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e-book
"A Brief Introduction to Engineering Computation with MATLAB is specifically designed for students with no programming experience. However, students are expected to be proficient in First Year Mathematics and Sciences and access to good reference books are highly recommended. Students are assumed to have a working knowledge of the Mac OS X or Microsoft Windows operating systems. The strategic goal of the course and book is to provide learners with an appreciation for the role computation plays in solving engineering problems. MATLAB specific skills that students are expected to be proficient at are: write scripts to solve engineering problems including interpolation, numerical integration and regression analysis, plot graphs to visualize, analyze and present numerical data, and publish reports."--BC Campus website.
- Subjects:
- Computing, Data Science and Artificial Intelligence and Mechanical Engineering
- Keywords:
- Systems engineering Textbooks
- Resource Type:
- e-book
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e-book
The focus of this book is on using quantitative research methods to test hypotheses and build theory in political science, public policy and public administration. It is designed for advanced undergraduate courses, or introductory and intermediate graduate-level courses. The first part of the book introduces the scientific method, then covers research design, measurement, descriptive statistics, probability, inference, and basic measures of association. The second part of the book covers bivariate and multiple linear regression using the ordinary least squares, the calculus and matrix algebra that are necessary for understanding bivariate and multiple linear regression, the assumptions that underlie these methods, and then provides a short introduction to generalized linear models.The book fully embraces the open access and open source philosophies. The book is freely available in the SHAREOK repository; it is written in R Markdown files that are available in a public GitHub repository; it uses and teaches R and RStudio for data analysis, visualization and data management; and it uses publically available survey data (from the Meso-Scale Integrated Socio-geographic Network) to illustrate important concepts and methods. We encourage students to download the data, replicate the examples, and explore further! We also encourage instructors to download the R Markdown files and modify the text for use in different courses.
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e-book
Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software. The book discusses how to get started in R as well as giving an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book.
- Subjects:
- Psychology and Mathematics and Statistics
- Keywords:
- Statistics -- Computer programs R (Computer program language) Textbooks Statistics Social sciences -- Statistical methods
- Resource Type:
- e-book
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e-book
Introductory Business Statistics with Interactive Spreadsheets – 1st Canadian Edition is an adaptation of Thomas K. Tiemann's book, Introductory Business Statistics. This new edition still contains the basic ideas behind statistics, such as populations, samples, the difference between data and information, and sampling distributions as well as information on descriptive statistics and frequency distributions, normal and t-distributions, hypothesis testing, t-tests, f-tests, analysis of variance, non-parametric tests, and regression basics. New topics include the chi-square test and categorical variables, null and alternative hypotheses for the test of independence, simple linear regression model, least squares method, coefficient of determination, confidence interval for the average of the dependent variable, and prediction interval for a specific value of the dependent variable. This new edition also allows readers to learn the basic and most commonly applied statistical techniques in business in an interactive way — when using the web version — through interactive Excel spreadsheets. For each topic, a customized interactive template has been created within which selected values can be repeatedly changed to observe how the entire process, as well as the outcomes, are automatically adjusted. Also, in this adapted edition, the real-world examples throughout the text, and the information in general, have been revised to reflect Canadian content.
- Subjects:
- Management and Statistics and Research Methods
- Keywords:
- Business -- Decision making Textbooks Microsoft Excel (Computer file) Commercial statistics
- Resource Type:
- e-book
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e-book
The book "Introductory Business Statistics" by Thomas K. Tiemann explores the basic ideas behind statistics, such as populations, samples, the difference between data and information, and most importantly sampling distributions. The author covers topics including descriptive statistics and frequency distributions, normal and t-distributions, hypothesis testing, t-tests, f-tests, analysis of variance, non-parametric tests, and regression basics. Using real-world examples throughout the text, the author hopes to help students understand how statistics works, not just how to "get the right number."
- Subjects:
- Mathematics and Statistics and Management
- Keywords:
- Textbooks Commercial statistics Industrial management -- Statistical methods
- Resource Type:
- e-book
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e-book
Natural Resources Biometrics begins with a review of descriptive statistics, estimation, and hypothesis testing. The following chapters cover one- and two-way analysis of variance (ANOVA), including multiple comparison methods and interaction assessment, with a strong emphasis on application and interpretation. Simple and multiple linear regressions in a natural resource setting are covered in the next chapters, focusing on correlation, model fitting, residual analysis, and confidence and prediction intervals. The final chapters cover growth and yield models, volume and biomass equations, site index curves, competition indices, importance values, and measures of species diversity, association, and community similarity.
- Subjects:
- Biology
- Keywords:
- Natural resources Biometry Textbooks
- Resource Type:
- e-book