Nguồn ngoài
Visual Statistics Use R!
The ignorance of the statistical methods can lead to mistakes and misinterpretations. Unfortunately, understanding of these methods is far from common. Many college majors require a course in probability theory and mathematical statistics, but all many of us remember from these courses is horror and/or frustration at complex mathematical formulas filled with Greek letters, some of them wearing hats.
- Tác giả
- Alexey Shipunov
- Nhà xuất bản
- Minot State University
- Nguồn
- LibreTexts
- Giấy phép
- CC0 / Public domain
- Ngôn ngữ
- EN
- Danh mục
- Khoa học cơ bản
Mục lục· 75 mục
- 01: Data
- 1.01: Origin of the data
- 1.02: Population and sample
- 1.03: How to obtain the data
- 1.04: What to find in the data
- 1.05: Answers to exercises
- 02: How to process the data
- 2.01: General purpose software
- 2.02: Statistical software
- 2.03: The very short history of the S and R
- 2.04: Use advantages and disadvantages of the R
- 2.05: How to download and install R
- 2.06: How to start with R
- 2.07: R and Data
- 2.08: R graphics
- 2.09: Answers to exercises
- 03: Types of Data
- 3.01: Degrees hours and kilometers- measurement data
- 3.02: Grades and t-shirts- ranked data
- 3.03: Colors Names and Sexes - Nominal Data
- 3.04: Fractions counts and ranks- secondary data
- 3.05: Missing data
- 3.06: Outliers and how to find them
- 3.07: Changing data- basics of transformations
- 3.08: Inside R
- 3.09: Answers to exercises
- 04: One-Dimensional Data
- 4.01: How to Estimate General Tendencies
- 4.02: 1-Dimensional Plots
- 4.03: Confidence intervals
- 4.04: Normality
- 4.05: How to create your own functions
- 4.06: How good is the proportion
- 4.07: Answers to exercises
- 05: Two-Dimensional Data - Differences
- 5.01: What is a statistical test
- 5.02: Is there a difference Comparing two samples
- 5.03: If there are More than Two Samples - ANOVA
- 5.04: Is there an association Analysis of tables
- 5.05: Answers to exercises
- 06: Two-Dimensional Data - Models
- 6.01: Analysis of Correlation
- 6.02: Analysis of regression
- 6.03: Probability of the success- logistic regression
- 6.04: Answers to exercises
- 07: Multidimensional Data - Analysis of Structure
- 7.01: How to draw the multivariate data
- 7.02: Classification without learning
- 7.03: Machine learning
- 7.04: Semi-supervised learning
- 7.05: Deep Learning
- 7.06: How to choose the right method
- 7.07: Answers to exercises
- 08: Appendix A- Example of R session
- 8.01: Starting...
- 8.02: Describing...
- 8.03: Plotting...
- 8.04: Testing...
- 8.05: Finishing...
- 8.06: Answers to exercises
- 09: Appendix B- Ten Years Later or use R script
- 9.01: How to make your first R script
- 9.02: My R script does not work
- 9.03: Common pitfalls in R scripting
- 9.04: Good Bad and Not-too-bad
- 9.05: Answers to exercises
- 10: Appendix C- R fragments
- 10.01: R and databases
- 10.02: R and time
- 10.03: R and Bootstrapping
- 10.04: R and shape
- 10.05: R and Bayes
- 10.06: R DNA and evolution
- 10.07: R and reporting
- 10.08: Answers to exercises