1. Learn
    2. /
  2. Courses
    3. /
  3. Helsinki Open Data Science
  • 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

  • 4

    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

Connected

Exercise

Holding on to independence: The Linear model

Continuing to ignore the repeated-measures structure of the data, we will fit a multiple linear regression model with weight as response and Time and Group as explanatory variables.

Recall again from Chapter 1: Multiple regression that this is done by defining explanatory variables with the formula argument of lm(), as below

y ~ x1 + x2 + ..

Here y is again the target variable and x1, x2, .. are the explanatory variables.

Instructions

100 XP
  • Create a regression model with Weight as the response variable and Time and Group as explanatory variables
  • Print out the summary of the model
  • Observe 1) How Group2 and Group3 differ from Group1 conditional on Time and 2) The significance of the regression on Time