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- Differential Calculus with Applications to Life Sciences
- University of British Columbia
- Verified Notes
Browse the full collection of course materials, past exams, study guides and class notes for MATH 102 - Differential Calculus with Applications to Life Sciences at University of …
PROFESSORS
All Professors
All semesters
Elyse Yeager
fall
36TBA
fall
40Colin MacDonald
fall
4Andreas Buttenschoen
fall
3Verified Documents for Elyse Yeager
Class Notes
Taken by our most diligent verified note takers in class covering the entire semester.
MATH 102 Lecture 1: Math 102 Notes: Lecture 1 & 2
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MATH 102 Lecture 2: Math 102 Notes: Lecture 2-Calculus
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MATH 102 Lecture 3: Maths 102 notes Lecture 3: Approximations on rational functions
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MATH 102 Lecture 4: Maths 102 notes lecture 4: Rate of change & Derivative
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MATH 102 Lecture 5: Maths 102 note: Derivatives and continuity
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MATH 102 Lecture 6: Maths 102 Notes Lecture 6: Continuous functions and the Definition of Derivative
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MATH 102 Lecture 7: Power rule & Tangent line
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MATH 102 Lecture 8: Antiderivatives, Product rule & Quotient Rule, Chain Rule
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MATH 102 Lecture Notes - Lecture 9: Chain Rule, Antiderivative, University Of Manchester
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MATH 102 Lecture 10: Sketching Antiderivatives recap, Approximation using tangent lines
Use of tangent lines: to approximate a function using a xed point > assume that the tangent line on the. Step 1: choose a nice value that you know,
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MATH 102 Lecture 11: Linear Approximation overestimation or underestimation, Newton’s Method
Lecture 11 - linear approximation overestimation or underestimation, newton"s method. , using linear approximation to determine f(8. 75), and if the re
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MATH 102 Lecture Notes - Lecture 12: Aphid, Asymptote, Ant Colony
= xk f (xk ) f "(xk : it is used for a more accurate approximation. Application 1: ladybugs and aphids" populations: size of aphid"s population = x, la
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MATH 102 Lecture Notes - Lecture 13: Mnemonic, Linear Approximation, Asymptote
Example 1: sketch ! f (x) = x3 3x 2. X 0 f (x) x3. Therefore, (2, -4) is a minimum point, (0,0), (3,0) is x-intercepts. Step 3: assemble information an
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MATH 102 Lecture Notes - Lecture 14: Cell Division, Maxima And Minima
Example 1: identify the extrema from the following graphs (yeager, ch. 6 slide 17) A: local minimum at x = 0 (because sign changes from (-) to (+) ) B:
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MATH 102 Lecture 15: Sketching graph using calculus
Example 1: sketch the graph of the following functions showing important features such as critical points, roots, discontinuities, asymptotes. f (x) =
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MATH 102 Lecture Notes - Lecture 16: Logistic Function, Pythagorean Theorem
Example 1: logistic growth rate: small populations % large populations grow slowly, equation: # K: n: density of the population, g: growth rate, r, k -
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MATH 102 Lecture 17: Optimization and critical points
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MATH 102 Lecture Notes - Lecture 18: Situation Two
Lecture 18 - optimal foraging: the energy gained from t minutes at a patch is given by: , where e and k are positive constants. Over time, they tired,
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MATH 102 Lecture 19: Least square and optimization
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MATH 102 Lecture 20: Optimization
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MATH 102 Lecture 21: Related rates
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MATH 102 Lecture 22: Related rates, Implicit Differentiation
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MATH 102 Lecture 23: Exponential function and related rates examples
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MATH 102 Lecture 24: Invertibility, exponential functions and properties
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MATH 102 Lecture 25: Application and differentiation of log, Differential equation, Quantity of radioactive isotope,
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MATH 102 Lecture 26: Logistic growth equation and examples
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MATH 102 Lecture 27: Slope field & state-space diagram
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MATH 102 Lecture 28: First order differential equation
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MATH 102 Lecture 29: Newton's cooling method and euler's method
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MATH 102 Lecture 31: Euler's method & Disease dynamics
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MATH 102 Lecture 32: Basic Trigonometry review
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MATH 102 Lecture 33: Arcsine, Arccosine, Arctangent
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MATH 102 Lecture 34: Derivatives of Trig functions
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MATH 102 Lecture 35: Trig with Related rates
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MATH 102 Lecture 36: Prey response in related rates
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MATH 102 Lecture 37: 2010 Final exam review lecture
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