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.

Mathematics

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

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Mục lục· 91 mục

1.6.01: Data Visualization
1.6.1.01: Anatomy of a Plot
1.6.1.02: Principles of Good Visualization
1.6.1.03: Accommodating Human Limitations
1.6.1.04: Correcting for Other Factors
1.6.1.05: Suggested readings and videos
1.6.02: Data
1.6.2.01: Origin of the data
1.6.2.02: Population and sample
1.6.2.03: How to obtain the data
1.6.2.04: What to find in the data
1.6.2.05: Answers to exercises
1.6.03: How to process the data
1.6.3.01: General purpose software
1.6.3.02: Statistical software
1.6.3.03: The very short history of the S and R
1.6.3.04: Use, advantages and disadvantages of the R
1.6.3.05: How to download and install R
1.6.3.06: How to start with R
1.6.3.07: R and Data
1.6.3.08: R graphics
1.6.3.09: Answers to exercises
1.6.04: Types of Data
1.6.4.01: Degrees, hours and kilometers- measurement data
1.6.4.02: Grades and t-shirts- ranked data
1.6.4.03: Colors, Names and Sexes - Nominal Data
1.6.4.04: Fractions, counts and ranks- secondary data
1.6.4.05: Missing data
1.6.4.06: Outliers, and how to find them
1.6.4.07: Changing data- basics of transformations
1.6.4.08: Inside R
1.6.4.09: Answers to exercises
1.6.05: One-Dimensional Data
1.6.5.01: How to Estimate General Tendencies
1.6.5.02: 1-Dimensional Plots
1.6.5.03: Confidence intervals
1.6.5.04: Normality
1.6.5.05: How to create your own functions
1.6.5.06: How good is the proportion?
1.6.5.07: Answers to exercises
1.6.06: Two-Dimensional Data - Differences
1.6.6.01: What is a statistical test?
1.6.6.02: Is there a difference? Comparing two samples
1.6.6.03: If there are More than Two Samples - ANOVA
1.6.6.04: Is there an association? Analysis of tables
1.6.6.05: Answers to exercises
1.6.07: Two-Dimensional Data - Models
1.6.7.01: Analysis of Correlation
1.6.7.02: Analysis of regression
1.6.7.03: Probability of the success- logistic regression
1.6.7.04: Answers to exercises
1.6.08: Multidimensional Data - Analysis of Structure
1.6.8.01: How to draw the multivariate data
1.6.8.02: Classification without learning
1.6.8.03: Machine learning
1.6.8.04: Semi-supervised learning
1.6.8.05: Deep Learning
1.6.8.06: How to choose the right method
1.6.8.07: Answers to exercises
1.6.09: Appendices
1.6.9.01: Example of R session
1.6.9.1.01: Starting...
1.6.9.1.02: Describing...
1.6.9.1.03: Plotting...
1.6.9.1.04: Testing...
1.6.9.1.05: Finishing...
1.6.9.1.06: Answers to exercises
1.6.9.02: Ten Years Later, or use R script
1.6.9.2.01: How to make your first R script
1.6.9.2.02: My R script does not work!
1.6.9.2.03: Common pitfalls in R scripting
1.6.9.2.04: Good, Bad, and Not-too-bad
1.6.9.2.05: Answers to exercises
1.6.9.03: R fragments
1.6.9.3.01: R and databases
1.6.9.3.02: R and time
1.6.9.3.03: R and Bootstrapping
1.6.9.3.04: R and shape
1.6.9.3.05: R and Bayes
1.6.9.3.06: R, DNA and evolution
1.6.9.3.07: R and reporting
1.6.9.3.08: Answers to exercises
1.6.9.04: Most essential R commands
1.6.9.05: The short R glossary
1.6.9.06: References and reference cards
1.6.10: Examining the Evidence using Graphs and Statistics
1.6.10.0E: 1.6.10.E: Examining the Evidence using Graphs and Statistics (Exercises)
1.6.11: Graphical Descriptions of Data
1.6.11.01: Qualitative Data
1.6.11.02: Quantitative Data
1.6.11.03: Other Graphical Representations of Data