Statistics & probability

Probability & Statistics
SJ van Heerden
6 April 2014

What is Probability?…
• Probability is the likelihood or chance of something
happening.
• And that something can be pretty much anything – from
something boring like getting a head when you toss a coin, to
something much more interesting, like the probability of a R5.2
billion space rocket returning safely from it’s mission.
• We use probabilities every single day in the decisions we
make without even knowing it.
• The tools I am going to arm you with in this section will
hopefully enable you to understand and enjoy probability.

The Lingo you need:
Experiment – now, this doesn’t necessarily mean rats and
men in lab coats, it just means something is
happening, and someone else is observing what happens
Outcomes – these are all the different things that could
happen in a probability experiment. One question you must
always ask yourself is: “is every outcome equally likely to
happen?”
Event – this just means the particular outcome or outcomes
we are interested in

P (event) = the number of ways the event could happen
the total number of possible equally likely outcomes
What each bit means:
P (event) – this is just a quick way of writing: “the probability of an
event happening”. e.g. P (rain on Wednesday) means “the
probability it will rain on Wednesday”
The number of ways the event could happen – you have to carefully
count up all the different ways there are of the event you are
interested in actually occurring

The total number of possible equally likely outcomes – this is the
hardest and most important bit. You must carefully count up
all the total possible things that could happen, but you must
remember that they must all be equally likely!
And when you get your answer using the formula, it will be a
fraction, and you should simplify it you can!
And everything you need to know about probability comes from
this formula!
Now, we will discover all the important probability concepts,
using a few examples…

Example 1
Imagine, for some reason, someone has put each of the 26 letters
of the alphabet on identical tiles and chucked them in a bag. This
person then decides it would be fun to get you to close your eyes
and pick tiles out of this bag. You are not so sure, but you decide
to give it a go as it will be a good way of learning about probability.
Question 1: What is the probability of picking out a vowel?
Well, let’s use our formula:
( )
5
26
vowelPQuick way of
writing: ”the
probability of
picking a vowel”
The number of
vowels in the bag:
a, e, i, o, u
The number of equally likely
things that could happen: we
could have picked any of the 26
identical tiles!

Question 2: What is the probability of picking out a letter?
The answer might be obvious, but let’s see why:
( )
26
1
26
lett rP eQuick way of
writing: ”the
probability of
picking a letter”
There are 26 letters in the
bag, and any will do for us!
There are 26 equally likely
outcomes
Rule 1: If something has a probability of 1, it is CERTAIN to
happen

Question 3: What is the probability of picking out a number?
Again, it’s easy, but look why it works!
( )
0
0
26
num eP b rQuick way of
writing: ”the
probability of
picking a vowel”
There is nothing in the bag
we are interested in as
there are no numbers!
The number of equally likely
things that could happen: we
could have picked any of the 26
identical tiles!
Rule 2: If something has a probability of 0, it is IMPOSSIBLE
Rule 3: All probabilities lie between 0 and 1, so if you find
yourself with a negative answer, or something like 2.4, then
you have done something wrong!!!

Question 4: What is the probability of picking the letter A, given that your friend tells you the
tile in your hand is a vowel?
Now, believe it or not, this question is bordering on being A Level, but our good old formula
still works!
( )
1
5
AP
Quick way of
writing: ”the
probability of
picking A”
There is only one letter A
Seeing as our friend has told us
that our tile is a vowel, there are
5 equally likely possibilities

Example 2
Ms van Heerden is wondering what her Mom will make her for
Lunch. Going off past experience, the probability of it being
Peanut Butter sandwich is 0.6, Scrambled Egg is 0.25, Fish and
chips is 0.1, and no food at all is 0.05
Question 1: What is the probability Ms van Heerden has a peanut butter sandwich or
scrambled egg?
Now there is a key word in that question and it is OR. This means that Ms van Heerden can
have either a peanut butter sandwich or scrambled egg, it does not matter which occurs.
So what do you think we need to do with the probabilities?:
Now, events like these are called– MUTUALLY EXCLUSIVE. All that means is that both events
cannot occur at the same time – Ms van Heerden can’t have both at the same time.
Rule 4: To find the probability of something happening OR
something else happening, just add up your probabilities
P(PB OR Egg) = P(Peanut Butter ) + P(Scrambled Egg)
= 0.6 + 0.25
= 0.85

Question 2: What is the probability Ms van Heerden actually gets her lunch made?
Now one way to do this is to add up all the possible food outcomes… but there is a quicker
way:
There is only one outcome that results in no food, and the probability of any of the four
outcomes occurring is 1 as it is certain that something will occur so:
( ) 1 ( )
1 0.05 = 0.95
nfP o foPood a e dm d o
Rule 5: To find the probability of something not
happening, subtract the probability of that something
happening from 1.

Question 3: What is the probability Ms van Heerden has a Peanut Butter sandwich one day
and the next?
Now, to answer this question, we must first make an assumption: what Ms van Heerden has
for lunch one day and the next day are INDEPENDENT of each other – in other words, the
choice of yesterday’s lunch does not affect today’s choice.
Now, that may seem pretty unrealistic, but you will tend to find that a lot of probability
questions ask you to assume that two events are independent of each other.
Again there is a very important word in the question that helps you spot this type – AND.
To work out the probability of something happening and something else happening you do
not add up the probabilities, as in this case you would get an answer bigger than 1, which is
impossible.
Rule 6: To find the probability of something happening AND
something else happening, just multiply your probabilities
together!
P(PB AND PB) = P(Peanut Butter ) x P(Peanut Butter )
= 0.6 x 0.6
= 0.36

Example 3
I am feeling pretty bored so I decide to roll a pair of dice and each
time subtract the highest score from the lowest
Question 1: Draw a sample space diagram to show all the equally likely outcomes.
Classic opportunity to use a sample space diagram – two experiments, each with lots of
equally likely outcomes. Okay, so the outcomes from each dice go across the top and up the
side, and the numbers in the middle come from subtracting the smallest number from the
biggest.
Question 2: What is the probability of getting a
score of 0?
36 equally likely outcomes, how many are 0?...
0( )
6 1
36 6
score ofP
1 2 3 4 5 6
1 0 1 2 3 4 5
2 1 0 1 2 3 4
3 2 1 0 1 2 3
4 3 2 1 0 1 2
5 4 3 2 1 0 1
6 5 4 3 2 1 0
Dice2
Dice 1

Question 3: If you rolled the two dice 180 times, how many times would you expect to get a
score of 1?
This is where we can use probabilities to help us predict results.
There are 36 equally likely outcomes, and 10 of them give us a score of 1.
So, if we rolled the dice 36 times, we’d expect to get a score of 1 on 10 occasions
So, What about if we rolled them 180 times?...
50 times!
Classic Mistakes
The most common mistake pupils make with probability question is
they mix up Mutually Exclusive and Independent events, and end up
multiplying when they should be adding!
Learn to look for key words in questions:
Mutually
Exclusive
Key Words: Or, Either
Independent Key Words: And, Both, Together

What do you make of this argument…
If you toss two coins together, the probability of getting one head and one tail is: because…
1
3
(
1
3
)tP ahead nd ail
One way you can get a
head and a tail
Three equally likely outcomes:
head-head, head-tail, or tail-tail
Sounds convincing, doesn’t it?... Until you think about it and realise it’s absolute rubbish!
1. There is not just “one way you can get a head and a tail”… there are two: head-tail and tail-head!
2. The three outcomes might be “equally likely”, but there’s one missing… tail-head!
So where did we go wrong?...
Well, when you have two experiments happening at the same time (like our two coins here), the safest
way to ensure you account of all the outcomes is to knock up a SAMPLE SPACE DIAGRAM…
Head Tail
Head H-H H-T
Tail T-H T-T
Coin 1
Coin2
So, we can now clearly see that there are 4 equally
likely outcomes, and so get our probabilities, we just
need to count up the number of outcomes we are
interested in:
( )
2 1
4 2
taP ahead ilnd
1
4
( )head headP and

What are Tree Diagrams, and when do you use them?
•You tend to use tree diagrams to answer questions where there is more than one
experiment going on at once, and the outcomes are not all equally likely
BONUS: Tree Diagrams can be used to answer questions involving both independent
and non-independent events.
The Two Absolutely Crucial Rules of Tree Diagrams
1. We MULTIPLY probabilities going ACROSS
2. We ADD probabilities going DOWN
NOTE: And a really good way to check you have down everything right is to add up all the
probabilities at the end of your branches… because you know that the sum of the
probabilities of all outcomes must add up to 1!

Example 1
Sarah is very bored so she puts twelve coloured cubes in a bag. Five of the cubes are red
and 7 are blue. She decides it would be fun to remove a cube at random from the bag and
note the colour before replacing it. For even more fun she then chooses a second cube at
random. What is the probability she pulls out two cubes of the same colour?
1. What are our two experiments so we can spilt up our tree diagram?...
Well, what about Sarah’s “first pick” and then “second pick”?
2. Do we know what the probability of picking a red cube is?...
Well, there are 12 cubes in the bag, and 5 of them are red, so…
3. How about a blue cube?...
Again, 7 blues out of the 12 in the bag, so…
4. On our second pick, do our probabilities change?...
Well, there is a crucial little phrase hidden in the question: “replacing it”. Because Sarah pops
the cube back into the bag after each pick, whatever she gets on her first pick has no
effect whatsoever on what she gets on here second, so the probabilities remain the same.
If you want to be really fancy about this (and why not!), you could say that because Sarah
replaces the cubes, the events are INDEPENDENT of each other!

Check: Our probabilities add up
to 1, so it’s looking good!
Note: I wouldn’t
bother simplifying
my fractions here,
as it makes the
adding up easier
when they have
the same
denominator!
x
+
144
1
144
Question: What is the probability she gets two cubes of the same colour?...
Well, the end of which braches give us that?
( )
( ) ( )
25 49
144 1
74
14444
red
P same colour
blueP and P a bluered nd
Now we can simplify
37
72

Example 2
For many years, Hannah and George have been locked in some pretty heated games of
Scrabble and Monopoly. The probability that Hannah wins at Scrabble is 0.7, and the
probability that George wins at Monopoly is 0.65. One rainy day they sit down for another
fierce battle. What is the probability George wins both games?
Well, what about “Scrabble” and then “Monopoly”?
2. We know the probability Hannah wins at Scrabble is 0.7, but what about George?...
Well, either one wins, or the other (we assume no draws), so the two probabilities must add up to 1
So…
3. And how about Hannah winning at Monopoly?... It’s a similar sort of thing…
4. On the second game, do our probabilities change?...
Well, because the question does not say so, we must assume that the probabilities stay the same,
and the results in Scrabble and Monopoly are INDEPENDENT.
( ) 0.31 ( ) 1 0.7P wins Scrabble P wins ScrabbG H le
( ) 1 ( 0) 1 0.6 355 .P wins Mon P winsG MoH n

1
x
+
Check: Our probabilities add up to
1, so it’s looking good!
Question: What is the probability George wins both games?
Well, we just follow the bottom branch…
( ) 0.3 0 0.19. 565P wins botG o he rge

Example 3
Sarah is bored again, so it’s back to the bag of beads! However, this time she really
decides to spice things up. She still has 12 beads, but this time there are 5 red, 6 blue
and 1 green. Crazier still, when she picks one out this time, she decides not to put it
back! What is the probability that after two picks, Sarah has two beads that are the same
colour?
It must be “first pick” and then “second pick”?
2. The probabilities on the first pick should be easy enough:
3. It’s on the second pick that things start getting tricky.
Say Sarah picks a red out first, what is the probability of her picking a red out second?...
There are now only 4 reds in the bag, and there are only 11 beads as well!
Whenever things are not replaced, you have to think very carefully about the probabilities on your
branches!
4. There is a crucial phrase: “not to put it back”. This means that whatever happens on the first pick
DOES affect the probabilities on the second pick, so these events are… DEPENDENT!
( )
12
5
r dP e ( )
12
6
blP ue ( )
12
1
greP en
( )
11
4
r dP e

132
1
132
x
+
Check: Our probabilities add up to
1, so it’s looking good!
Note: a probability
of zero here makes
sense as there is
only 1 green!

Question: What is the probability she gets two beads of the same colour?...
( ) ( ) ( ) ( )
20 30
0
50
=
132132 132
red r green greeblue blueP same colour P and P and ne P andd
25
66
Thinking like a Tree Diagram
Sometimes you can answer a question by picturing a tree diagram in your head and imagining the
branches, without actually drawing one. This might just save you some precious minutes in an
exam…
Example 4
The probability I somehow find the energy to go to the gym on Monday is 0.3. If a miracle
happens and I do go to the gym on Monday, the probability I go again on Tuesday falls to
0.1. If I don’t go on Monday, the probability remains the same. What is the probability that:
(a) I go to the gym on both days
(b) I go to the gym on just one day?

(a) Right, let’s think about this… I need to go on both days… well the probability I go to the gym on
Monday is 0.3… and if I go on Monday, the probability I also go on Tuesday falls to 0.1… so to
find the probability I go on both days I would travel along both branches of my tree diagram, so I
must MULTIPLY!
( ) 0.3 0.1 0.03Mon TuP gym eand s
(b) This is a bit trickier… I only go to the gym on one day… how could that happen?... Well, I could go
on Monday, and then not go on Tuesday… OR I could give Monday a miss, and then go
Tuesday!... So what would be the probabilities of those?...
( ) 0.3 0 .9 2. 0 7TuesP gym but non tMo
( ) 0.7 0.210.3TueMonP sno gym but go
And these would be the ends of the branches, so to get the
probability of either happening, I need to ADD:
0.48

3. Averages and Measures of Spread

What are Averages and Measures of Spread, and why do we need them?
•Averages and Measures of Spread allow us to look at a huge load of data and make
some sense out of it, summarise it, and compare it to other huge sets of data.
•Averages and Measures of Spread are used every single day, whether it be crowd
attendance, or the salary of your average poor maths teacher.
The usefulness of the different types of Averages and Measures of Spread will be
looked at in The Big Cricket Example, so for now, let’s learn how to work them out!
1. The Mean
Whenever most people talk about an average, this is the one they… mean!
How to work out the Mean:
1. Add up all your data values
2. Divide this total by the number of data values

2. The Median
How to work out the Median:
1. Place all your data values in ascending order (biggest to smallest)
2. The piece of data in the middle is your median
NOTE: If you have an EVEN number of data values, there will be TWO pieces of data
in the middle. No problem, just add them together and divide by two to find the
number halfway between them… and this is your median!
3. The Mode
How to work out the Mode:
1. Find the most common piece of data (number or letter) and this is your mode!
NOTE: You can have no modes or more than one mode, and you must write them all
down!

4. The Range
The Range is a Measure of Spread, and tells you… well, how spread out the data is!
How to work out the Range:
1. Subtract the smallest data value away from the biggest data value!
NOTE: It’s one thing knowing how to work out the three averages and the measure of
spread, but it’s just as important to know how to interpret them! Hopefully this example will
help!
The Big Cricket Example
Andrew Flintoff and Michael Vaughn are having an argument in the bar trying to decide who
has had the better season with the cricket bat. Here are their scores:
Use your knowledge of Averages and Measures of Spread to decide which cricketer has had
the better season
20 35 22 55 60
10 17 32 64 86
14 32 50 24 30
0 0 0 2 0
15 5 370 250
0 3 5 0 1

NOTE: The first point to notice is that by just looking at the scores as they are makes it hard
to come to a decision about who has had the better season… that’s why we need Statistics!
Secondly, just adding up the total amount of runs and deciding that way would not be fair.
Why?... well, because they have not played the same number of games!
So… let’s work out some statistics!
1. The Mean
1. Add up all your data values
2. Divide this total by the number of data values
Andrew Flintoff
total runs scored: 551
total games played: 15
mean: 36.7 runs
Michael Vaughn
total runs scored: 651
total games played: 14
mean: 46.5 runs

What does this tell us? – well, it looks like, on average, Michael Vaughn has had the better
season
Good thing about the mean – notice how every single score was used to calculate the mean –
this means it gives a good summary of the whole season.
Bad thing about the mean – look at Michael Vaughn’s scores. He only had two decent ones,
and yet his mean is far higher than Andrew Flintoff’s! This is because the mean is significantly
affected by outliers – pieces of data which stand out for being really low or really high like the
two scores of 370 and 250. You could argue that these have distorted the result!

2. The Median
1. Place all your data values in ascending order (biggest to smallest)
2. The piece of data in the middle is your median
10 14 17 20 22 24 30 32 32 35 50 55 60 64 86
Note: there are the same number of data (7 pieces) either side of the box!
Median = 32 runs
0 0 0 0 0 0 1 2 3 5 5 15 250 370
Note: there are still the same number of data (6 pieces) either side of the box!
(1 2) 1 52 .
Median = 1.5 runs

What does this tell us? – well, this time it looks like Andrew Flintoff has had the better season
Good thing about the median – because we are only focussing on pieces of data in the
middle, outliers don’t have as big an effect, so they cannot distort the results!
Bad thing about the median – the problem here is that you are only looking at – at most – a
couple of pieces of data from each player. You could argue that the result is not
representative as a lot of pieces of data (scores) are just ignored!

3. The Mode
1. Find the most common piece of data (number or letter) and this is your mode!
Andrew Flintoff
mode: 32 runs
Michael Vaughn
mode: 0 runs
What does this tell us? – well, using the mode it again looks like Andrew Flintoff is on top!
Good thing about the mode – very speedy to work out!
Bad thing about the mode – can give distorted, or even no results. Imagine if Andrew Flintoff
only scored one innings of 32 runs… he would have no mode to compare! Or, imagine if he
instead scored a couple of innings of 200… the mode would then say this was his average!

What does this tell us? – well, one answer that I often here is this: “Michael Vaughn has the
biggest range, so he is the best!”… but that’s not quite right.
The bigger the range, the more spread out your scores are… so the less consistent (brilliant
maths word that always impresses examiners/teacher) your performance is.
So, I would argue, because Andrew Flintoff has a smaller range, his performance is more
consistent, and therefore he has had the better season!
Good thing about the range – gives a very quick measure of how spread out the data is
Bad thing about the range – unfortunately, this statistic is vulnerable to outliers as well! Michael
Vaughn had a couple of big scores, and look at the effect it had on his range!... This is why
mathematicians prefer to measure the spread of data using the Inter-quartile Range or
Standard Deviation… but don’t worry about them yet!
4. The Range
1. Subtract the smallest data value away from the biggest data value!
Andrew Flintoff
largest value: 86
smallest value: 10
range: 76 runs
Michael Vaughn
largest value: 370
smallest value: 0
range: 370 runs

So who is the better cricketer?...
Well, in the end, it’s up to you! The most important thing is that you have shown you
can calculate each of the statistics and – not a lot of people can do this - interpret
what they mean!

Estimating the Mean from Grouped Data Attendance Frequency
0 < A ≤ 5,000 5
5,000 < A ≤ 10,000 12
10,000 < A ≤ 15,000 24
15,000 < A ≤ 20,000 8
Example:
Calculate an estimate of the mean number of
fans attending the mighty Preston North End
football matches from the following table:
Okay, now do you see a problem here?... Look at the first group… we know there were 5 matches
where between 0 and 5,000 people turned up, but we don’t know exactly how many people were at
those matches!... One match could have had 1,309… another 4,510… we just don’t know!
So… the best we can do is to make an estimate!
And what is our best estimate for that first group?... Well, the MID-POINT… 2,500!
And that is how we calculate an estimate for the mean from grouped data:
1. Work out the mid-point
2. Work out the mid-point x Freq for each group
3. Use this formula:
Mean =
Sum of Mid-Point x Freq
Total Frequency
Attendance Mid-Point Frequency Mid-Point x Freq
0 < A ≤ 5,000 2,500 5 12,500
5,000 < A ≤ 10,000 7,500 12 90,000
10,000 < A ≤ 15,000 12,500 24 300,000
15,000 < A ≤ 20,000 17,500 8 140,000
TOTALS 49 542,500
54250
49
11,07
0
1
Mean
(nearest whole)

4. Cumulative Frequency and Box Plots

Why do we bother with Statistical Diagrams?
•We live in a world jam-packed full of statistics, and if we were forced to look at all the facts
and figures in their raw, untreated form, not only would we probably not be able to make any
sense out of them, but there is also a very good chance our heads would explode.
•Statistical Diagrams – if they are done properly - present those figures in a clear, concise,
visually pleasing way, allowing us to make some sense out of the figures, summarise them, and
compare them to other sets of data.
1. What is Cumulative Frequency?
Cumulative is just a posh way of saying “add up as you go along”
Frequency is just a posh word for “total”
So… if you put them together, you get a very posh way of saying “add the totals up as you go
along”

2. Adding a Cumulative Frequency Column
Before you can even start thinking about drawing a Cumulative Frequency Curve, you need to be
able to add a Cumulative Frequency column to your Frequency table.
Remember, Cumulative Frequency just means that you add up the frequencies as you go along,
so that is exactly what you do!
Big Example
To the right is a table showing the length of
time a group of 40 Year 10 students spent
playing on the Nintendo Wii on a gloomy week
in January. Draw a Cumulative Frequency
Curve, use it to estimate the Median and
Inter-Quartile Range, and construct a Box Plot
Hours spent playing Frequency
0 < h ≤ 1 2
1 < h ≤ 2 5
2 < h ≤ 3 10
3 < h ≤ 4 15
4 < h ≤ 6 5
6 < h ≤ 10 3
Hours spent playing Frequency Cumulative Freq
0 < h ≤ 1 2 2
1 < h ≤ 2 5 7
2 < h ≤ 3 10 17
3 < h ≤ 4 15 32
4 < h ≤ 6 5 37
6 < h ≤ 10 3 40
This is the number of
people who play for 1
hour or less
hours or less (5 + 2)
hours or less (5 + 2 + 10)
Check: This final entry should always equal the total frequency!

3. Drawing the Cumulative Frequency Curve
Hours spent playing Frequency Cumulative Freq
0 < h ≤ 1 2 2
1 < h ≤ 2 5 7
2 < h ≤ 3 10 17
3 < h ≤ 4 15 32
4 < h ≤ 6 5 37
6 < h ≤ 10 3 40
Remember: we plot Cumulative
Frequency (y axis) against the
upper boundary of each group
(x axis)
So… for group one it’s 1 on the
x axis and 2 on the y
and for group two, it’s 2 on the
x axis and 7 on the y…
Things to notice about the Cumulative Frequency Curve:
1. When you have finished plotting the points, join them up with a smooth curve.
2. The curve starts at (0, 0). This is because there is nobody playing less than 0 hours a week!
3. You must label your axis correctly, or you lose very easy marks!

1 2 3 4 5 6 7 8 9 10
5
10
15
20
25
30
35
40
Time Spent Playing Wii (hours)
Cumulative Frequency

4. Estimating the Median and Inter-Quartile Range
We have spent a while drawing our cumulative frequency curve, so we may as well use it. Very
quickly we can come up with estimates for the Median and the Inter-Quartile Range
(a) Median
As you hopefully remember, the Median is the MIDDLE value.
To find it we:
1. Work out what is 50% of our total frequency (half way up the
y axis)
2. Draw a horizontal line across until it hits our curve
3. When it hits the curve, draw a vertical line down to the x axis
4. The value on the x axis is our Median
(b) Inter-Quartile Range
For this we need to work out the upper quartile (UQ) and the lower quartile (LQ), and then calculate:
UQ - LQ
To find the Upper Quartile:
1. Work out what is 75% of our total frequency (three-quarters of the way up the y axis)
2. Draw a horizontal line across until it hits our curve
3. When it hits the curve, draw a vertical line down to the x axis
4. The value on the x axis is our Upper Quartile
The Lower Quartile is the same, but 25% (one-quarter) of the way up!

1 2 3 4 5 6 7 8 9 10
5
10
15
20
25
30
35
40
Median:
50% of 40 = 20
Median = 3.2 hours
Upper Quartile
75% of 40 = 30
UQ = 3.8 hours
Lower Quartile
25% of 40 = 10
LQ = 2.4 hours
Remember: The Median is a form of average, and just like the
Range, The Inter-Quartile Range is a measure of consistency
Inter-Quartile Range
= UQ – LQ
= 3.8 – 2.4
= 1.4 hours

5. Drawing Box Plots
Box Plots are another way of representing all the same information that can be found on a
Cumulative Frequency graph.
Top Tip: if you have the chance, draw your box plot directly below your cumulative frequency
graph, using the same scale on the x axis, and you can just extend the vertical lines
downwards and save yourself a lot of time!
Lowest value
Upper Quartile
Highest value
Lower Quartile
Median
Inter-Quartile Range
Range
Note: The minimum value is the lowest possible value of your first group, and the maximum
value is the highest possible value of your last group

1 2 3 4 5 6 7 8 9 10
5
10
15
20
25
30
35
40
Min Value = 0
LQ = 2.6
Median = 3.2
UQ = 3.8
Max Value = 10

Example
A group of 72 maths teachers were asked to choose their favourite TV show from a
list, and their responses are shown in the table on the right. Construct a pie chart to
illustrate this information
TV Show Total
Lost 12
Heroes 10
Desperate Housewives 4
Countdown 15
Teachers TV 13
The Beauty of Maths 18

1. Working out the Angles
•Before you can start to draw the pie chart, you need to know how big a slice each of the choices is
going to take up – in other words, you need to know the angle of each segment
•To work this out, you need to remember that there are 360 degrees in a circle
•That means there are 360 degrees to share between each of the people who took part in the
survey
•How many degrees does each person get?... Well, divide 360 by the number of people surveyed!
To Calculate the Angles
1. Add up the total number of pieces of data
2. Divide 360 by this number – this tells you how many degrees is allocated to each piece of
data
3. To work out the size of angle for each category, multiply the answer to 2. by the number of
people in each category – rounding your answers sensibly if you need to.
4. Check: Before you start to draw, make sure you check that your total number of degrees
does add up to 360!

Our Example:
1. So, we have a total of 72 teachers who were surveyed.
2. 360 72 = 5
So… each teacher is worth 5 degrees on our pie chart
3. We know how many teachers are in each segment, so let’s use our answer to 2. to work out
what angle each segment gets
TV Show Total Working Out Angle of Segment
Lost 12 12 x 5 = 60 600
Heroes 10 10 x 5 = 50 500
Desperate Housewives 4 4 x 5 = 20 200
Countdown 15 15 x 5 = 75 750
Teachers TV 13 13 x 5 = 65 650
The Beauty of Maths 18 18 x 5 = 90 900
Remember: Check this column adds
up to 360 before you move on!

2. Drawing the Pie Chart
You’ve done all the hard work, and drawing the pie chart should be easy… but you’ll be
amazed how many people mess it up, so take your time and follow these steps…
1. Draw a circle using a compass. Mark the centre with a
dot and draw a straight line from the centre up to the
right of your circle
2. Carefully place your angle measurer along the line,
with the centre exactly on the centre of the circle.
Now, count around from 0 until you reach the correct
number of degrees – in this case 600 – and place a
dot

3. Join up your dot to the centre with a straight line, and
label your segment.
4. Now, this is the tricky bit… turn your pie chart
clockwise until your new line is horizontal (where the
first line used to be). Now you can mark your next
angle in exactly the same way.
Lost

5. Keep doing this until you have drawn all your segments
6. If you want to you can colour in your segments, but you must remember to
label them clearly, or add a key!
Check: You will know if you have got it right if the line to make your final segment is
the very first line you drew!
Maths Teachers' Favourite TV Shows
Lost
Heroes
Desperate
Housewives
Countdown
Teachers TV
The Beauty of
Maths

3. What CAN we tell from Pie Charts
•Well, if you look back at our pie chart, you will see that it shows pretty clearly that The Beauty
of Maths was the most popular choice amongst our maths teachers, whereas Desperate
Housewives was the least popular
•If you want to be really fancy, you might be able to say things like: “roughly 3 times as many
teachers preferred Lost to Desperate Housewives”
4. What CAN’T we tell from Pie Charts
•Well, imagine we were just given our pie chart (and no original data), and someone said: “how
many maths teachers said that Countdown was their favourite show?”, what would we say?...
•Well, probably not a lot, because there is no way of knowing!
•Unless we are told how many people were surveyed all together, we cannot answer that
question!
•When making statements based on Pie Charts, just make sure what you are saying is
definitely, 100% true!

5. Interpreting Pie Charts
Big Example 2
240 Maths teachers were asked “what is your
favourite drink?” and a pie chart was drawn to
show to information.
Work out how many teachers preferred coffee
Tea
Coffee
J20
Squash
Milk
Water
108o
84o
60o
48o 36o
24o
To answer this question we must do the opposite of what we did when we were drawing the pie
chart – we must use our angles to find our totals!
Let’s look at coffee… it takes up 840 out of 3600, and what we want to know is “how much does it
take up out of our 240 people?”
Well, what about using this as an excuse to show off our Algebra skills!...
84
360 0
?
24
Multiply both sides by 240 84
240
360
?
So, turning to our calculator, we get an answer of… 56 people

1. What are Stem and Leaf Diagrams
•To be honest, Stem and Leaf Diagrams are just a fancy way of listing a fairly large group
of numbers in order
•They are seen as a quicker, more convenient, and ultimately more useful way of
presenting data than just a long list of numbers.
•An example of a typical Stem and Leaf Diagram is on the right
stem
leaves

2. Constructing a Stem and Leaf Diagram
1. Decide on your stems – these are the digits which go down the left hand side of your diagram. You
should choose them in a way so that you have between 4 and 10 groups, and so that each of your
leaves is only one digit!
2. Go through your data, in the order in which it is written, and add it to the correct stem on your
diagram. I would mark each piece of data once it has been entered, so you don’t loose your spot!
3. When completed, this is your un-ordered stem and leaf diagram
4. Now draw yourself another stem and leaf diagram, but this time put the leaves in order!
Note: Everyone seems to want to jump straight to the ordered stem and leaf diagram, but I promise
you this way is quicker and a lot safer in terms of mistakes!
Example
Here are the times (in minutes) that it takes Ms van Heerden to actually get out of bed after her
alarm has sounded on a Monday morning:
12 6 20 24 52 41 3 35 55 32 11 13 2 25 38 39 41 52 13 59 18 22 29 35
Use the data to construct a Stem and Leaf Diagram, and then calculate the Median and Inter-
Quartile Range

Our Example
1. For our stems, we only need the first digit of
each piece of data, and I think 6 groups should
do us!
Note: to make sure we can enter the single digit
pieces of data, we must make our first stem
start with 0
0
1
2
3
4
5
0 6
1 2
2 0
3
4
5
2. Next we begin to go through our data, creating them leaves of our diagram, marking off each
piece of data as we use it…
12 6 20 24 52 41 3 35 55 32 11 13 2 25 38 39 41 52 13 59 18 22 29 35
3. Continuing like this eventually
gives us our un-ordered stem
and leaf diagram
0 6 3 2
1 2 1 3 3 8
2 0 4 5 2 9
3 5 2 8 9 5
4 1 1
5 2 5 2 9

Things to Notice:
a) For numbers like 20, we must place a 0 as our leaf
b) Single digit numbers are placed on the top stem
c) If we come across a number that we have already recorded, we must record it again!
0 6 3 2
1 2 1 3 3 8
2 0 4 5 2 9
3 5 2 8 9 5
4 1 1
5 2 5 2 9
0 2 3 6
1 1 2 3 3 8
2 0 2 4 5 9
3 2 5 5 8 9
4 1 1
5 2 2 5 9
5. And the final thing we must remember to add to our diagram is a KEY!
You must let anyone who looks at your diagram know exactly what each of the leaves stands
for… so in this case, I have chosen the 2 and the 0 from the 3rd row, and just explained that
this is actually 20!
4. Having made sure that we have 24 digits as the leaves of our diagram (there are 24 pieces of
data!), we can now very quickly change our unordered diagram into an ordered one by placing
the leaves on each stem… in order!
Key
2 0 = 20

3. Finding the Median and Inter-Quartile Range from a Stem and Leaf Diagram
Remember: our Stem and Leaf Diagram is just a group of numbers, written out in order… and
so we don’t have to learn any different skills to find the median and inter-quartile range!
(a) Finding the Median
It’s the usual thing… the median is the middle number… and if there is an even amount of
numbers, then you will have two numbers in the middle.
Draw a box around the number/numbers you think are in the middle, and make sure you have
the same amount of numbers on either side!
0 2 3 6
1 1 2 3 3 8
2 0 2 4 5 9
3 2 5 5 8 9
4 1 1
5 2 2 5 9
Check: There are 11 numbers to the left of our box,
and 11 numbers to the right!
Our median is half way between our two numbers
in the box, and so is… 27!
So many people would put 7… but remember what
the leaves actually stand for!

(b) Finding the Inter-Quartile Range
Remember: Inter-Quartile Range = Upper Quartile – Lower Quartile
The way I do this is to think of the Lower Quartile as being the median of the lower half of
numbers… and the Upper Quartile as the median of the upper half of numbers.
And I just find these values the same old way… using my boxes, and making sure there are the
same amount of numbers on either side!
0 2 3 6
1 1 2 3 3 8
2 0 2 4 5 9
3 2 5 5 8 9
4 1 1
5 2 2 5 9
Lower Quartile = 13
(5 numbers either side in the lower half)
Upper Quartile = 40
(5 numbers either side in the upper half)
Inter-Quartile Range = 40 – 13
= 27
13 13
1
2
3
39 41
4
2
0

4. What’s GOOD about Stem and Leaf Diagrams?
•Well, the major advantage over things like bar charts and histograms, is that no information is
lost – the stem and leaf diagram keeps and allows you to see each original piece of data
•It is also quite an effective way of ordering and displaying relatively small sets of data
5. What’s BAD about Stem and Leaf Diagrams?
•Well, it’s quite time consuming, and impractical for large data sets. Imagine how long it would
take to sort over 300 pieces of data, and how complicated the final diagram would look!

Example
To the right is a table showing the length of applause after Ms van Heerden announces
that there will be no homework tonight. Construct a Bar Chart and a Histogram, and
comment on the differences
Length of Applause (mins) Frequency
0 < a ≤ 1 2
1 < a ≤ 2 4
2 < a ≤ 3 15
3 < a ≤ 5 10
5 < h ≤ 8 6
8 < h ≤ 13 5

1. Drawing a Bar Chart (Frequency Diagram)
Note: Sometimes bar charts are called Frequency Diagrams!
1. Decide on an appropriate scale to fit the paper you are working with – as a general rule, the
bar chart (or any statistical diagram, for that matter) should take up between half and three-
quarters of the space you have to work with.
Crucial: Your numbers must go up in equal steps!... see 8. Scatter Diagrams for examples of
some very dodgy scales!
2. Label your axes. Is it usual to put frequency (total) on the y axis, and whatever the data is
along the x axis.
3. Carefully draw in your bars… and add a title!
Note: In examples like this where the groups are numbers, then it is usual to have the bars
touching each other
If the groups were categories (such as “colour of cars”), then you could have gaps between
your bars if you like!

A Bar Chart to show the Length of Applause after Ms van Heerden says “no homework”
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
2
4
6
8
10
12
14
16
18
20
applause (mins)
frequency

2. Drawing a Histogram
The major difference between a bar chart and a histogram is what goes on the y axis
On a Bar Chart it is Frequency
On a Histogram it is… Frequency Density!
The reasons for this will be discussed soon!
1. Add two extra columns to your table… Group Width and Frequency Density
2. To work out Group Width, we just do the upper limit of each group minus the lower limit
3. To work out Frequency Density, we use this lovely formula:
4. We then plot frequency density on the y axis, and our data on the x axis as before
Note: In Histograms you have no choice… the bars must always be touching!
Frequency Density =
Frequency
Group Width

Length of Applause
(mins)
Frequency Group Width
Frequency
Density
0 < a ≤ 1 2 1 2
1 < a ≤ 2 4 1 4
2 < a ≤ 3 15 1 15
3 < a ≤ 5 10 2 5
5 < h ≤ 8 6 3 2
8 < h ≤ 13 5 5 1
Group Width: 1 – 0 = 1
Group Width: 13 – 8 = 5
Frequency Density:
2 ÷ 1 = 2
Frequency Density:
5 ÷ 5 = 1

A Histogram to show the Length of Applause after Ms van Heerden says “no homework”
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
2
4
6
8
10
12
14
16
18
20
applause (mins)
frequency density
Note: Sometimes you get
asked to draw a Frequency
Polygon. Do not fear, this is
just a Histogram, but with
the top mid-points of each
bar joined together with
straight lines!
2
4
6
8
10
12
14
16
18
20
applause (mins)
frequency density

3. What is the Point of Histograms?
• Have a quick look back at the two diagrams
• On the Bar Chart, which group looks like it has the most people in it?… maybe the last
one?... because it takes up such a large area compared to the other groups… but if you look
on the table of data, this group only had a total of 5!
So… Bar Charts can be deceptive
• But look at the Histogram…the group that looks the biggest now is the “between 2 and 3
minutes” group… and this is the group that has the highest frequency!
• The reason for this is that in a Histogram, the areas of the bars are proportional to the
frequency, and not just the height like a bar chart.
• Note: If all our groups had the same width, it wouldn’t matter, but often they do not, so that is
why Histograms tend to be used more than Bar Charts!

4. Interpreting Histograms
Example: Here is a Histogram showing the time taken by some year 7s to complete all their times
tables. Find the frequencies of each group.
This time we are given Frequency Density, and asked to work out Frequency… well, if we do a little
re-arranging to our formula we get…
Frequency = Frequency Density x Group Width
Time (mins) Frequency Density Group Width Working Frequency
0 < t ≤ 3 2 3 2 x 3 = 6 6
3 < t ≤ 5 4 2 4 x 2 = 8 8
5 < t ≤ 6 10 1 10 x 1 = 10 10
6 < t ≤ 8 5 2 5 x 2 = 10 10
8 < t ≤ 12 1.5 4 1.5 x 4 = 6 6
1 2 3 4 5 6 7 8 9 10 11 12 13 14
2
4
6
8
10
12
14
16
18
20
time (mins)
frequency density

References
- Barton, C. (2013). Probability[PowerPoint Presentation]. Available at:
http://www.tes.co.uk/Download.aspx?storycode=6075878&type=X&id=6792049 . Accessed on:
7 April 2014
- Barton, C. (2013). Tree Diagrams[PowerPoint Presentation]. Available at:
7 April 2014
- Barton, C. (2013). Averages[PowerPoint Presentation]. Available at:
7 April 2014
- Barton, C. (2013). Cumulative Frequency and Box Plots[PowerPoint Presentation]. Available at:
7 April 2014
- Barton, C. (2013). Pie Charts[PowerPoint Presentation]. Available at:
7 April 2014
- Barton, C. (2013). Stem and Leaf Diagrams[PowerPoint Presentation]. Available at:
7 April 2014
- Barton, C. (2013). Bar Charts and Histograms[PowerPoint Presentation]. Available at:
7 April 2014

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