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missing word? #3

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4 changes: 2 additions & 2 deletions eda_checklist.Rmd
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Expand Up @@ -260,7 +260,7 @@ Let's take a look at one of the higest level counties, Mariposa County, Californ
filter(ozone, State.Name == "California" & County.Name == "Mariposa") %>% nrow
```

Always be checking. Does that number of observations sound right? Well, there's 24 hours in a day and 365 days per, which gives us `r 24 * 365`, which is close to that number of observations. Sometimes the counties use alternate methods of measurement during the year so there may be "extra" measurements.
Always be checking. Does that number of observations sound right? Well, there's 24 hours in a day and 365 days per year, which gives us `r 24 * 365`, which is close to that number of observations. Sometimes the counties use alternate methods of measurement during the year so there may be "extra" measurements.

We can take a look at how ozone varies through the year in this county by looking at monthly averages. First we'll need to convert the date variable into a `Date` class.

Expand Down Expand Up @@ -346,7 +346,7 @@ Here we can see that the bottom 7 counties are identical in both rankings, but a

In this chapter I've presented some simple steps to take when starting off on an exploratory analysis. The example analysis conducted in this chapter was far from perfect, but it got us thinking about the data and the question of interest. It also gave us a number of things to follow up on in case we continue to be interested in this question.

At this point it's useful to consider a few followup questions.
At this point it's useful to consider a few follow up questions.

1. **Do you have the right data?** Sometimes at the conclusion of an exploratory data analysis, the conclusion is that the dataset is not really appropriate for this question. In this case, the dataset seemed perfectly fine for answering the question of which counties had the highest levels of ozone.

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