exponentials, logarithms and rescaling of data math 151 based upon previous notes by scott duke-...

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Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke-Sylvester as an adjunct to the Lecture notes posted on the course web page

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Page 1: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

Exponentials, logarithms and rescaling of data

Math 151

Based upon previous notes by Scott Duke-Sylvester as an adjunct to the Lecture notes posted

on the course web page

Page 2: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

Definitions

• f(x) = ax, exponential function with base a

• f(x) = logax, logarithm of x base a

• f(x) = axb, is an allometric function

Page 3: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

Motivation• Many biological phenomena are non-linear:

Population growthRelationship between different parts/aspects of an

organism (allometric relationships)The number of species found in a given area (species-area

relationships)Radioactive decay and carbon datingMany others

• Exponentials, logs and allometric functions are useful in understanding these phenomena

Page 4: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

• Population growth is a classic exampleAlgae : cell division

Geometric growth

…1

2 48 16

32 64

t = 0 1 2 3 4 5 6

Page 5: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

Population Size vs. time

0

10

20

30

40

50

60

70

0 1 2 3 4 5 6 7

Time (t)

Popula

tion S

ize (

N)

Page 6: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

Exponentialsf(x) = ax, a > 0

a > 1exponential increase

0 < a < 1exponential decrease

As x becomes very negative, f(x) gets close to zero

As x becomes very positive, f(x) gets close to zero

Page 7: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

• Special case, a = 1

Page 8: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

• f(x) = ax is one-to-one. For every x value there is a unique value of f(x).

• This implies that f(x) = ax has an inverse.

• f-1(x) = logax, logarithm base a of x.

Page 9: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

-4

-2

0

2

4

6

8

-6 -4 -2 0 2 4 6 8

f(x) = ax

f(x) = logax

Page 10: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

• logax is the power to which a must be raised to get x.

• y = logax is equivalent to ay = x• f(f-1(x)) = alogax = x, for x > 0• f-1(f(x)) = logaax = x, for all x.• There are two common forms of the log fn.

a = 10, log10x, commonly written a simply log x

a = e = 2.71828…, logex = ln x, natural log.

• logax does not exist for x ≤ 0.

Page 11: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

Laws of logarithms• loga(xy) = logax + logay

• loga(x/y) = logax - logay

• logaxk = k·logax

• logaa = 1

• loga1 = 0

• Example 15.7 :

23x 1.7

log10 23x log10 1.7

3x log10 2 log10 1.7

xlog10 1.7

3log10 2

x0.2304

3* 0.3010.2551

Page 12: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

• Example 15.8 : Radioactive decayA radioactive material decays according to the law N(t)=5e-0.4t

0

1

2

3

4

5

6

0 2 4 6 8 10

Time t (months)

Num

ber

of

gra

ms

N

months023.4)4.0/(6909.1t

)4.0)/(2.0(lnt

t4.0)2.0(ln

)(eln)2.0(ln

e5/1

e51

t4.0

t4.0

t4.0

When does N = 1?For what value of t does N = 1?

Page 13: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

loga xlog10 x

log10 a

loga xln x

lna

To compute logax if your calculator doesn’t have loga

log2 64 ln64

ln2

4.1588...

0.6931...6

Example:

or use

Page 14: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

• general formula for a simple exponential function:f(x) = x or y = x

Then ln(y) = ln x

ln(y) = ln + ln (x )ln(y) = ln + x ln

Let b = ln , and m = ln , Y=ln (y) then this showsY = b + mx

which is the equation of a straight line.This is an example of transforming (some) non-linear data so that the

transformed data has a linear relationship. An exponential function gives a straight line when you plot the log of y against x (semi-log plot).

Special exponential form : f(x) = emx

Page 15: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

Consider the algae growth example again. How do you know when a relationship is exponential?

0

10

20

30

40

Num

ber

of

cells

N

-1 0 1 2 3 4 5 6Time t

-1

0

1

2

3

4

ln(N

)

-1 0 1 2 3 4 5 6Time t

Regular plot Semilog plot

Time t Number of Cells ln(N)0 1 01 2 0.693147182 4 1.386294363 8 2.079441544 16 2.772588725 32 3.4657359

N= t ln(N) = mt+b

Page 16: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

-1

0

1

2

3

4

ln(N

)

-1 0 1 2 3 4 5 6Time t

• Fit a line to the transformed data

• Estimate the slope and intercept using the least squares method.

• Y=mx+b

• b~0, m = 0.693..

• Estimate and .b = ln -> = eb = e0 = 1

m = ln -> = em = e0.693.. = 2.0

N = 2t

0

10

20

30

40

Num

ber

of

cells

N

-1 0 1 2 3 4 5 6Time t

ln(N) = (0.693)t

N=2t

Page 17: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

Example 17.9 : Wound healing rate

Time t (days) Area A (cm^2) ln(A)0 107 4.672828834 88 4.477336818 75 4.31748811

12 62 4.1271343916 51 3.9318256320 42 3.7376696224 34 3.5263605228 27 3.29583687

25

50

75

100

125

are

a A

(cm

^2)

-5 0 5 10 15 20 25 30Time t (days)

Regular plot

3.25

3.5

3.75

4

4.25

4.5

4.75

ln(A

)

-5 0 5 10 15 20 25 30Time t (days)

Semilog plot

Page 18: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

• How do you make a semilog plot?

• Use the semilog(x,y) command in Matlab

• Take the log of one column of data and plot the transformed data (here log y) against the untransformed data (here x)

Page 19: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

3.25

3.5

3.75

4

4.25

4.5

4.75

ln(A

)

-5 0 5 10 15 20 25 30Time t (days)

• Estimate slope and intercept using least squares

• Y = b+mx• m = -0.048• b = 4.69• b = ln -> = eb = e4.69 =

108.85• m = ln -> = em = e-

0.048 = 0.953• A = 108.85(0.953)t

25

50

75

100

125

are

a A

(cm

^2)

-5 0 5 10 15 20 25 30Time t (days)

ln(A)=4.96-0.048t

A=108.85(0.953)t

Page 20: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

f(x)=bxa

• Allometric relationships (also called power laws)• Describes many relationships between different aspects of a single

organism:Length and volumeSurface area and volumeBody weight and brain weightBody weight and blood volume

• Typically x > 0, since negative quantities don’t have biological meaning.

Page 21: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

a > 1

a = 1

a < 1

a < 0

Page 22: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

f(x) = bax

f(x) = bxa

Page 23: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

• Example : It has been determined that for any elephant, surface area of the body can be expressed as an allometric function of trunk length.

• For African elephants, a=0.74, and a particular elephant has a surface area of 20 ft2 and a trunk length of 1 ft.

• What is the surface area of an elephant with a trunk length of 3.3 ft?

• x = trunk length

• y= surface area

• y = bxa = bx0.74

• 20 = b(1)0.74 20 = b

• y=20x0.74

• y=20(3.3)0.74=48.4 ft2

Page 24: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

• How do you know when your data has an allometric relationship?

• Example 17.10

length L (cm) weight W (lbs)70 14.380 21.590 30.8

100 42.5110 56.8120 74.1130 94.7140 119160 179180 256

0

50

100

150

200

250

300

weig

ht

W (

lbs)

50 75 100 125 150 175 200length L (cm)

Page 25: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

2.5

3

3.5

4

4.5

5

5.5

6

ln(W

)

50 75 100 125 150 175 200length L (cm)

2.5

3

3.5

4

4.5

5

5.5

6

ln(W

)

4 4.25 4.5 4.75 5 5.25ln(L)

0

50

100

150

200

250

300

weig

ht

W (

lbs)

50 75 100 125 150 175 200length L (cm)

log-log plotSemilog plot

Regular plot

Page 26: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

• How do you make a log-log plot?

• Use the loglog(x,y) command in matlab

• Take the log of both columns of data and plot the transformed columns.

Page 27: Exponentials, logarithms and rescaling of data Math 151 Based upon previous notes by Scott Duke- Sylvester as an adjunct to the Lecture notes posted on

• Y=bxa

• ln(y)=ln(bxa)• ln(y) = ln(b) + ln(xa)• ln(y) = ln(b) + a ln(x)• Let

Y = ln(y)X=ln(x)B=ln(b)Then Y=B + a X

Which is the equation for a straight line. So an allometric function gives a straight line on a loglog plot.