Today's estimation was estimating my height. Some students had heard me say it before, and I loved students that saw me walk by my door where I have 2 meter sticks taped on top of each other for 200 centimeters. They said I walked by and they saw me at about 180. They were pretty accurate. The lowest estimate was 5'10" I believe. Still saw some students use decimals, after making the same mistake day 1. The highest I got was... 6"7"? No, I am not as tall as Dan Meyer, thanks. Some students compared themselves to my height, Ryan in the picture, or the tree next to me
I had students spend 5 minutes completing cards C1 to C6 of the FAL: Classifying Solutions. In some classes we went over 1 card, and it revealed misconceptions with multiplying by -1/2, adding integers, and switching the variables x and y at times. The lesson had started though with whiteboards to go over substituting a variable for x to find y, and writing it as an ordered pair solution (x,y). I asked what does x=5 and y=17 mean in relation to the graph of the equation y=3x+2? Some students said it was point, and it was difficult to articulate that it was a point on the graph of the line of the equation.
|Here we an see the upper and lower boundary lines of the line of best of y=5x+25. The residual was 25, so students added and subtracted 25 from the y-intercepts. They reasoned data predictions would generally fall in between these two lines.|
In accelerated we started where we left off with Jason showing us his scatterplot. He had skipped a multiple of 25 when making his graph and it didn't affect the data and I wanted to make a point of not getting another student's work because I asked how we could fix it. He drew his slope triangle to interpret his slope based on the scale of his axes and found his y-intercept of 25.
We heard different predictions of the humanoids height based on different equations. Most students had a slope of around 5.
Then students started on the Desmos activity I created, LSRL's based on the start of the Golden State Warriors season.
|Some of students notices and wonders.|
Students had different predictions of what the data represented and a lot of students predicted correctly.
The other objective was for students to make a manual line of best fit, and look what it looked like with overlay. Today and tomorrow we will discuss how these inconsistencies lead to a need for the best line of best fit, or the Least Squares Regression Line.
Look at the overlay:
|After seeing the x/y table of Warriors rosters. Not labeled.|
|Notice all the different lines of best fit.|
I forgot to have students tweet what they learned from the lesson.