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Math 19a: Modeling and Differential Equations for the Life Sciences Calculus Review Danny Kramer Fall 2013

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Math 19a: Modeling and Differential Equations for the Life Sciences

Calculus Review

Danny KramerFall 2013

Derivatives

Point Slope Concept

𝑓 β€² (π‘₯ )= limβˆ†π‘₯β†’ 0

𝑓 (π‘₯+βˆ† π‘₯ )βˆ’ 𝑓 (π‘₯)βˆ† π‘₯

𝑑𝑑π‘₯

𝑓 (π‘₯ )=π‘ π‘™π‘œπ‘π‘’π‘Žπ‘‘ π‘Žπ‘›π‘¦ π‘π‘œπ‘–π‘›π‘‘ π‘₯

Think of , but change in y measured over infinitely small change in x

x

y

Solve it Out

Derivate of x2?

0

Derivative Rules

𝑑𝑑π‘₯

π‘˜=0

𝑑𝑑π‘₯

π‘₯𝑛=𝑛π‘₯π‘›βˆ’1

𝑑𝑑π‘₯

𝑐 π‘œπ‘ π‘₯=βˆ’π‘ π‘–π‘›π‘₯

𝑑𝑑π‘₯

𝑠𝑖𝑛π‘₯=π‘π‘œπ‘ π‘₯

𝑑𝑑π‘₯ln ∨π‘₯∨¿

1π‘₯

𝑑𝑑π‘₯

𝑒π‘₯=𝑒π‘₯

All with respect to dx, ie if you’re using 2x, then put 2x in for x and 2 in front of all derivatives.

Derivatives and Operations

𝑑𝑑π‘₯

( 𝑓 +𝑔)= 𝑓 β€²+𝑔 β€²

𝑑𝑑π‘₯

( 𝑓 βˆ’π‘”)= 𝑓 β€²βˆ’π‘” β€²

𝑑𝑑π‘₯

( 𝑓 βˆ—π‘”)= 𝑓 ′𝑔+ 𝑓𝑔 β€²

𝑑𝑑π‘₯

ΒΏ

π‘π‘œπ‘‘π‘’ :𝑑𝑑π‘₯

𝑓= 𝑓 β€²

Applications

β€’ Positionβ€’ Speed/Velocityβ€’ Acceleration

𝑣=βˆ†π‘₯βˆ† 𝑑

a=βˆ† π‘£βˆ† 𝑑

𝑣 (𝑑 )= 𝑑𝑑𝑑

π‘₯=π‘₯ β€² (t)

a (t )= 𝑑𝑑𝑑

𝑣= 𝑑𝑑𝑑 ( 𝑑𝑑𝑑 π‘₯)= 𝑑2

𝑑𝑑 2π‘₯=π‘₯ β€² β€² (𝑑)

Maxima and Minima

x

f(x)

f’(x)=0

f’(x)=0

Some Vocabulary

β€’ Continuous- no holes or jumps in the graph

β€’ Differentiable- continuous graph with a derivative at each point…no β€œcusps”

βœ“ XX

βœ“ X X

Sample Problem

Maximum Point?

Integrals and Antiderivatives

Area Concept

π·π‘’π‘Ÿπ‘–π‘£π‘Žπ‘‘π‘–π‘£π‘’β†’βˆ†π‘žπ‘’π‘Žπ‘›π‘‘π‘–π‘‘π‘¦βˆ† π‘‘π‘–π‘šπ‘’

=π‘Ÿπ‘Žπ‘‘π‘’

It is area under a curve, but think of it more generally as multiplying a changing rate by the elapsed time over which the rate occurs, giving you the change in quantity that the rate is measuring.

x

y

=

Some Notation

∫ 𝑓 (π‘₯ )=𝐹 (π‘₯)𝐹 β€² (π‘₯ )= 𝑓 (π‘₯)

Antiderivative Rules and Operations

What’s with the C? Disappears in derivative!

βˆ«π‘’π‘₯=𝑒π‘₯+πΆβˆ«π‘π‘œπ‘ π‘₯=𝑠𝑖𝑛π‘₯+𝐢∫ 𝑠𝑖𝑛π‘₯=βˆ’π‘π‘œπ‘ π‘₯+𝐢

∫( 𝑓 +𝑔)=∫ 𝑓 +βˆ«π‘” ∫( 𝑓 βˆ’π‘”)=∫ 𝑓 βˆ’βˆ«π‘”

U substitution

Replace to visualize

βˆ«π‘’sin ( π‘₯)cos (π‘₯)𝑑π‘₯β†’βˆ«π‘’u𝑑𝑒=𝑒𝑒+𝐢→𝑒=sin (π‘₯)𝑑𝑒=cos (π‘₯)𝑑π‘₯

𝑒𝑠𝑖𝑛π‘₯+𝐢

Integration by Parts

βˆ«π‘’π‘‘π‘£=π‘’π‘£βˆ’βˆ«π‘£π‘‘π‘’ Opposite of product rule. Test it out!

What Becomes u?LogInverse Trig (the arcs)AlgebraTrigExponential

∫π‘₯ π‘’βˆ’π‘₯𝑑π‘₯𝑒=π‘₯𝑑𝑒=𝑑π‘₯𝑣=βˆ’π‘’βˆ’π‘₯𝑑𝑣=π‘’βˆ’π‘₯𝑑π‘₯

Taylor Series

Approximating Polynomial Curves

x

f(x)

x = a

f(a)

Taylor’s Formula

𝑓 (π‘₯ )= 𝑓 (π‘Ž)+ 𝑓 β€² (π‘Ž ) (π‘₯βˆ’π‘Ž )+ 𝑓 β€² β€² (π‘Ž)2 !

(π‘₯βˆ’π‘Ž)2+…

𝑇=βˆ‘π‘›=0

∞ 𝑓 𝑛(π‘Ž)𝑛 !

(π‘₯βˆ’π‘Ž )𝑛

Practicing Taylor

𝑓 (π‘₯ )=π‘₯ π‘’βˆ’π‘₯

𝑓 β€² (π‘₯ )=π‘’βˆ’π‘₯βˆ’π‘₯ π‘’βˆ’π‘₯=π‘’βˆ’π‘₯(1βˆ’x )

𝑓 β€² β€² (π‘₯ )=βˆ’π‘’βˆ’π‘₯βˆ’π‘’βˆ’π‘₯ (1βˆ’ x )=π‘’βˆ’π‘₯(xβˆ’2)

Practicing Taylor

𝑓 (1 )=(1 )π‘’βˆ’1=πŸπ’†

=

𝑓 β€² β€² (1 )=βˆ’π‘’βˆ’1βˆ’π‘’βˆ’1 (1βˆ’1 )=π‘’βˆ’1 (1βˆ’2 )=βˆ’πŸπ’†

𝑇= 𝑓 (π‘Ž )+ 𝑓 β€² (π‘Ž) (π‘₯βˆ’π‘Ž )+ 𝑓 β€² β€²(π‘Ž)2 !

(π‘₯βˆ’π‘Ž)2 ,π‘Ž=1

𝑇 π‘₯=πŸπ’†

+πŸŽβˆ’πŸ2𝒆

(π‘₯βˆ’1)2

Parametric Curves

Dimensions of Measurement

β€’ x(t) , y(t) x(y) / y(x) ?β€’ Match up x and y at any given time t.

t

x , yx y

5

10

x

y

5

10

5 10t0 tf

t0

tf

Parametric Conversion

π‘₯=2 𝑑+1 𝑦=3 π‘‘βˆ’1

𝑑=π‘₯βˆ’12

𝑑=𝑦+13

π‘₯βˆ’12

=𝑦+13

𝑦+1=32(π‘₯βˆ’1)

𝑦=32π‘₯βˆ’

52β†’π‘₯=

23𝑦+53

𝑑π‘₯𝑑𝑑

=2 𝑑𝑑𝑦𝑑𝑑

=3 𝑑

𝑑𝑦𝑑𝑑𝑑π‘₯𝑑𝑑

=3 𝑑2𝑑

=3 /2

𝑑𝑦𝑑π‘₯

=3/2