# 11.3: Conditional Probability (Section 10.4)

- Page ID
- 8782

Let’s determine the conditional probability of someone being unhealthy, given that they are over 70 years of age, using the NHANES dataset. Let’s create a new data frame that

```
healthDataFrame <-
NHANES %>%
mutate(
Over70 = Age > 70,
Unhealthy = DaysPhysHlthBad > 0
) %>%
dplyr::select(Unhealthy, Over70) %>%
drop_na()
glimpse(healthDataFrame)
```

```
## Observations: 4,891
## Variables: 2
## $ Unhealthy <lgl> FALSE, FALSE, FALSE, TRUE, FALSE, TRUE,…
## $ Over70 <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALS…
```

First, what’s the probability of being over 70?

```
pOver70 <-
healthDataFrame %>%
summarise(pOver70 = mean(Over70)) %>%
pull()
# to obtain the specific value, we need to extract it from the data frame
pOver70
```

`## [1] 0.11`

Second, what’s the probability of being unhealthy?

```
pUnhealthy <-
healthDataFrame %>%
summarise(pUnhealthy = mean(Unhealthy)) %>%
pull()
pUnhealthy
```

`## [1] 0.36`

What’s the probability for each combination of unhealthy/healthly and over 70/ not? We can create a new variable that finds the joint probability by multiplying the two individual binary variables together; since anything times zero is zero, this will only have the value 1 for any case where both are true.

```
pBoth <- healthDataFrame %>%
mutate(
both = Unhealthy*Over70
) %>%
summarise(
pBoth = mean(both)) %>%
pull()
pBoth
```

`## [1] 0.043`

Finally, what’s the probability of someone being unhealthy, given that they are over 70 years of age?

```
pUnhealthyGivenOver70 <-
healthDataFrame %>%
filter(Over70 == TRUE) %>% # limit to Over70
summarise(pUnhealthy = mean(Unhealthy)) %>%
pull()
pUnhealthyGivenOver70
```

`## [1] 0.38`

```
# compute the opposite:
# what the probability of being over 70 given that
# one is unhealthy?
pOver70givenUnhealthy <-
healthDataFrame %>%
filter(Unhealthy == TRUE) %>% # limit to Unhealthy
summarise(pOver70 = mean(Over70)) %>%
pull()
pOver70givenUnhealthy
```

`## [1] 0.12`