Introductory Statistics Wiley
Umbelina Baublitz
An updated and revised edition of the popular introduction to statistics for students of economics or business, suitable for a one- or two-semester course. Presents an approach that is generally available only in much more advanced texts, yet uses the simplest mathematics consistent with a sound presentation.
This Fifth Edition includes a wealth of new problems and examples (many of them real-life problems drawn from the literature) to support the theoretical discussion. Emphasizes the regression model, including nonlinear and multiple regression. Topics covered include randomization to eliminate bias, exploratory data analysis, graphs, expected value in bidding, the bootstrap, path analysis, robust estimation, maximum likelihood estimation and Bayesian estimation and decisions.
This e-book is a complete interactive study guide with quizzing functionality that reports to the instructor. The on-line text also has animated figures and graphs that bring the print graphic to life for deeper understanding.
This book is geared toward students majoring in fields other than math or engineering. This text assumes students have been exposed to intermediate algebra, and it focuses on the applications of statistical knowledge rather than the theory behind it.
This text is for an introductory level probability and statistics course with an intermediate algebra prerequisite. The focus of the text follows the American Statistical Association's Guidelines for Assessment and Instruction in Statistics Education (GAISE).
This book puts a heavy emphasis on exploratory data analysis and provides a thorough discussion of simulation-based inference using randomization and bootstrapping, followed by a presentation of the related Central Limit Theorem based approaches.
This book is designed for students taking an introductory statistics class. The emphasis throughout the entire book is on how to make decisions with only partial evidence. It focuses on the thought process.
If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics.
A masterful guide to how the inferential bases of classical statistics can provide a principled disciplinary frame for the data science of the twenty-first century. Every aspiring data scientist should carefully study this book, use it as a reference.
This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly. Examples use R with many data sets taken from the authors' own consulting experience.
This book aim to provide an accessible though technically solid introduction to the logic of systematical analyses of statistical data to both undergraduate and postgraduate students, in particular in the Social Sciences, Economics, and the Financial Services.
Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You'd be surprised how many scientists are doing it wrong.
The goal is to help you learn How to Tell the Truth with Statistics and, therefore, how to tell when others are telling the truth ... or are faking their "news". Covers Data Analysis, Binomial and normal models, Sample statistics, confidence intervals, hypothesis tests, etc.
A uniquely developed presentation of key statistical topics, Introductory Statistics and Analytics: A Resampling Perspective provides an accessible approach to statistical analytics, resampling, and the bootstrap for readers with various levels of exposure to basic probability and statistics. Originally class-tested at one of the first online learning companies in the discipline, www.statistics.com, the book primarily focuses on applications of statistical concepts developed via resampling, with a background discussion of mathematical theory. This feature stresses statistical literacy and understanding, which demonstrates the fundamental basis for statistical inference and demystifies traditional formulas.
The book begins with illustrations that have the essential statistical topics interwoven throughout before moving on to demonstrate the proper design of studies. Meeting all of the Guidelines for Assessment and Instruction in Statistics Education (GAISE) requirements for an introductory statistics course, Introductory Statistics and Analytics: A Resampling Perspective also includes:
Introductory Statistics and Analytics: A Resampling Perspective is an excellent primary textbook for courses in preliminary statistics as well as a supplement for courses in upper-level statistics and related fields, such as biostatistics and econometrics. The book is also a general reference for readers interested in revisiting the value of statistics.
A quantitative literacy course with a statistical theme. Includes descriptive statistics, sampling, and inferential methods. Emphasizes problem solving and critical thinking. Canvas Course Mat $91/Macmillan applies.
Includes summarizing data, measures of central location, measures of variation, probability, mathematical expectation, probability distributions, sampling and sampling distributions, estimation, hypothesis testing, analysis of variance, regression analysis, and correlation. Canvas Course Mats of $66/Wiley applies.
Is an introductory statistics course for statistics majors. Applies discrete and continuous probability distributions to real data sets. Teaches confidence intervals and hypothesis testing for both one and two sample problems. Covers introductory topics in experimental design, linear regression, bootstrapping, and categorical data analysis. Canvas Course Mats of $66/Wiley applies.
Familiarizes students with the SAS statistical software package. Teaches how to organize, input data, and be able to use reference books to figure out the appropriate way to run the analysis needed using SAS.
Provides students with the mathematical background to complete upper division courses in applied statistical methods. Includes topics from calculus, linear algebra, mathematical statistics and introductory probability.
Introduces mathematical statistics for scientists and engineers. Includes counting techniques, random variables, expected values, joint and marginal distributions, point estimation, hypothesis testing, analysis of variance, and regression.
Provides students in non-mathematical disciplines the ability to answer typical research questions for their senior projects or graduate-level research. Includes linear regression, transformations, variable selection techniques, logistic regression, indicator variables, multicollinearity, and ARIMA time series. Satisfies the VEE statistics requirement for the Society of Actuaries. Introduces standard software as a tool for statistical analysis.
Introduces the design and analysis of randomized comparative experiments. Includes single factor ANOVAs, randomized block designs, latin squares, factorial designs, and nested and split plot designs. Covers mixed models including random effects and computation of expected mean squares to form appropriate F-ratios. Uses SAS statistical program software to perform statistical analysis.
Introduces survey sampling including simple random sampling, stratified random sampling, systematic and cluster sampling. Discusses ratio and difference estimators, weighting for non-responses, eliminating sources of bias and designing the questionnaire.
Teaches how to perform statistical inference on Markov chains, including classifying states, computing mean and variance of recurrence times, and investigating long-run limiting behavior to model physical systems uses the Poisson process. Teaches how to calculate and analyze queuing characteristics of each of the popular queuing models.
Introduces multivariate data analysis. Covers inference on data arising from the multivariate normal distribution using MANOVA, principal component analysis, factor analysis, canonical correlation analysis, discriminant analysis, and cluster analysis. Uses statistical software throughout.
Introduces nonparametric statistical procedures to apply in situations when parametric statistics (usually based on normality) are not appropriate. Covers types of nonparametric analyses that includes one and two sample hypothesis tests, goodness-of-fit tests, contingency tables, block designs, and regression analysis.
Emphasizes theoretical statistical inference. Includes concept sufficiency, theory of estimation, testing of statistical hypothesis, the Neyman-Pearson lemma, Bayesian inference, sequential testing, and large sample theory for inference.
The Faculty of Rehabilitation Medicine offers an undergraduate introductory statistics course PTHER 352 Introductory Statistics for Health Care Professionals which is an introduction to statistical principles, research methods and critical appraisal of research reports with a focus on the healthcare environment. Prerequisites: none
The Faculty of Rehabilitation Medicine offers an undergraduate introductory statistics course that qualifies as a pre-requisite for the MSc PT, MSc OT and MSc SLP programs. PTHER 352 Introductory Statistics for Health Care Professionals *3 (fi 6) is an introduction to statistical principles, research methods and critical appraisal of research reports with a focus on the healthcare environment. Prerequisites: none
Permission from the Faculty of Rehabilitation Medicine is not required to take PTHER 352. This course is open to all students registered at the University of Alberta. Students from outside the University of Alberta must apply to be registered as an open studies student to take the course.
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