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  • 1

    Regression and model validation

    Data wrangling, simple regression, multiple regression, regression diagnostics

  • 2

    Logistic regression

    Regression for binary outcomes, training and testing a (predictive) model, cross-validation

  • 3

    Clustering and classification

    Datasets in R, Linear Discriminant Analysis (LDA) and K-means clustering

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    Dimensionality reduction techniques

    Principal component analysis (PCA), Correspondence analysis (CA)

  • 5

    Analysis of longitudinal data

    Graphical Displays and Summary Measure Approach, Linear Mixed Effects Models for Normal Response Variables

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Exercise

Good things come in Summary graphs

With large numbers of observations, graphical displays of individual response profiles are of little use and investigators then commonly produce graphs showing average (mean) profiles for each treatment group along with some indication of the variation of the observations at each time point, in this case the standard error of mean

$$se = \frac{sd(x)}{\sqrt{n}}$$

Instructions

100 XP
  • Create the summary data BPRSS with the mean and standard error of the variable bprs
  • Glimpse the data
  • Plot the mean profiles (with geom_errorbar() line commented out)
  • Uncomment the geom_errorbar() line and plot the mean profiles again
  • Note the considerable overlap in the mean profiles of the two treatment groups suggesting there might be little difference between the two groups in respect to the mean BPRS values