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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 / 2.3.4 Direct & Inverse Variation


2.3.4 Direct & Inverse Variation


Direct Variation

What is direct variation?

  • Two variables are said to vary directly if their ratio is constant (k)
    • This is also called direct proportion
  • If yand x to the power of n (for positive integer n) vary directly then:
    • It is denoted as y proportional to x to the power of n
    • y equals k x to the power of n for some constant k
      • This can be written as y over x to the power of n equals k
  • The graphs of these models always start at the origin

How do I solve direct variation problems?

  • Identify which two variables vary directly
    • It might not be x and y
    • It could be x cubed and y
  • Use the given information to find their constant ratio k
    • Also called constant of proportionality
    • Substitute the given values of x and y into your formula
    • Solve to find k
  • Write the equation which models their relationship
    • y equals k x to the power of n
  • You can then use the equation to solve problems

Worked Example

A computer program sorts a list of numbers into ascending order. The time it takes, t milliseconds, varies directly with the square of the number of items, n, in the list. The computer program takes 48 milliseconds to order a list with 8 items.

a)
Find an equation connecting t and n.

2-3-4-ib-ai-sl-direct-variation-a-we-solution

b)
Find the time it takes to order a list of 50 numbers.

2-3-4-ib-ai-sl-direct-variation-b-we-solution

Inverse Variation

What is inverse variation?

  • Two variables are said to vary inversely if their product is constant (k)
    • This is also called inverse proportion
  • If y and x to the power of n (for positive integer n) vary inversely then:
    • It is denoted y proportional to 1 over x to the power of n
    • y equals k over x to the power of n for some constant k
      • This can be written x to the power of n y equals k
  • The graphs of these models all have a vertical asymptote at the y-axis
    • This means that as x gets closer to 0 the absolute value of y gets further away from 0
    • x can never equal 0

How do I solve inverse variation problems?

  • Identify which two variables vary inversely
    • It might not be x and y
    • It could be x cubed and y
  • Use the given information to find their constant product k
    • Also called constant of proportionality
    • Substitute the given values of x and y into your formula
    • Solve to find k
  • Write the equation which models their relationship
    • y equals k over x to the power of n
  • You can then use the equation to solve problems

Exam Tip

  • Reciprocal graphs generally have two parts/curves
    • Only one – usually the positive – may be relevant to the model
    • Think about why x/t/θ can only take positive values - refer to the context of the question

Worked Example

The time, t hours, it takes to complete a project varies inversely to the number of people working on it, n. If 4 people work on the project it takes 70 hours to complete.

a)
Write an equation connecting t and n.

2-3-4-ib-ai-sl-inverse-variation-a-we-solution

b)
Given that the project needs to be completed within 18 hours, find the minimum number of people needed to work on it.

2-3-4-ib-ai-sl-inverse-variation-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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