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DP IB Maths: AI SL

Revision Notes

Home / IB / Maths: AI SL / DP / Revision Notes / 2. Functions / 2.3 Modelling with Functions / 2.3.3 Exponential Models


2.3.3 Exponential Models


Exponential Models

What are the parameters of an exponential model?

  • An exponential model is of the form
    •  space f left parenthesis x right parenthesis equals k a to the power of x plus c or space f left parenthesis x right parenthesis equals k a to the power of negative x end exponent plus c for space a greater than 0
    • space f open parentheses x close parentheses equals k straight e to the power of r x end exponent plus c
      • Where e is the mathematical constant 2.718…
    • The c represents the boundary for the function
      • It can never be this value
    • The a or r describes the rate of growth or decay
      • The bigger the value of a or the absolute value of r the faster the function increases/decreases

What can be modelled as an exponential model?

  • Exponential growth or decay
    • Exponential growth is represented by
      • a to the power of x where a greater than 1
      • a to the power of negative x end exponent where 0 less than a less than 1
      • straight e to the power of r x end exponent where r greater than 0
    • Exponential decay is represented by
      • a to the power of x where 0 less than a less than 1
      • a to the power of negative x end exponent where a greater than 1 
      • straight e to the power of r x end exponent where r less than 0
  • They can be used when there a constant percentage increase or decrease
    • Such as functions generated by geometric sequences
  • Examples include:
    • V(t) is the value of car after t years
    • S(t) is the amount in a savings account after t years
    • B(t) is the amount of bacteria on a surface after t seconds
    • T(t) is the temperature of a kettle t minutes after being boiled

What are possible limitations of an exponential model?

  • An exponential growth model does not have a maximum
    • In real-life this might not be the case
    • The function might reach a maximum and stay at this value
  • Exponential models are monotonic
    • In real-life this might not be the case
    • The function might fluctuate

Exam Tip

  • Look out for the word "initial" or similar, as way of asking you to make the power equal to zero to simplify the equation
  • Questions regarding the boundary of the exponential model are also frequently asked

Worked Example

The value of a car, V (NZD), can be modelled by the function

space V open parentheses t close parentheses equals 25125 cross times 0.8 to the power of t plus 8500 comma blank t greater or equal than 0

where t is the age of the car in years.

a)
State the initial value of the car.

2-3-3-ib-ai-sl-exponential-models-a-we-solution

b)
Find the age of the car when its value is 17500 NZD.

2-3-3-ib-ai-sl-exponential-models-b-we-solution



  • 1. Number & Algebra
    • 1.1 Number Toolkit
      • 1.1.1 Standard Form
        • 1.1.2 Exponents & Logarithms
          • 1.1.3 Approximation & Estimation
            • 1.1.4 GDC: Solving Equations
            • 1.2 Sequences & Series
              • 1.2.1 Language of Sequences & Series
                • 1.2.2 Arithmetic Sequences & Series
                  • 1.2.3 Geometric Sequences & Series
                    • 1.2.4 Applications of Sequences & Series
                    • 1.3 Financial Applications
                      • 1.3.1 Compound Interest & Depreciation
                        • 1.3.2 Amortisation & Annuities
                      • 2. Functions
                        • 2.1 Linear Functions & Graphs
                          • 2.1.1 Equations of a Straight Line
                          • 2.2 Further Functions & Graphs
                            • 2.2.1 Functions
                              • 2.2.2 Graphing Functions
                                • 2.2.3 Properties of Graphs
                                • 2.3 Modelling with Functions
                                  • 2.3.1 Linear & Piecewise Models
                                    • 2.3.2 Quadratic & Cubic Models
                                      • 2.3.3 Exponential Models
                                        • 2.3.4 Direct & Inverse Variation
                                          • 2.3.5 Sinusoidal Models
                                            • 2.3.6 Strategy for Modelling Functions
                                          • 3. Geometry & Trigonometry
                                            • 3.1 Geometry Toolkit
                                              • 3.1.1 Coordinate Geometry
                                                • 3.1.2 Arcs & Sectors
                                                • 3.2 Geometry of 3D Shapes
                                                  • 3.2.1 3D Coordinate Geometry
                                                    • 3.2.2 Volume & Surface Area
                                                    • 3.3 Trigonometry
                                                      • 3.3.1 Pythagoras & Right-Angled Triganometry
                                                        • 3.3.2 Non Right-Angled Trigonometry
                                                          • 3.3.3 Applications of Trigonometry & Pythagoras
                                                          • 3.4 Voronoi Diagrams
                                                            • 3.4.1 Voronoi Diagrams
                                                              • 3.4.2 Toxic Waste Dump Problem
                                                            • 4. Statistics & Probability
                                                              • 4.1 Statistics Toolkit
                                                                • 4.1.1 Sampling & Data Collection
                                                                  • 4.1.2 Statistical Measures
                                                                    • 4.1.3 Frequency Tables
                                                                      • 4.1.4 Linear Transformations of Data
                                                                        • 4.1.5 Outliers
                                                                          • 4.1.6 Univariate Data
                                                                            • 4.1.7 Interpreting Data
                                                                            • 4.2 Correlation & Regression
                                                                              • 4.2.1 Bivariate data
                                                                                • 4.2.2 Correlation Coefficients
                                                                                  • 4.2.3 Linear Regression
                                                                                  • 4.3 Probability
                                                                                    • 4.3.1 Probability & Types of Events
                                                                                      • 4.3.2 Conditional Probability
                                                                                        • 4.3.3 Sample Space Diagrams
                                                                                        • 4.4 Probability Distributions
                                                                                          • 4.4.1 Discrete Probability Distributions
                                                                                            • 4.4.2 Expected Values
                                                                                            • 4.5 Binomial Distribution
                                                                                              • 4.5.1 The Binomial Distribution
                                                                                                • 4.5.2 Calculating Binomial Probabilities
                                                                                                • 4.6 Normal Distribution
                                                                                                  • 4.6.1 The Normal Distribution
                                                                                                    • 4.6.2 Calculations with Normal Distribution
                                                                                                    • 4.7 Hypothesis Testing
                                                                                                      • 4.7.1 Hypothesis Testing
                                                                                                        • 4.7.2 Chi-squared Test for Independence
                                                                                                          • 4.7.3 Goodness of Fit Test
                                                                                                            • 4.7.4 The t-test
                                                                                                          • 5. Calculus
                                                                                                            • 5.1 Differentiation
                                                                                                              • 5.1.1 Introduction to Differentiation
                                                                                                                • 5.1.2 Applications of Differentiation
                                                                                                                  • 5.1.3 Modelling with Differentiation
                                                                                                                  • 5.2 Integration
                                                                                                                    • 5.2.1 Trapezoid Rule: Numerical Integration
                                                                                                                      • 5.2.2 Introduction to Integration
                                                                                                                        • 5.2.3 Applications of Integration
                                                                                                                      Daniel Finlay

                                                                                                                      Author: Daniel

                                                                                                                      Dan graduated from the University of Oxford with a First class degree in mathematics. As well as teaching maths for over 8 years, Dan has marked a range of exams for Edexcel, tutored students and taught A Level Accounting. Dan has a keen interest in statistics and probability and their real-life applications.


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