Experimental Design B/C

[quizbowl]

Oh, and graphs. I have no idea what to do with graphs.

Graphs really don't have to be too complicated. Record the numerical data from your trials (preferably 2-4 of them) and plot them on a line graph or have bars for a bar graph. When doing each variable a certain amount of times, use different colors and a key to distinguish between variables. Remember to label your x and y axis, and to title your bar graph. You can also do double, triple, or even quadruple bar graphs (2, 3, or for in per clump).

For C Division, you need to do a scatter plot- not a bar graph- you need error bars on your data points, and you don't connect them with a line; you do a line of best fit.

What do we do if the relationship is nonlinear? Do we just write in analysis as the reason why the r squared values are so low is that reason?

[Phenylethylamine]

What do we do if the relationship is nonlinear? Do we just write in analysis as the reason why the r squared values are so low is that reason?

Yeah, you should still have a best fit line; ideally, you'd be able to fit some other curve, but good luck doing that on your TI-30. Like you said, you would then mention in the analysis that because the r-squared values (or whatever other measure you're using) are so low, your data may be nonlinear.

In most cases, either you'll be doing an experiment that should give linear results, or it'll be on such a small scale that the parabola/exponential/whatever actually looks linear. There are some exceptions, but mostly you can just treat your data as linear.

[The only real exception I can think of off the top of my head is if they give you M&Ms and ask you to model radioactive decay. I doubt they'd do that, since it's so simple, but if they did- at an Invitational or something- you should treat your data as exponential and not try to fit a line.]

[jayadh]

So for states does anyone have an idea of what the experiment might be (C division and B) please and thank ya!

[nejanimb]

What do we do if the relationship is nonlinear? Do we just write in analysis as the reason why the r squared values are so low is that reason?

Yeah, you should still have a best fit line; ideally, you'd be able to fit some other curve, but good luck doing that on your TI-30. Like you said, you would then mention in the analysis that because the r-squared values (or whatever other measure you're using) are so low, your data may be nonlinear.

In most cases, either you'll be doing an experiment that should give linear results, or it'll be on such a small scale that the parabola/exponential/whatever actually looks linear. There are some exceptions, but mostly you can just treat your data as linear.

[The only real exception I can think of off the top of my head is if they give you M&Ms and ask you to model radioactive decay. I doubt they'd do that, since it's so simple, but if they did- at an Invitational or something- you should treat your data as exponential and not try to fit a line.]

Doing data transformations on a calculator that's legal for this event is really not all that complicated. There are tons of instances where you'll do experiments with non-linear results: inverse relationships (pressure-volume labs with a gas, for instance), square root (the most basic of all: pendulum labs), inverse square (like the static electricity lab at nationals two years ago), etc. So many things aren't linear! Usually, you'll know it in advance and your hypothesis should predict that non-linear relationships, and then your data and analysis section should have transformations (not too difficult). A thorough lab will consider non-linear possibilities if you didn't know the relationship beforehand and just predicted linear but get results that are close but not quite - parabolas that fit, for example. Do a rough sketch of a residual plot to check for curves, and do a transformation and compare R^2 values.

[JustDroobles]

What kind of statistics do you guys use for your "other relevant statistic"?

[packer-backer91]

What kind of statistics do you guys use for your "other relevant statistic"?

I always included: Range,mode,avg,(population) standard deviation. For the other relevant statistic it was dependent on what kind of experiment it was, when it was testing the effect of a different material has on an experiment I liked to include easy things like sum. I don't know if anyone else use sum ever but I did along with others like variance too just because it was so easy to calculate. sometimes also the slope of line of best fit, these were all statistics that i remember using one time or another.

[EpicFailure]

Does anyone have any ideas about what we can do for an experiment related to friction? Our materials are just sandpaper, regular paper, several types of balls, cups, and a ruler.

[zyzzyva980]

Maybe make a ramp, using the cups as a support, and roll the balls down the regular paper and then the sandpaper? The only problem would be the lack of three measures of the IV. I did a similar experiment for my sixth grade science fair, rolling a car down a ramp with the textures of sandpaper, regular paper, wax paper, and wood.

[Swag]

So what happens when u get a bad experiment and have no way to measure statistics?

[zyzzyva980]

Do your best and try to find something to do that is quantifiable.