests and chi-squared tests, intermediate methods like regression and analysis of variance, and more advanced techniques like generalized linear modelling. * Includes numerous worked examples and exercises within each chapter. * Accompanied by a website featuring worked examples, data sets, exercises and solutions: http: //www.imperial.ac.uk/bio/research/crawl... Statistics: An Introduction using R is the first text to offer such a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to a broad range of disciplines. It is primarily aimed at undergraduate students in medicine, engineering, economics and biology - but will also appeal to postgraduates who have not previously covered this area, or wish to switch to using R.

    It allows one to gain information by discarding information, namely, the individuality of the observations. Stigler s second pillar, information measurement, challenges the importance of big data by noting that observations are not all equally important: the amount of information in a data set is often proportional to only the square root of the number of observations, not the absolute number. The third idea is likelihood, the calibration of inferences with the use of probability. Intercomparison is the principle that statistical comparisons do not need to be made with respect to an external standard. The fifth pillar is regression, both a paradox (tall parents on average produce shorter children; tall children on average have shorter parents) and the basis of inference, including Bayesian inference and causal reasoning. The sixth concept captures the importance of experimental design for example, by recognizing the gains to be had from a combinatorial approach with rigorous randomization. The seventh idea is the residual the notion that a complicated phenomenon can be simplified by subtracting the effect of known causes, leaving a residual phenomenon that can be explained more easily.The Seven Pillars of Statistical Wisdom presents an original, unified account of statistical science that will fascinate the interested layperson and engage the professional statistician."

    Graphs are not included, but both the graphs and the concepts behind them are explained; only basic math is included, and you can even skim over the math and still come away with an understanding of the concepts; statistics is not included at all.BASIC ECONOMICS FOR STUDENTS AND NON-STUDENTS ALIKE is an easy way to learn concepts relating to economics and the economy. It is a product of thousands of hours spent online, teaching basic concepts in economics to hundreds of students worldwide over the course of the past several years. From back and forth communications, I have discovered the explanations for the concepts that students find easiest to understand, as well as the areas that most often get misunderstood and under-emphasized.I have worked with students located throughout the United States and from many different countries, on six different continents; students from many different school systems with different points of emphasis; students with different levels of knowledge, different backgrounds, and different levels of interest in the subject. I have received numerous comments and testimonials regarding the teaching methods that I incorporate in BASIC ECONOMICS FOR STUDENTS AND NON-STUDENTS ALIKE.The subject matter included in BASIC ECONOMICS FOR STUDENTS AND NON-STUDENTS ALIKE comes from a compilation of many different textbooks at the introductory and intermediate levels. My goal was to include every subject in economics that normally will be found in an introductory level textbook of economics, microeconomics, or macroeconomics. Since different school systems, different classroom instructors, and different textbooks cover a slightly different combination of topics, BASIC ECONOMICS FOR STUDENTS AND NON-STUDENTS ALIKE is a little more comprehensive than most single introductory textbooks of economics. Some of the topics will be found in introductory classes in some schools, but in intermediate-level classes in other schools.

    Managers, securities analysts, bankers and consultants all use them to make business decisions. There is strong demand among business students for course materials that provide a framework for using financial statement data in a variety of business analysis and valuation contexts.