| year | quarter | average |
|---|---|---|
| 2021 | Fall | 4.53 |
| 2021 | Fall | 4.36 |
| 2022 | Winter | 4.18 |
| 2022 | Winter | 4.24 |
| 2022 | Spring | 4.83 |
| 2022 | Spring | 4.41 |
| 2022 | Spring | 4.00 |
“Complete” = Satisfactory
“Incomplete” = Growing
A code chunk option is declared after a #|. Here are some options we may want to use:
#| label: packages – creates a label for the code chunk (describing its contents)#| echo: false – tells Quarto not to output the code in the rendered HTML (only the output)#| include: false – tells Quarto not to include the code or the output in the rendered HTMLThe glimpse() function is a great tool to preview the dataset you are working with! It gives you:
chr, dbl)Now that we’ve practiced making some plots, we know…
mapping = aes(y = manufacturer, x = hwy) declares what variables are plotted on the x- and y-axis.
Tip
The variable names you put insides aes() must be identical to the names of the variables in the dataset!
labs(x = "Highway Miles Per Gallon", y = "Car Manufacturer") declares new x- and y-axis labels for the plot.
Tip
Including nice axis labels (with their units) is a critical part of every visualization we make!
Lab 1 revisions are due by Wednesday, April 17 (at midnight).
Reflections
Revisions are required to be accompanied with reflections on what you learned while completing your revisions. These can be written in your Lab 1 Quarto file (next to the problems you revised), in a Word document, or in the comment box on Canvas.
15-minutes
Suppose…
“Overall this instructor was educationally effective.”
| year | quarter | average |
|---|---|---|
| 2021 | Fall | 4.53 |
| 2021 | Fall | 4.36 |
| 2022 | Winter | 4.18 |
| 2022 | Winter | 4.24 |
| 2022 | Spring | 4.83 |
| 2022 | Spring | 4.41 |
| 2022 | Spring | 4.00 |
How were those averages calculated?
What do these averages mean?
The Problem
It’s incredibly rare for scientists, including statisticians, to explicitly think about that conditions underlying their models.
“I’ve had many conversations in very different contexts with scientists about what the average calculated from the data (or mean in a model) could reasonably represent and whether that was really what the scientist was after.” Dr. Megan Higgs
Why so much resistance?
Departments hold specific expectations of statistics courses
These expectations are conditional on the assumption that means represent the magic quantity of interest.
I’m then expected to educate you to “play the game” in the scientific culture of averages
Averagarianism
“The primary research method of averagarianism is aggregate, then analyze: First, combine many people together and look for patterns in the group. Then, use these group patterns (such as averages and other statistics) to analyze and model individuals. The science of the individual instead instructs scientists to analyze, then aggregate: First, look for pattern within each individual. Then, look for ways to combine these individual patterns into collective insight.”
The End of Average by Todd Rose
“We’ve always done it this way”
Methods based on averages are available, easy, convenient, and take little creativity — and they are expected in our scientific culture.
Justification for using averages is simply not demanded — though justification for use of anything but averages is incredibly difficult to sell.
Some Rules to Play By
nycflights datasetbinwidthfilter() data to include only certain flightscalculate() summary statistics