Study Guide with Analysing Data using Linear Models by Van den Berg

Study guide with Analysing Data using Linear Models

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

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