I merged the study of triangle trigonometry and polygon area in my geometry class, since they go together very well. For their test, I created this multi-part area problem I like quite a bit. You can click the image to access the Geogebra sketch I used to make it on GeogebraTube if you’d like to download and modify it.
I had a hard time coming up with a candy activity for bivariate data that was really good. I ended up kind of cheating, and wrote an activity that has students gather lots of data points, one of which is “how many jelly beans can you pick up in one no-palm pinch?” and then letting them check the correlation of that with any of a wide variety of data points (height, hair length, finger length, age in days, etc.).
I don’t think it is as good, or will allow for quite as deep a discussion, as the earlier activities, but it is still decent I think.
I still haven’t actually taught regression inference, so I’m using this activity right after a brief mini-lesson on interpreting and using computer output for regression inference. I definitely will not be tackling this topic at the depth our book does; will focus instead on practicality and the basic idea of extending our knowledge of T-tests and confidence intervals to a more uncharted area. Some of the data sets they decide to plot will probably NOT satisfy all of the conditions for regression inference, though, so that could lead to a good discussion.
You can find all of my AP Stat candy activities, including this one (forgive any typos, it was made very late last night) here: https://drive.google.com/open?id=0B-C-lUvv4rQ4ZG9hcnh1azdHNjQ&authuser=0
Yesterday I did a chi-square goodness-of-fit test with my class comparing a large sample of M&Ms – over 800 of them – to the data that is provided by Mars for the true count of M&Ms. We got a p-value of 0.0002, which seems crazy. So now I simply need to know how accurate their data actually is.
So here is my proposal: if you do an activity that involves counting the various colors of M&ms in any random sample, any year, add your data to my collection using the form below. If you buy a bags of M&Ms and just feel like counting it, add data. If you want to put young children to work counting M&M colors, add their data too. If you have data from previous years, great! Add that too, and maybe I can add a time component to the analysis. If we get enough people on board, we should start to get an accurate picture of the true proportion of M&M colors, to see if Mars tells it true.
I have embedded the form and part of the analysis below, but you can also click here to access the full Google Sheet with the analysis of the total data, analysis by year (currently only 2015 makes sense obviously) submission-by-submission analysis, and original results.
[Note: I also had to make my own chi-square cumulative distribution function for Google Sheets, borrowing some source code from this online calculator at UCLA. If you want to know how to use it, or make your own custom Google Sheets functions, e-mail me and I can advise.]
As of now, I have written 3 AP Statistics review activities centered around candy. You can access them all by clicking here. They are taking us around 2 class periods each, which is a full 160 minutes of class time; not a short commitment by any means, but I’m enjoying the long game connections a LOT.
- relative frequency tables
- sample design, including
- voluntary response sample
- convenience sample
- simple random sample
- cluster sampling
- stratified sampling
- multistage sampling
- collecting a sample and creating frequency and relative frequency tables
- displaying categorical data with pie charts and bar graphs
- two-way tables
- marginal and conditional distributions
- sampling distribution of sample proportions
- confidence intervals for population proportions
- 1 proportion z tests
- Chi-square GOF tests
- power of a test
- calculating power (as an exercise in reviewing tests and CIs)
- 1-proportion z tests (again)
- 2-proportion z-tests
- gathering data
- review chi-square GOF with several possible distributions and sample sizes (exploring power, etc, along the way)
- check percentage of actual rejected hypotheses against alpha
- represent quantitative data visually and use SOCS
- confidence interval for sample means
- effect of sample size and alpha on power
- 2-sample mean t-test
Inspired by the general enthusiasm for the process in the MtBOS, I tried my first 3 Act Math Lesson(s) today.
Boat on the River
- It is 9.1 m tall, 0.61 m thick.
- It is made of steel plates attached to a skeleton – basically hollow!
- A real nickel is 2.12 cm tall, 1.27 mm thick
The Great Geometry Soccer Goal project ended a couple of weeks ago just before spring break, and I’m finally ready to finally debrief. I already wrote many of my thoughts at the mid-point of the project, so this one will be brief.
If you’re interested in seeing the products, here are the videos they made. They vary in polish, but all show the scope of the project well This was not the only product, but it is currently the only publicly available one; need to do some name removing, etc, before I can share snippets from reports or instructions.
Was it worth it?
Yes. I may streamline it a tad to take 4 class days instead of 5 next year (our class days are 80 minutes), but it’s worth it either way.
Did they learn anything that will help with their test?
Probably not. We learned tangent ratios, and practiced the Pythagorean theorem and some arithmetic, but especially with spring break right after they still had to be retaught tangent on our return.
So what DID they get out of it?
Visual thinking with 3D shapes. Recognition of the practical value of certain math topics. Mathematical communication skills. Learning to use a saw. Value of precision, but also seeing where there is room for a little error. Fun! Honestly, though, i think the mathematical communication and 3D manipulation skills are probably the most “measurable” for a math class.
I will probably use TinkerCAD next year instead of 3DTin, just because 3DTin seems to be a dying project, even though I prefer it a bit. I may skip the 3D modeling entirely, since it’s time consuming for the outcome, but I really like it so I’ll probably keep it. I will tweak the job assignments, but not much; my hard work in advance worked out well there. I will pray for no snow days. I may add other elements: have them calculate the cost of the goal using the Lowes website, for example.
I highly recommend this project, or one inspired by it, if you have the ability to buy and cut PVC with your students. It was a lot of fun.
Today in AP Statistics we continued the Great Candy Review by comparing Starburst proportions to the skittles proportions; specifically, we started trying to decide if the proportion of orange starbursts could be equal to the proportion of orange skittles.
The activity covers both 1-sample proportion tests (by assuming that 20% of skittles are orange, as we surmised, and comparing our starburst sample proportion to 0.2) and then 2-sample proportion tests by dropping that assumption and comparing our actual samples, but before I dove into the tests I decided to spend some time dwelling on power.
This is my first time teaching this course, and I haven’t always figured out until too late what aspects to prioritize. Power is hard, it comes near the end of a chapter, and I skimmed it.
Really thinking about the power of a test, even calculating it, turns out to be an extremely good way to really think about the underlying concepts of statistical inference. It took us 30-45 minutes to really get through the first two pages of the packet, which I didn’t expect, but I saw light bulbs going on all over the room as we slowly grasped the big picture. When students really understood the power of the test – when they realized that even if our friend is wrong there is a 75% chance we won’t be able to “prove” it with these techniques and understood why… well, they were obviously annoyed, but they also clearly understood the limitations and execution of inference tests better than they have all year.
It was a good moment.
Next class we will actually take a sample of starbursts and conduct the tests. I doubt we will be able to decide with high confidence that they proportions different (even though they really ARE) and now, hopefully, students will understand better why.