Category Archives: apsi

APSI Beginning Statistics Day 2


So much information. Landy Godbold, our instructor, is so good at story telling…I get wrapped up in discussion of tasks that I forget to jot down key points and big ideas.

1. One of my favorite tasks todays was when we were asked to sketch box-plot with varying lengths in each interval.  Then we had to create a possible histogram for that same data.  Thinking was going on.  You could see it on our faces.

I believe we don’t ask our students open questions like this enough.  An add on…create another possible histogram for the same box plot.  For some, actually creating a set of data was the approach. 

My question for students…Can 2 different sets of data actually result in the same box-plot?  And let them explore.

Create 2 data sets that match a single boxplot yet have vastly different histograms. 

These are not as simple as I would have expected initially. 

2.  Another task today was with a single set of data.  We created our histogram using zoom:9 feature.  Then we were split into 4 groups and asked to graph using different xmin values.  Great point.  I knew it was possible but did not expect such a big difference in our resulting graphs.

3.  In a group of 4, each person ran 1-var stats for their specific data set, (mckenzie set) then asked to sketch possible graph using our descriptive stats.  Then we were asked to create an actual histogram and compare the 2. 


Please dont judge  my sketches…these are all from memory since I was too absorbed in discussions to write down actual examples!

The thing I appreciate about our class, I am being asked to look at things through a new lense.  I am thinking.  I am learning.  I like it. A lot.  Hoping I can learn enough between this week and TMC to help my students find their own success!

The Lady Not Tasting Tea #apsi Day 1


Yep. Within minutes of walking into our classroom, I spilled my tea.  My super size it tea.  I made a friend for life, Teresa, as she ran down the hall with me to collect paper towels.  A.lot. of paper towels.  Did I say it was super size? running across the floor.  Bless the people around me for jumping in to be my friends.  Thank you, again for helping me.  Thank goodness I drink unsweet tea, no sticky mess.

Flipping through my INB from Day 1.  oh my.  Class is what I expected but not what I expected.
We are collecting data, somewhat simple things…yet it is the discussions that add to my take-aways.

Landy Godbold from Atlanta is our instructor.  He is good.  Very good.  His tshirt day one read:  Statistics mean never having to say you’re certain.  He says we need to get our students in the mindset: Statistics is about stories.  And he has whiteboards!!!  Love this guy.

Big idea for the day…week…variability.

E.A.C.  -Evidence. Argument. Conclusion.

Hey there’s that word again…ubiquitous…its everywhere.

Tell me the story!  Your students become lawers and must layout their case to the jury.  What do you want me to see, link it to the evidence and make your conclusion.

And the big question of the day… Compared to what?

What is typical?  How do I decide if this is typical or not?
Data set 1: What is the fastest speed you traveled in a car?

Story 1: Driving with cell phone is dangerous.  Given a two-way table and asked if it was typical…compared to what?

Simulation 1: come up with a method to simulate experiment using a deck of cards.  Discussion of ideas.  Ran experiment, combined data and came to a conclusion.
Data set 2: number of drivers, talking on cell phone who missed their exit from the simulation.

Do I really understand what the plot represents?  Where does my statistic of my sample fit?

Case, Sample, Population
Case-subject, variable -information about my case (categorical or numerical)
Sample, statisitc-always numerical
Population-everything that can happen, parameter

How often should I identify these myself to get it down?  I feel these are things that must become second nature for my students.  I suppose I will ask for these quite often.

Case:  driver talking on cell phone or passenger,
Variable: missed exit or didn’t
Sample: # talking on cell phone & missed the exit
Population: 48 who volunteered, bc experimental, really no population…

Data Set 3: measure length of peanuts in the shell
Data set 4: desk widths

When we begin to look at stem-leaf plot, is the detail there? Am I seeing what I want to see?  May need to split depending on size of set and am I seeing what I need to see?  How do we know when to split or not…let my sudents play around with it-let them see it, explore, build in fiddle time for them to experience it.

With the peanut data, based on our 6 pieces of data, we were asked to predict what we thought graph would look like. 
Everyone showed their white boards.  We got to asked questions….”why the dips/gaps?”  I wondered in a few of others’ sketches…

Unexpected to me to see bimodal behavior. 
The center and spread of bimodal is meaningless…discuss the clusters, and behavior around those. 
Pine needles from 2 types of trees always rssult in bimodal.

Did you know saving list of data in a program on TI84 saves memory?  handy tidbit to know.  Though he recommends Nspire, majority of participants have 84s available.

shape-describe patterns, peak, symmetry, skew
exceptions-outliers, gaps
center-what does my eye see?
spread-typical values
Try to create an image with your words.

These are some highlights from my first day at APSI at WKU.  Please feel free to offer corrections, ideas, clarify.  This truly is meant as a reflection to help me begin sorting all that I am learning this week.  I may not have tasted my tea on Monday, but I left with my mind running over…

Oh yeah, if you missed my tweet… @NationWide insurance sent me a giftcard to go purchase a #brandnew iced tea.  Turned my frown upside down!