Summary of Analysing Data using Linear Models by Van den Berg - 2022 edition

Summaries per chapter with Analysing Data using Linear Models

Summaries per chapter with Analysing Data using Linear Models

• For summaries with all chapters of the 2022 edition of Analysing Data using Linear Models by Van den Berg, see the supporting content of this page

Table of content

• Chapter 1 - What are variables, variation and co-variation?
• Chapter 2 - How can we make inferences about a mean?
• Chapter 3 - How can we make inferences about proportions?
• Chapter 4 - What does linear modelling entail?
• Chapter 5 - How can we make inferences about linear models?
• Chapter 6 - What are categorical predictor variables?
• Chapter 7 - What are the assumptions of linear models?
• Chapter 8 - What should we do when the assumptions are not met?
• Chapter 9 - What does moderation entail?
• Chapter 10 - How do researchers use contrast in statistical analysis?
• Chapter 11 - How do we perform post hoc comparisons?
• Chapter 12 - How do we perform linear mixed modelling?
• Chapter 13 - How do we conduct linear mixed models for more than two measurements?
• Chapter 14 - What are non-parametric alternatives for linear mixed models?
• Chapter 15 - How is logistic regression conducted with generalised linear models?
• Chapter 16 - How can generalised linear models be used for count data?
• Chapter 17 - What does big data analytics entail?

Related summaries and study assistance

• For all available bulletsummaries, booksummaries, practice questions, glossaries en studytips with Analysing Data using Linear Models by Van den Berg, see Study guide with Analysing Data using Linear Models by Van den Berg