Question

I have some questions on Transformations to Achieve Linearity.....

Answer 1

@jim_thompson5910

Answer 2

The first 2 screenshots r notes that r included in the document and then the third attachment is the first three problems for this assignment, only 5 questions total...

Answer 3

ok one sec

Answer 4

yah np ty

Answer 5

ok nearly done with the plot

Answer 6

ok np

Answer 7

thankyou!

Answer 8

ok, so is there anything i can do while i wait?

Answer 9

I guess you can post what you have so far. It's taking me a bit longer than normal because I have 3 plots set up so far. One sec

Answer 10

yah ill jusrt wait cuz i have no idea lol and ty

Answer 11

Im just gonna work on it my own

Answer 12

sorry I'm taking so long, but I have it ready now

Answer 13

ok here is L1 vs L4

Answer 14

its ok, its just i have alot of work and am trying to prioritze, afterall its my fault for getting a week behind, no worries

Answer 15

here is L3 vs L2

Answer 16

ok thankyou

Answer 17

and here is L3 vs L4

Answer 18

ty

Answer 19

notice how L3 vs L4 appears to be the most linear of all the data sets

Answer 20

so if you look at your notes here

http://assets.openstudy.com/updates/attachments/54790c0ee4b082319a9f06ae-ineedhelpnowplz-1417219163135-screenshot20141128at6.00.09pm.png

you'll see that the original data set is probably close to a power function a*x^b since the rule it states here is "take log of both variables"

Answer 21

oh ok

Answer 22

thnxs

Answer 23

what do you get for each regression line

Answer 24

im still trying to calculate that right now

Answer 25

I have to get this done later because I have environmental science work to get done too so ...

Answer 26

ok when I use geogebra, I get these regression lines

L1 vs L4: y = 0.00171x + 0.28254
L3 vs L2: y = 43.13174x - 197.86581
L3 vs L4: y = 1.50093x - 6.81079

Answer 27

and here are the r & r^2 values for each


For L1 vs L4,
r = 0.89382
r^2 = 0.79891


For L3 vs L2,
r = 0.82568
r^2 = 0.681747


For L3 vs L4,
r = 1
r^2 = 1


the fact that r = 1 means we have a perfect fit (r = +1 means we have perfect positive linear correlation)

Answer 28

TY!