## Description

These data are 10-year incidence rates of ischemic stroke. The central question is whether smoking and hypertension interact (does the combination of both increase the risk of ischemic stroke more than their individual contributions).

## 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:

-   The incidence of ischemic stroke for each combination of smoking and hypertension.

-   Informative axes and a legend.

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

## Fitting the model

Choose the right model, and decide which terms it needs. 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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