data that ain't linear--but we want it to be!
set1=c(1,2,3,4,5,6,7,8,9,10)
set2=c(2,4,8,16,32,64,128,256,512,1024)
1) what is a logarithm?
a) what do different bases mean?
2) how can we change our data?
3) log(base=x, data)
size of a mold colony!
hours=c(0,3,6,9,12,15,18,21,24,36)
mean.mold=c(1.23, 1.18, 0.94, 1.70, 2.13, 3.99, 9.02, 14.27, 33.89, 112.26)
we are going to:
1) input the data
2) plot the data
3) find the correlation
4) talk logarithms
5) do a log conversion
6) plot the data
7) get rid of some data (6-36 is important)
8) plot the data
9) find the correlation
10) find an approximation for 10 hours
***
USA population
date=c(1790,1800, 1810, 1820, 1830, 1840, 1850, 1860, 1870, 1880)
pop.mill=c(3.9, 5.3, 7.2, 9.6, 12.9, 17.1, 23.2, 31.4, 39.8, 50.2)
"the population of the United States grew exponentially from 1790 - 1880"
1) find a model. make a graph of that model, making sure to label appropriately.
2) approximate the population in 1850.
3) what residual does that give?
4) what does that give as a population in 2010?
5) what is the residual there? go online and find out.
***
"Expose marine bacteria to X-rays for time periods. Here are the number of bacteria surviving (in hundreds)."
time=c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15)
alive=c(355,211,197,166,142,106,104,60,56,38,36,32,21,19,15)
1) find a model. make a graph of that model, making sure to label appropriately.
2) approximate the population after 8 minutes.
3) what residual does that give?
4) what does that give as a population after 20 minutes?
the dangers of extrapolation.