Interpreting Data Visualizations

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# Interpreting Data Visualizations

## Dr. Mine Dogucu

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## Reminder

- Close all apps on your computer other than zoom.  
- Open slides for this session from the cluster website (https://uci-dshs.netlify.app/).

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[Limbo Lines: Dead Here, Alive There](https://pudding.cool/2018/02/death/)

[Anxiety: It Gets Worse, Then It Gets Better](https://www.instagram.com/p/B4U81DJlqTG/)

[The Wealth & Health of Nations](https://observablehq.com/@mbostock/the-wealth-health-of-nations)

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Data Visualizations

- are graphical representations of data

- use different colors, shapes, and the coordinate system to summarize data

- tell a story

- are useful for exploring data

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class:inverse middle

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## Bar plot

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<img src="02b-interpret-dataviz_files/figure-html/unnamed-chunk-4-1.png" style="display: block; margin: auto;" />
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class:inverse middle

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## Box plot

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- The horizontal line inside the box represents the median.
- The box itself represents the middle 50% of the data with Q3 on the upper end and Q1 on the lower end. 
- Whiskers extend from the box. They can extend up to 1.5 IQR away from the box (i.e. away from Q1 and Q3). 
- The points are potential outliers that represent babies with really low or high birth weight.

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## Histogram

Bin width = 5 ounces

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Bin width = 20 ounces

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[Exploring Histograms Interactively](http://tinlizzie.org/histograms/)

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[There is no "best" number of bins](https://en.wikipedia.org/wiki/Histogram#Number_of_bins_and_width)

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## Fun fact

__histo__ comes from the Greek word _histos_ that literally means "anything set up right".

__gram__: comes from the Greek word _gramma_ which means "that which is drawn".

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## Histogram vs. Boxplot

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Tail tells the tale.

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## In Breakout Rooms

Discuss:

- In right-skewed distributions mean > median, true or false?
- In left-skewed distributions mean > median, true or false?

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## Standardized Bar Plot

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## Dodged Bar Plot

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## Side-by-side box plots

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## Scatter plots

Length of gestation can **possibly** eXplain a baby's birth weight. 
Gestation is the eXplanatory variable and is shown on the x-axis.
Birth weight is the response variable and is shown on the y-axis.

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## Linear Relationship

In Week 3, we will start statistical modeling during which we will numerically define the relationship between gestation and birth weight. 
For now we can say that this relationship is positive and moderate.