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CIE A Level Maths: Probability & Statistics 1

Revision Notes

Home / A Level / Maths: Probability & Statistics 1 / CIE / Revision Notes / 1. Data Presentation & Interpretation / 1.1 Statistical Measures / 1.1.4 Coding


1.1.4 Coding


Coding

Sometimes data needs to be coded for further use with calculations. This is particularly useful with data that deals with very small or very large numbers, or with data that needs to be classified for research purposes.

What is coding?

  • Coding is a way of simplifying data to make it easier to work with
  • The coding must be carried out on all values within the data set and will normally be done using a given formula
  • Coding can be carried out in a number of ways:
    • Adding or subtracting a constant to each data value
    • Multiplying or dividing each data value by a constant
    • A combination of both of the above
  • You may occasionally see coding called an assumed mean
    • This refers to the value chosen to subtract from the original data
    • It is usually chosen as an easy value, similar to an estimate of the true mean

How are statistical calculations carried out with coded data?

  • If you know the mean or standard deviation of the original data it is possible to find the mean or standard deviation of the coded data and vice versa
  • It is important to remember what the mean and standard deviation actually tell us about the data to understand how coding calculations work
    • The mean is a measure of location, changing the data set in any way will cause the mean to change in the same way
    • The standard deviation is a measure of spread, adding or subtracting a constant to every value within the data set will not change the standard deviation of the data set
      • Multiplying or dividing every value within the data set by a constant will change the standard deviation by the modulus of the constant
      • If the data were coded by multiplying or dividing by a negative, the standard deviation will change by the equivalent positive value
  • Anytime calculations are carried out on data that has been coded,
    • The original mean can be found by solving the equation to reverse the coding
    • For example, if the data, begin mathsize 16px style x end style, was coded using the formula

y equals a x plus b

Then the mean of the coded data, begin mathsize 16px style y with bar on top end style would be   

y with bar on top equals a x with bar on top plus b

The original mean, begin mathsize 16px style x with bar on top end style , will be 

 begin mathsize 16px style x with bar on top space equals fraction numerator y with bar on top space minus b over denominator a end fraction end style

    • The original standard deviation
      • Will be the same as the coded standard deviation if the data was coded by adding or subtracting a constant only
      • Can be found by reversing the coding if the data was coded by multiplying or dividing by a constant only
      • If the data was coded by a combination of both then only the multiplying or dividing will need to be reversed to find the original standard deviation
    • For example, if the data, was coded using the formula

y space equals space a x plus b

Then the standard deviation of the coded data, begin mathsize 16px style sigma subscript y end stylewould be

sigma subscript y equals open vertical bar a close vertical bar sigma subscript x

The original standard deviation, begin mathsize 16px style sigma subscript x end style, will be

sigma subscript x equals fraction numerator sigma subscript y over denominator open vertical bar a close vertical bar end fraction

What will summary statistics and formulae look like with an assumed mean?

  • If an assumed mean, a, has been subtracted from all data values, x, then the summary statistics for the coded data will be
    • The sum of the coded data straight capital sigma left parenthesis x minus a right parenthesis
    • The sum of the squares of the coded data straight capital sigma left parenthesis x minus a right parenthesis squared
      • Be careful not to mix this up with the square of the sum of the coded data left parenthesis straight capital sigma left parenthesis x minus a right parenthesis right parenthesis squared
    • If an assumed mean, a, has been subtracted from all data values, x, then the formulae for the mean and standard deviation for the coded data will be

 top enclose left parenthesis x minus a right parenthesis end enclose equals fraction numerator capital sigma left parenthesis x minus a right parenthesis over denominator n end fraction

sigma subscript x minus a end subscript equals square root of fraction numerator capital sigma left parenthesis x minus a right parenthesis squared over denominator n end fraction minus open parentheses fraction numerator capital sigma left parenthesis x minus a right parenthesis over denominator n end fraction close parentheses end root squared 

  

  • Most questions will give you either the summary statistics or the mean of the coded data and expect you to work with these formulae to find original information about the data

 

Worked Example

A coffee machine is set to dispense 150 ml of coffee per cup.

In a random sample of 20 cups of coffee straight capital sigma left parenthesis c minus 150 right parenthesis equals negative 16 ,  where c ml is the volume of coffee in a cup.

(a)
Find the mean volume in a cup of coffee.

 

(b)
Given that straight capital sigma left parenthesis c minus 150 right parenthesis squared equals 112,  find the standard deviation of the sampled cups of coffee.
(a)
Find the mean volume in a cup of coffee.

 1-1-4-coding-we-solution-1_a

(b)
Given that straight capital sigma left parenthesis c minus 150 right parenthesis squared equals 112,  find the standard deviation of the sampled cups of coffee.

1-1-4-coding-we-solution-1_b

Exam Tip

  • Be careful when using the formulae for the mean and standard deviation with coded summary statistics, you must make sure that you use the summary statistics consistently throughout. For example, if you use the sum of the coded data squared in the formula for the standard deviation, you must subtract the square of the coded mean.


  • 1. Data Presentation & Interpretation
    • 1.1 Statistical Measures
      • 1.1.1 Basic Statistical Measures
        • 1.1.2 Frequency Tables
          • 1.1.3 Standard Deviation & Variance
            • 1.1.4 Coding
            • 1.2 Representation of Data
              • 1.2.1 Data Presentation
                • 1.2.2 Stem and Leaf Diagrams
                  • 1.2.3 Box Plots & Cumulative Frequency
                    • 1.2.4 Histograms
                    • 1.3 Working with Data
                      • 1.3.1 Interpreting Data
                        • 1.3.2 Skewness
                      • 2. Probability
                        • 2.1 Basic Probability
                          • 2.1.1 Calculating Probabilities & Events
                            • 2.1.2 Venn Diagrams
                              • 2.1.3 Tree Diagrams
                              • 2.2 Permutations & Combinations
                                • 2.2.1 Arrangements & Factorials
                                  • 2.2.2 Permutations
                                    • 2.2.3 Combinations
                                    • 2.3 Further Probability
                                      • 2.3.1 Set Notation & Conditional Probability
                                        • 2.3.2 Further Tree Diagrams
                                          • 2.3.3 Further Venn Diagrams
                                            • 2.3.4 Probability Formulae
                                          • 3. Statistical Distributions
                                            • 3.1 Probability Distributions
                                              • 3.1.1 Discrete Probability Distributions
                                                • 3.1.2 E(X) & Var(X) (Discrete)
                                                • 3.2 Binomial & Geometric Distribution
                                                  • 3.2.1 The Binomial Distribution
                                                    • 3.2.2 Calculating Binomial Probabilities
                                                      • 3.2.3 The Geometric Distribution
                                                      • 3.3 Normal Distribution
                                                        • 3.3.1 The Normal Distribution
                                                          • 3.3.2 Standard Normal Distribution
                                                            • 3.3.3 Normal Distribution - Calculations
                                                              • 3.3.4 Finding Sigma and Mu
                                                              • 3.4 Working with Distributions
                                                                • 3.4.1 Modelling with Distributions
                                                                  • 3.4.2 Normal Approximation of Binomial


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                                                                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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