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.

This exercise is part of the course

Helsinki Open Data Science

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Exercise instructions

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

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# dplyr, tidyr, RATS and RATSL are available

# create a regression model RATS_reg
RATS_reg <- "Regression model here!"

# print out a summary of the model

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