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?

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