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DP IB Maths: AI SL

Revision Notes

Home / IB / Maths: AI SL / DP / Revision Notes / 1. Number & Algebra / 1.1 Number Toolkit / 1.1.2 Exponents & Logarithms


1.1.2 Exponents & Logarithms


Laws of Indices

What are the laws of indices?

  • Laws of indices (or index laws) allow you to simplify and manipulate expressions involving exponents
    • An exponent is a power that a number (called the base) is raised to
    • Laws of indices can be used when the numbers are written with the same base
  • The index laws you need to know are:
    • left parenthesis x y right parenthesis to the power of m equals x to the power of m end exponent y to the power of m
    • stretchy left parenthesis x over y stretchy right parenthesis to the power of m equals x to the power of m over y to the power of m
    • x to the power of m cross times x to the power of n equals x to the power of m plus n end exponent
    • x to the power of m divided by x to the power of n equals x to the power of m minus n end exponent
    • stretchy left parenthesis x to the power of m stretchy right parenthesis to the power of n equals x to the power of m n end exponent
    • x to the power of 1 equals x
    • x to the power of 0 equals 1
    • 1 over x to the power of m equals x to the power of negative m end exponent
  • These laws are not in the formula booklet so you must remember them

How are laws of indices used?

  • You will need to be able to carry out multiple calculations with the laws of indices
    • Take your time and apply each law individually
    • Work with numbers first and then with algebra
  • Index laws only work with terms that have the same base, make sure you change the base of the term before using any of the index laws
    • Changing the base means rewriting the number as an exponent with the base you need
    • For example, 9 to the power of 4 equals left parenthesis 3 squared right parenthesis to the power of 4 equals 3 to the power of 2 cross times 4 end exponent equals 3 to the power of 8
    • Using the above can them help with problems like 9 to the power of 4 divided by 3 to the power of 7 equals 3 to the power of 8 divided by 3 to the power of 7 equals 3 to the power of 1 equals 3

Exam Tip

  • Index laws are rarely a question on their own in the exam but are often needed to help you solve other problems, especially when working with logarithms or polynomials
  • Look out for times when the laws of indices can be applied to help you solve a problem algebraically 

Worked Example

Simplify the following equations:

i)
fraction numerator left parenthesis 3 x squared right parenthesis left parenthesis 2 x cubed y squared right parenthesis over denominator left parenthesis 6 x squared y right parenthesis end fraction.
 

ai-sl-1-1-2-laws-of-indices-we-i

ii)
left parenthesis 4 x squared y to the power of negative 4 end exponent right parenthesis cubed left parenthesis 2 x cubed y to the power of negative 1 end exponent right parenthesis to the power of negative 2 end exponent.
 
ai-sl-1-1-2-laws-of-indices-we-ii



Introduction to Logarithms

What are logarithms?

  • A logarithm is the inverse of an exponent
    • If a to the power of x equals b then log subscript a open parentheses b close parentheses equals x where a > 0, b > 0, a ≠ 1
      • This is in the formula booklet
      • Your GDC will be able to use this function to solve equations involving exponents
  • Try to get used to ‘reading’ logarithm statements to yourself
    • log subscript a left parenthesis b right parenthesis space equals space x would be read as “the power that you raise a to, to get b, is x”
    • So log subscript 5 125 space equals space 3 would be read as “the power that you raise 5 to, to get 125, is 3”
  • Two important cases are:
    • ln space x equals log subscript straight e open parentheses x close parentheses
      • Where e is the mathematical constant 2.718…
      • This is called the natural logarithm and will have its own button on your GDC
    • log space x equals log subscript 10 open parentheses x close parentheses
      • Logarithms of base 10 are used often and so abbreviated to log x

Why use logarithms?

  • Logarithms allow us to solve equations where the exponent is the unknown value
    • We can solve some of these by inspection
      • For example, for the equation 2x = 8 we know that x must be 3
    • Logarithms allow use to solve more complicated problems
      • For example, the equation 2x = 10 does not have a clear answer
      • Instead, we can use our GDCs to find the value of log subscript 2 10

Exam Tip

  • Before going into the exam, make sure you are completely familiar with your GDC and know how to use its logarithm functions

Worked Example

Solve the following equations:

i)
x equals log subscript 3 27,
 

ai-sl-1-1-2intro-to-logs-we-i

ii)
2 to the power of x equals 21.4, giving your answer to 3 s.f.
 
ai-sl-1-1-2intro-to-logs-we-ii



  • 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
                                                                                                                      Amber Perry

                                                                                                                      Author: Amber

                                                                                                                      Amber gained a first class degree in Mathematics & Meteorology from the University of Reading before training to become a teacher. She is passionate about teaching, having spent 8 years teaching GCSE and A Level Mathematics both in the UK and internationally. Amber loves creating bright and informative resources to help students reach their potential.


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