You can, but they only care about the first three that you list under the IV. So no, you would not get more points.Could you do 4 levels? And if you did, would you get more points for it?
I don't know the conventions for your regional tournament, but given that you are in Illinois, it should be run. Their state organization is well established, so you should expect all events to be run.Is there going to be Experimental Design in regionals for Div. B?
While this isn't an official answer, Experimental Design should be run at the regional you're attending (assuming that your school is Wredling).Is there going to be Experimental Design in regionals for Div. B?
I think it's generally best to use a linear regression because then you can interpret the intercepts. Plus, I think it's generally good practice to linearize data when the relationship turns out not to be linear (although I don't know that the event supervisors would expect teams to do that in the time given). Best case scenario is that you design an experiment that you know will turn out to be linear. If it's not, I would still sketch the line/curve of best fit just to be safe. Although technically you don't /need/ it since it's in the statistics section, so as long as you have enough other appropriate statistics to get the 6 points, you should be fine. However, I'm not sure how relevant other statistics would be on a non-linear graph, so I think having a non-linear graph could be a bit of a risk.Have you guys ever used anything other than linear regression in an experiment? Do you think it'd be worth it to know quadratic, logarithmic, and/or sinusoidal regressions? And if a quadratic curve does fit the data better, should it still be sketched on the graph as a "line (or curve) of best fit"?
In simple terms, a regression is a statistical model of a set of bivariable data (i.e. has an independent and dependent variable, each with some numerical value) such that the average distance from the line to each point is a minimum. This link talks about linear regressions, which should be used when the data appears to form a uniform line. Likewise, quadratic regressions are for data that form a parabolic shape; logarithmic regressions for data forming the shape of a logarithmic graph; and sinusoidal regressions for data forming the shape of a sine wave. The derivation involves a ton of linear algebra; if you want to look it up, feel free.Hi, what exactly are linear/quadratic/logarithmic/sinusoidal regressions? This is my first year doing the event and I've honestly never even heard of those before.
Also, I'm in Div. B, so would knowing those be necessary for my division or is it mostly just for C?
Thanks
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