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Fix typos #126

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merged 2 commits into from
Feb 26, 2024
Merged

Fix typos #126

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47b064b
Fix typos
Feb 26, 2024
7e1e978
Merge branch 'main' into hyweldd-episode_01-updates
alanocallaghan Feb 26, 2024
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4 changes: 2 additions & 2 deletions _episodes_rmd/01-introduction-to-high-dimensional-data.Rmd
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Expand Up @@ -73,7 +73,7 @@ knitr::include_graphics("../fig/intro-table.png")

> ## Challenge 1
>
> Descriptions of three research questions and their datasets are given below.
> Descriptions of four research questions and their datasets are given below.
> Which of these scenarios use high-dimensional data?
>
> 1. Predicting patient blood pressure using: cholesterol level in blood, age,
Expand Down Expand Up @@ -315,7 +315,7 @@ We will explore this type of analysis in the *Regression with many outcomes* epi
is typically used to fit regression models when there is a single outcome
variable or interest but the number of potential predictors is large, e.g.
there are more predictors than observations. Regularisation can help to prevent
over-fitting and may be used to identify a small subset of predictors that are
overfitting and may be used to identify a small subset of predictors that are
associated with the outcome of interest. For example, regularised regression has
been often used when building *epigenetic clocks*, where methylation values
across several thousands of genomic sites are used to predict chronological age.
Expand Down