## Description

The height of trees (m) of the same species is compared on two types of soil.

## Dependencies

If you use any packages, include them here, not where they are (first) used. This avoids multiple calls to the same package. You can add a short comment after each package explaining what it's used for.

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## Reading the data

Read the data and perform at least some data quality checks:

-   Do the dimensions (rows, columns) match what you expect? A common problem is when the delimiter is not recognized, in this case the number of columns.

-   Are numbers recognized as numeric?

-   Are categorical variables stored as `factor`, or `character`? In case of the latter, convert them to `factor`.

-   Are there any missing values? Any unrealistic values?

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If you note anything interesting, write this down over here, not in the code chunk.

## Visualizing the data

A simple visualization often goes a long way towards understanding the data. Create a figure with:

-   Soil type on the $x$-axis and tree height on the $y$-axis.

-   Informative axes with unit of measurement.

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If the visualization reveals anything important, write that down as well:

## Fitting the model

Choose the right model. Explain your choice below the code chunk.

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## Model diagnostics

Always perform visual diagnostics on models used for inference. In case of GLMs, pay particular attention to overdispersion.

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After running the diagnostic plots, comment on what you observe. Even if nothing stands out, explain how you decided that there are no issues.

## Interpreting the model output

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Write down what you see in the regression table, and write a conclusion in words.

## Visualize the estimated relationship

This is a nice addition for any report/presentation. Try using the `plot_model` function from `sjPlot` to visualize the fit.

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