I'm studying the following experiment: https://9arevision.wikispaces.com/file/view/kl%3Bkl'.gif/33170891/kl%3Bkl'.gif The results given were the following: Distance of lamp from pondweed/cm VS. Number of gas bubbles per minute. 4cm - 80 6cm - 35 8cm - 20 10cm - 13 12cm - 10
How do I predict the number of bubbles per minute the student might get with a lamp distance of 16cm? I also need to explain how I obtained my answer.
hmm
tricky 1
@tester97 @Elsa213 @Abhisar @anonymous_user help please >.<
@nincompoop @ShadowLegendX @Abhisar
@Cpt.Sparz
Ohhhhhh, you're doing an experiment? :/ I haven't gotten to experiments yet. I just started taking Biology.
Yeah >.< aw, okay, np :)
@Whitemonsterbunny17
This is all it gives you?
Yes, that's all.
I know that as the light intensity decreased the rate of photosynthesis, or the number of bubbles, will slowed down. And that, all of the results concluded that when the lamp was further away from the beaker, less bubbles were produced from the pondweed. So with the lamp now being sixteen cm's away, there will be less than 10 bubbles produced. But, how many? :/
You will have to look at the number of bubbles produced in each distance, and compare them. For instance, at 4 cm, it was 80, then at 6 it was 35. So find how many less bubbles occurred after that added 2 cm distance. Do that with each, and see if you find a similarity.
I've tried that, but I can't see a similarity. 45, 15, 7, 3. :/
@Kayne @ikram002p >.<
@dan815
@tester97 @Abhisar @tkhunny @OBEYrolo @paki
@secretx_x
@Somy @jhonyy9 @Jesstho.-.
@hba @hartnn @shrutipande9
have you practically done this experiment?
coz you can't really predict unless you did couple of times
only thing you should know is this : As light intensity increases (distance between lamp and plant decreases) the volume of oxygen (or the rate of bubble production) increases. This indicates that the rate of photosynthesis increases with light intensity. However, at sufficiently high levels of light intensity, the rate oxygen evolution remains constant.
@Somy No, I have not done this experiment myself personally, I have to answer from the information given
hmmm well the shorter the distance the more bubbles the longer the distance the less bubbles
so u can just use this idea
Lol, thank you, I knew that from before, and I know the number of bubbles will be less than 10,but I'm not sure about the exact number. I don't know how to work that exact number out >.<
how do you know it? btw does this question have any more details?
Why there will be less than 10 bubbles? Because the distance is 16 cm, and the last distance measured was twelve- he number of bubbles from that was 10. For the 16cm distance it would be less >.< - did that make sense? No, no more details >.<
.-. why is this not mentioned in the question? and i was thinking how are we supposed to find anything if there is no distance
oh lol i didnt see your post below the post lmao my bad
Lol, the question is in the second post. s'okay x
@PRAETORIAN.10 @Miracrown
hello motto
Hello? >.< can you help?
trying to
lots to read
Can anyone help? >.<
@Compassionate @AngelWilliams16 @sammychahal
>.<
I think one way to go about answering this question is to do some simple regression analysis. I entered the data into excel and added some trendlines. It appears that the exponential (R^2 = .958) and power (R^2 = .998) regressions fit the data the best. According to the exponential model, the equation that best fits the data is: \[f(x) = 185e^{-0.26x}\]According to the power model, the equation that best fits the data is: \[f(x) = 1105x^{-1.92}\]In the two equations, x represents the distance between the lamp and the pondweed and f(x) represents the rate of evolution of gas bubbles. Hence, f(16) in both equations would yield an estimate for the # of gas bubbles per minute if the distance is 16 cm. From the exponential model, f(16) = 3.03. From the power model, f(16) = 5.46. If you had to give an answer, anywhere from 3 to 5 gas bubbles would be sufficient from these models. I'd lean closer to 5 gas bubbles per minute because the power regression model fits the data slightly better.
I see. I understand :O
WOW, thank you so so so so so so much for the help! :O
You're welcome! :)
Join our real-time social learning platform and learn together with your friends!