Introduction to Modern Statistics (openintro-ims2.netlify.app)

Article summary

The article introduces the second edition of the textbook 'Introduction to Modern Statistics' by Mine Çetinkaya-Rundel and Johanna Hardin. The book is available for free online and covers topics such as data analysis, regression modeling, and statistical inference. The textbook is part of the OpenIntro project and is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported United States License. The book's website also provides additional resources, including exercise solutions and references.

Main themes

• statistics education
• mathematical vs applied approach
• Bayesian vs frequentist methods
• programming languages in statistics
• textbook recommendations
• learning statistics

What commenters say

• Some commenters argue that a mathematical approach to learning statistics is the most effective way to gain a deep understanding of the subject.
• Others believe that an applied approach, using real-world examples and interactive tools, can be more effective for many learners.
• There is disagreement about the value of teaching Bayesian statistics versus traditional frequentist methods.
• Some commenters recommend specific textbooks, such as 'Statistical Inference' by Casella and Berger, as a good resource for self-learning.
• The use of programming languages like R and Python is also discussed, with some commenters arguing that R is still widely used in certain fields.
• Others suggest that Python or Julia may be more suitable for certain applications.
• Some commenters emphasize the importance of understanding the underlying mathematical theory of statistics, while others argue that a more practical, hands-on approach can be sufficient for many purposes.