# The best DAWGS
Statistical model as golem is a great metaphor because it forces you to think from the model's perspective. Similar to when we
debug, the first step is to realize that the unwanted behaviors might not be a bug but exactly what you are asking for.
Note. By clicking on the headers, you'll be sent to the corresponding location in the lectures.
## [Context & discrimination](https://www.youtube.com/watch?v=Zi6N3GLUJmw&list=PLDcUM9US4XdPz-KxHM4XHt7uUVGWWVSus&index=9&t=4432s)
```js
mermaid`
graph LR
X[Status] --> Z(Context)
Z --> Y[Event]
X --> Y
style X fill:#fff, stroke-width:0px
style Y fill:#fff, stroke-width:0px
style Z fill:#fff, stroke-width:0px
`
```
For instance, in the Berkeley admission problem, we get that women might choose departments that are more competitive (think maths vs. psychology at graduate level), and maybe there is also a gender bias:
```js
mermaid`
graph LR
X[Gender] --> Z[Department]
Z --> Y[Admission]
X --> Y
style X fill:#fff, stroke-width:0px
style Y fill:#fff, stroke-width:0px
style Z fill:#fff, stroke-width:0px
`
```
The gender bias is confounded by department choice. Or, more controversially
```js
mermaid`
graph LR
u((hidden quality))
X[Gender] --> Z[Department]
Z --> Y[Admission]
X --> Y
u -.-> Y
u -.-> Z
style X fill:#fff, stroke-width:0px
style Y fill:#fff, stroke-width:0px
style Z fill:#fff, stroke-width:0px
style u fill:#fff, stroke-dasharray: 5 5
`
```
---
## [Varying confound](https://www.youtube.com/watch?v=sgqMkZeslxA&list=PLDcUM9US4XdPz-KxHM4XHt7uUVGWWVSus&index=13)
```js
mermaid`
graph LR
U((Neighborhood))
U --> Z
U -->Y
Z --> Y[Children]
X[Granparents] --> Z(Parents)
X --> Y
style X fill:#fff, stroke-width:0px
style Y fill:#fff, stroke-width:0px
style Z fill:#fff, stroke-width:0px
style U fill:#fff, color:#AA4A44, stroke:#AA4A44, stroke-dasharray: 5 5
linkStyle 1 stroke:#AA4A44
linkStyle 0 stroke:#AA4A44
`
```
It is similar to context & discrimination, but here we have a _collider_. For instance, in the children education problem, parents influence children outcome. But grand parents and neighborhod (a proxy for socioeconomic status), also influence children outcome AND parents. Similarly, one could see the following model of becoming a National Academy of Science, given citation patterns, gender, and hidden quality of the works:
```js
mermaid`
graph LR
X[Gender]
Y[Citations]
Z[NAS member]
U((quality))
U --> Y
U --> Z
Y --> Z
X --> Y
X --> Z
style U fill:#fff, color:#AA4A44, stroke:#AA4A44, stroke-dasharray: 5 5
linkStyle 1 stroke:#AA4A44
linkStyle 0 stroke:#AA4A44
style X fill:#fff, stroke-width:0px
style Y fill:#fff, stroke-width:0px
style Z fill:#fff, stroke-width:0px
`
```
---
## [Oceanic tool](https://www.youtube.com/watch?v=jokxu18egu0&list=PLDcUM9US4XdPz-KxHM4XHt7uUVGWWVSus&index=10)
```js
mermaid`
graph LR
X[Population]
Y[Tools]
Z[Contact]
L[Location]
X --> Y
X --> Z
L --> X
L --> Z
L --> Y
Z --> Y
style L fill:#fff, stroke-width:0px
style X fill:#fff, stroke-width:0px
style Y fill:#fff, stroke-width:0px
style Z fill:#fff, stroke-width:0px
`
```
---
## [Survival Analysis](https://www.youtube.com/watch?v=Zi6N3GLUJmw&list=PLDcUM9US4XdPz-KxHM4XHt7uUVGWWVSus&index=9)
```js
mermaid`
graph LR
`
```
---
## Got milk? multicollinearity example (rethinking p.173)
```js
mermaid`
graph LR
L((Lactose))
F[[Fat]]
K
L --> K
D --> L
D --> F
F --> K
style D fill:#fff, color:#AA4A44, stroke:#AA4A44, stroke-dasharray: 5 5
linkStyle 1 stroke:#AA4A44
linkStyle 0 stroke:#AA4A44
linkStyle 3 stroke:#AA4A44
linkStyle 2 stroke:#AA4A44
style L fill:#fff, stroke-width:0px
style K fill:#fff, stroke-width:0px
style F fill:#fff, stroke-width:0px
`
```