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Angry Birds?

Recently, Minitab’s Joel Smith posted about his vacation and being pooped on twice by birds. Then guest blogger Matthew Barsalou wrote a wonderful follow-up on the chances of Joel being pooped on a third time. While I cannot comment on how Joel has handled this situation psychologically so far, I can say that if I had been pooped on twice in a short amount of time, I would be wary of our feathered friends for quite a while.

falcon

If Joel is experiencing any ongoing distress about these recent events, one avenue he could take is birdwatching. Ebird.org allows birdwatchers to easily keep track of their observations. By observing and documenting what he sees, Joel could take a more proactive approach in avoiding a third "encounter." For example, here is what birdwatcher Ron Crandall documented on December 14, 2014, on ebird:

Observational location: PSU (Univ. Park)--central campus

Red-tailed Hawk

2

Rock Pigeon

2

Blue Jay

2

Tufted Titmouse

3

White-breasted Nuthatch

2

Golden-crowned Kinglet

2

White-throated Sparrow

8

Dark-eyed Junco

34

American Goldfinch

3

We can look at this data visually by using Minitab’s Pareto Chart, which can be found under Stat > Quality Tools > Pareto Chart. (If you want to follow along and don't already have Minitab on your computer, please download the free trial.) The dialog window would be filled out as shown below:

Here are the results after pressing OK: 

Pareto charts are a very common tool, and a very common question I receive from Minitab users is “How do I remove the table located below the Pareto Chart?" To delete the table, you simply have to select the labels ‘Observational Count’, ‘Percent’, and ‘Cum %’, one at a time and hit the delete key. However, the only way I have found to bring these rows back is by going to Edit > Undo. So if you’ve made multiple changes to your project since you deleted this Pareto table, you’ll have to either recreate the graph or undo multiple times!

It looks like Ron documented that he had more visual contact with the Dark-eyed Junco than any other bird that day. Was does that mean for Ron? Most likely...nothing.  If Joel were to take up bird watching, it may tell him how many are in visual range and from what species they belong to, but it probably won't tell him if he's being targeted. That is, unless, he finds himself in Tippi Hedren's situation in Hitchcock's The Birds. (Incidentally, I was having a hard time finding information on how many birds were found for the movie. One web site mentions that 20,000 crows were captured in Arizona, and that's just one location.)

Maybe a statistical analysis like a  binary logistic regression can shed more light on this matter. You use a binary logistic regression to perform logistic regression on a binary response variable. A binary response variable has two possible values, the event and non-event. You could then attempt to model a relationship between various predictor variables and this binary response.

Let’s say, hypothetically, Joel constructs a helmet that could track when a bird was directly overhead of him. The helmet could also track if they attempted to poop on him, and we could define this as the “event.” When the bird didn't poop, we could define this as the “non-event.” The predictors can be continuous or categorical in nature. Three hypothetical (and quite silly) variables of interest could be outdoor temperature, whether one is wearing a Scarecrow outfit, and the loudness of the boombox that person is carrying.

Over course of some time, a data sheet might start to look like this:

Under C1, “1” represents the event and “0” represents the non-event. To analyze this in Minitab, you would go to Stat > Regression > Binary Logistic Regression > Fit Binary Logistic Model, and fill in your dialog window has follows:

If you wanted to add interactions, you’d need to go into the Model… sub-menu:

To add the interaction between Temperature and ‘db levels...”, you’d  follow these steps:

  1. Shift select on your keyboard both Temperature and dB levels… under the Predictors  box.
  2. Under the interactions through order drop down, choose 2.
  3. Click on the Add button.

You’d see Temperature* ‘DB levels of carried boombox’ under the Terms in the model box. After hitting "OK" in a few dialog windows, your results will show in the session window.

Hopefully there will not be a third time for Joel.  If there is, he may take solace in that some people view it as good luck. However...my shirt would disagree. For more information on how to interpret results from a binary logistic regression, please click on the links below.

Analyzing Titanic Survival Rates, Part II: Binary Logistic Regression

Interpreting Halloween Statistics with Binary Logistic Regression

Using Binary Logistic Regression to Investigate High Employee Turnover

 

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