5 The Many Variables & The Spurious Waffles

File setup

Code

library(tidyverse)

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Code

library(patchwork)

5.1 Original

Since most correlations do not indicate causal relationships, we need tools for distinguishing mere association from evidence of causation. This is why so much effort is devoted to multiple regression, using more than one predictor variable to simultaneously model an outcome.

Reasons given for multiple regression models include: 1. Statistical “control” for confounds 2. Multiple and complex causation 3. Interaction of variables

In this chapter, we begin use multiple regression to deal with simple confounds and to take multiple measurements of association.

5.1.1 Spurious association

# The Many Variables & The Spurious Waffles {#sec-chap05}

## File setup {.unnumbered}

```{r}
#| label: setup

library(tidyverse)
library(patchwork)
```

## Original

Since most correlations do not indicate causal relationships, we need
tools for distinguishing mere association from evidence of causation.
This is why so much effort is devoted to
`r glossary("multiple regression")`, using more than one predictor
variable to simultaneously model an outcome.


Reasons given for multiple regression models include: 1. Statistical
"control" for confounds 2. Multiple and complex causation 3.
`r glossary("Interaction")` of variables


In this chapter, we begin use multiple regression to deal with simple
confounds and to take multiple measurements of association.

### Spurious association