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OCR A Level Maths: Statistics

Revision Notes

Home / A Level / Maths: Statistics / OCR / Revision Notes / 2. Data Presentation & Interpretation / 2.2 Data Presentation / 2.2.3 Histograms


2.2.3 Histograms


Histograms

What is a histogram?

  • A histogram is similar to a bar chart but with some key differences
    • A histogram is for displaying grouped continuous data whereas a bar chart is for discrete or qualitative data
    • There will never be any gaps between the bars of adjacent groups in a histogram
    • Whilst in a bar chart the frequency is read from the height of the bar, in a histogram the height of the bar is the frequency density
  • On a histogram frequency density is plotted on the y – axis
    • This allows a histogram to be plotted for unequal class intervals
    • It is particularly useful if data is spread out at either or both ends
  • The area of each bar on a histogram will be proportional to the frequency in that class

What are the key features of a histogram?

  • You will not be asked to draw a histogram but you may have to add information to one so you should make sure you are familiar with the process for drawing one
  • Step 1.  Always check that there are no gaps between the upper boundary of a class and the    lower boundary of the next class
    • If there are gaps you will need to close them by changing the boundaries before carrying out any calculations
      • Consider whether the values are rounded or truncated before closing the gaps
  • Step 2.  Find the class width of each group by subtracting the lower boundary from the upper boundary
  • Step 3.  Calculate the frequency density for each group using the formula:

frequency space density equals k cross times fraction numerator frequency over denominator class space width end fraction

  • Step 4.  The histogram will be drawn with the data values on the x – axis and frequency density on the y – axis
    • Remember that the scale on both axes must be even, although the class widths may be uneven
      • Both axes should be clearly labelled and units included on the x – axis
    • Most often, the bars will have different widths
  • Occasionally you will be asked to add a frequency polygon to the histogram
    • This is done by joining up the midpoints at the top of each bar
    • You should not join up the first or last midpoint to the x – axis (it is not really a polygon!)

How do we interpret a histogram?

  • It is important to remember that the y – axis does not tell us the frequency of each bar in the histogram
  • The area of the bar gives information about the frequency
    • Most of the time, the frequency will be the area of the bar and is found by multiplying the class width by the frequency density
    • Occasionally, the frequency will be proportional to the area of the bar
      • Frequency = k space cross times space area
      • In these cases more information will be given to help you find the value of k
  • You may be asked to find the frequency of part of a bar within a histogram
    • Find the area of that section of the bar using any information you have already found out
    • You will need to have found the value of k first

Worked Example

The table below and its corresponding histogram show the mass, in kg, of some new born bottlenose dolphins.

Mass, m kg

Frequency

4 space less or equal than space m space less than space 8

4

8 space less or equal than space m space less than space 10

15

10 space less or equal than space m space less than space 12

19

12 space less or equal than space m space less than space 15

 

15 space less or equal than space m space less than space 30

6

2-2-3-histograms-we-diagram

(i)
Use the histogram to find the value of k in the formula
frequency space density space equals space k space cross times space fraction numerator frequency over denominator class space width end fraction 
(ii)
Estimate the number of dolphins whose weight is greater than 13 kg.
(i)
Use the histogram to find the value of in the formula
frequency space density space equals space k space cross times space fraction numerator frequency over denominator class space width end fraction

2-2-3-histograms-we-solution-part-1

(ii)
Estimate the number of dolphins whose weight is greater than 13 kg.

2-2-3-histograms-we-solution-part-2

Exam Tip

  • Remember that the area of a bar in a histogram is not always the frequency itself but could be proportional to the frequency. Look carefully at the scales on the axes, it will rarely be a simple 1 unit to 1 square.



  • 1. Statistical Sampling
    • 1.1 Sampling & Data Collection
      • 1.1.1 Sampling & Data Collection
    • 2. Data Presentation & Interpretation
      • 2.1 Statistical Measures
        • 2.1.1 Basic Statistical Measures
          • 2.1.2 Frequency Tables
            • 2.1.3 Standard Deviation & Variance
            • 2.2 Data Presentation
              • 2.2.1 Data Presentation
                • 2.2.2 Box Plots & Cumulative Frequency
                  • 2.2.3 Histograms
                  • 2.3 Working with Data
                    • 2.3.1 Outliers & Cleaning Data
                      • 2.3.2 Intrepreting Data
                      • 2.4 Correlation & Regression
                        • 2.4.1 Correlation & Regression
                        • 2.5 Further Correlation & Regression (A Level only)
                          • 2.5.1 PMCC & Non-linear Regression
                            • 2.5.2 Hypothesis Testing for Correlation
                          • 3. Probability
                            • 3.1 Basic Probability
                              • 3.1.1 Calculating Probabilities & Events
                                • 3.1.2 Venn Diagrams
                                  • 3.1.3 Tree Diagrams
                                  • 3.2 Further Probability (A Level only)
                                    • 3.2.1 Set Notation & Conditional Probability
                                      • 3.2.2 Further Venn Diagrams
                                        • 3.2.3 Further Tree Diagrams
                                          • 3.2.4 Probability Formulae
                                        • 4. Statistical Distributions
                                          • 4.1 Probability Distributions
                                            • 4.1.1 Discrete Probability Distributions
                                            • 4.2 Binomial Distribution
                                              • 4.2.1 The Binomial Distribution
                                                • 4.2.2 Calculating Binomial Probabilities
                                                • 4.3 Normal Distribution (A Level only)
                                                  • 4.3.1 The Normal Distribution
                                                    • 4.3.2 Normal Distribution - Calculations
                                                      • 4.3.3 Standard Normal Distribution
                                                      • 4.4 Choosing Distributions (A Level only)
                                                        • 4.4.1 Modelling with Distributions
                                                          • 4.4.2 Normal Approximation of Binomial
                                                        • 5. Hypothesis Testing
                                                          • 5.1 Hypothesis Testing
                                                            • 5.1.1 Hypothesis Testing
                                                            • 5.2 Hypothesis Testing (Binomial Distribution)
                                                              • 5.2.1 Binomial Hypothesis Testing
                                                              • 5.3 Hypothesis Testing (Normal Distribution) (A Level only)
                                                                • 5.3.1 Sample Mean Distribution
                                                                  • 5.3.2 Normal Hypothesis Testing
                                                                • 6. Large Data Set
                                                                  • 6.1 Large Data Set
                                                                    • 6.1 Large Data Set


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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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