Thư viện số
Giáo trình, tài liệu tham khảo và tài nguyên học tập mở.
Nguồn ngoài
Advanced High School Statistics
David Diez; Christopher Barr; Mine Çetinkaya-Rundel; Leah Dorazio · OpenIntro · 2019
We hope readers will take away three ideas from this book in addition to forming a foundationof statistical thinking and methods. (1) Statistics is an applied field with a wide range of practical applications. (2) You don't have to be a math guru to learn from real, interesting data. (3) Data are messy, and statistical tools are imperfect. But, when you understand the strengths and weaknesses of these tools, you can use them to learn about the real world. Textbook overviewThe chapters of this book are as follows: 1. Data collection. Data structures, variables, and basic data collection techniques. 2. Summarizing data. Data summaries and graphics. 3. Probability. The basic principles of probability. 4. Distributions of random variables. Introduction to key distributions, and how the normal model applies to the sample mean and sample proportion. 5. Foundation for inference. General ideas for statistical inference in the context of estimating the population proportion. 6. Inference for categorical data. Inference for proportions using the normal and chisquare distributions. 7. Inference for numerical data. Inference for one or two sample means using the t distribution, and comparisons of many means using ANOVA. 8. Introduction to linear regression. An introduction to regression with two variables. Instructions are also provided in several sections for using Casio and TI calculators.
Nguồn ngoài
Analysis of Variance and Design of Experiments
The Pennsylvania State University · LibreTexts
This is a graduate-level course that provides a thorough introduction to statistical methods used to analyze data resulting from a wide range of experimental designs. The concepts of comparative experiments, randomization, replication, repeated measures, blocking, and factorial designs will be discussed. The main goal of the course will be to develop problem-solving skills for identifying a variety of designs and making inferences on associated parameters.
Nguồn ngoài
Intermediate Statistics with R
Mark Greenwood · Montana State University
Có trong mục lục: 1.03: Basic summary statistics histograms and boxplots using R · 02: (R)e-Introduction to statistics
Nguồn ngoài
Time Series Analysis
Alexander Aue · University of California, Davis
A time series is an ordered sequence of values of a variable at equally spaced time intervals. Time series analysis accounts for the fact that data points taken over time may have an internal structure (such as autocorrelation, trend or seasonal variation) that should be accounted for.
Nguồn ngoài
Quantitative Research Methods for Political Science, Public Policy and Public Administration (With Applications in R)
Hank Jenkins-Smith; Joseph Ripberger · University of Oklahoma Libraries · 2017
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.
Nguồn ngoài
Python for Introductory Statistics
Simon Aman · City Colleges of Chicago · 2021
The purpose of this material is to provide an overview of the free programming language, Python, and a few of its software libraries that are most useful for improving Introductory Statistics students' learning and for providing an alternative statistical software package for those who perform statistical data analysis and plotting. This material, along with the companion workbook and a free web-based Python platform, could easily replace the use of other software packages and calculators in an Introductory Statistics course. Part I, Sections 1 to 4, covers the basics of the Python programming language. Part II, Sections 5 to 13, focuses on Python for introductory statistics, and Part III, Section 14, covers selected advanced statistics topics in Python. Additionally, there are demo video links accompanying key steps for further instruction. No prior programming knowledge is required or assumed.
Nguồn ngoài
Intermediate Statistics with R
Mark Greenwood · Montana State University · 2021
Introductory statistics courses prepare students to think statistically but cover relatively few statistical methods. Building on the basic statistical thinking emphasized in an introductory course, a second course in statistics at the undergraduate level can explore a large number of statistical methods. This text covers more advanced graphical summaries, One-Way ANOVA with pair-wise comparisons, Two-Way ANOVA, Chi-square testing, and simple and multiple linear regression models. Models with interactions are discussed in the Two-Way ANOVA and multiple linear regression setting with categorical explanatory variables. Randomization-based inferences are used to introduce new parametric distributions and to enhance understanding of what evidence against the null hypothesis “looks like”. Throughout, the use of the statistical software R via Rstudio is emphasized with all useful code and data sets provided within the text. This is Version 3.0 of the book.
Nguồn ngoài
Free Energy 1e (Snee)
Preston Snee · University of Illinois Chicago
Có trong mục lục: 5.04: Advanced Derivations- Maxwell Relationships II · 11: Boltzmann Statistics
Nguồn ngoài
SMC: Social Sciences Statistics
Bettina Spencer · Saint Mary's College
Có trong mục lục: 01: Introduction to Statistics · 1.4: Introduction to Statistics (Exercises) · 2.2: Definitions of Statistics, Probability, and Key Terms
Nguồn ngoài
ENVS C100: Environmental Science
Tanya Hoerer · Coastline College
Có trong mục lục: 1.3.02: Summarizing the data- Descriptive statistics · 1.3.03: Putting it all together- Inferential statistics and hypothesis testing · 2.5.9.07: Advanced Wastewater Treatment and Recycling
Nguồn ngoài
Introduction to Applied Statistics for Psychology Students
Gordon E. Sarty · University of Saskatchewan
Có trong mục lục: 02: Descriptive Statistics - Frequency Data (Counting) · 03: Descriptive Statistics- Central Tendency and Dispersion · 4.03: SPSS Lesson 3- Combining variables - advanced
Nguồn ngoài
Precalculus
CK12 · CK12
Có trong mục lục: 2.02: 2.2 Advanced Factoring · 15: Concepts of Statistics
Nguồn ngoài
Introductory Statistics
David Lane · Rice University
Có trong mục lục: 01: Introduction to Statistics · 1.01: What are Statistics · 1.02: Importance of Statistics
Nguồn ngoài
Quantitative Research Methods for Political Science, Public Policy and Public Administration
Jenkins-Smith et al. · University of Oklahoma
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.
Nguồn ngoài
Advanced Quantum Mechanics
Pieter Kok · University of Sheffield
Có trong mục lục: 8.4: Bose-Einstein and Fermi-Dirac Statistics
Nguồn ngoài
CHM 331 Advanced Analytical Chemistry 1
John Breen · Providence College
This LibreText is for the 1st course in analytical chemistry at Providence offered each fall to our Jr. chem & biochem majors. The subjects covered in the course include, measurements and statistics, simple optical spectroscopy in the UV and visible, separations, and mass spectrometry. I added Raman Spectroscopy in the summer of 2022. Chapters 1 - 5 are largely from Harvey's Modern Analytical Chemistry, Chapters 6 - 14 and 18 mimic Skoog's Instrumental Analysis, & Chapter 15 is a collection
Nguồn ngoài
Math in Society
David Lippman · Pierce College
Có trong mục lục: 10: Statistics · 16.04: Advanced shared symmetric-key methods · 18.10: Statistics
Nguồn ngoài
Statistical Mechanics
Daniel F. Styer · Oberlin College
Có trong mục lục: 6.06: Fermi-Dirac Statistics · 6.07: Bose-Einstein Statistics
Nguồn ngoài
Clinical Chemistry - Theory, Analysis, Correlation
Lawrence Kaplan & Amadeo Pesce · New York University & University of Cincinnati
Có trong mục lục: 1.16: Statistics and Reference Ranges · 4.47: Quality Control Advanced
Nguồn ngoài
Math for Liberal Arts
Ashley McHale · Las Positas College
Có trong mục lục: 6.04: Advanced shared symmetric-key methods · 14: Descriptive Statistics