### Statistics in the Math

At the this time, Thursday 12 may 2016, 5 ; 58 pm. i wanna to make a post about statistics on the math, for me statistics not too hard because this is only about the data on pie, diagram, or line. But , it's not simply i thinking, i mean that is's the wide knowledge i ever study. In below there were a material about statistics.

Statistics - Grade 10
15.1 Introduction
Information in the form of numbers, graphs and tables is all around us; on television, on the
radio or in the newspaper. We are exposed to crime rates, sports results, rainfall, government
spending, rate of HIV/AIDS infection, population growth and economic growth.
This chapter demonstrates how Mathematics can be used to manipulate data, to represent or
misrepresent trends and patterns and to provide solutions that are directly applicable to the world
around us.
Skills relating to the collection, organisation, display, analysis and interpretation of information
that were introduced in earlier grades are developed further.
15.2 Recap of Earlier Work
The collection of data has been introduced in earlier grades as a method of obtaining answers
to questions about the world around us. This work will be briefly reviewed.
15.2.1 Data and Data Collection
Data
Definition: Data
Data refers to the pieces of information that have been observed and recorded, from an
experiment or a survey. There are two types of data: primary and secondary. The word
”data” is the plural of the word ”datum”, and therefore one should say, ”the data are” and
not ”the data is”.
Data can be classified as primary or secondary, and primary data can be classified as qualitative
or quantitative. Figure 15.1 summarises the classifications of data.
Primary data describes the original data that have been collected. This type of data is also
known as raw data. Often the primary data set is very large and is therefore summarised
or processed to extract meaningful information.
Qualitative data is information that cannot be written as numbers.
Quantitative data is information that can be written as numbers.
Secondary data is primary data that has been summarised or processed.
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15.2 CHAPTER 15. STATISTICS - GRADE 10
data
primary secondary
qualitative quantitative
Figure 15.1: Classes of data.
Purpose of Data Collection
Data is collected to provide answers that help with understanding a particular situation. For
example:
• The local government might want to know how many residents have electricity and might
ask the question: ”Does your home have a safe, independent supply of electricity?”
• A supermarket manager might ask the question: “What flavours of soft drink should be
stocked in my supermarket?” The question asked of customers might be “What is your
favourite soft drink?” Based on the customers’ responses, the manager can make an
informed decision as to what soft drinks to stock.
• A company manufacturing medicines might ask “How effective is our pill at relieving a
headache?” The question asked of people using the pill for a headache might be: “Does
taking the pill relieve your headache?” Based on responses, the company learns how
effective their product is.
• A motor car company might want to improve their customer service, and might ask their
customers: “How can we improve our customer service?”
• A cell phone manufacturing company might collect data about how often people buy new
cell phones and what factors affect their choice, so that the cell phone company can focus
on those features that would make their product more attractive to buyers.
• A town councillor might want to know how many accidents have occurred at a particular
intersection, to decide whether a robot should be installed. The councillor would visit the
local police station to research their records to collect the appropriate data.
However, it is important to note that different questions reveal different features of a situation,
and that this affects the ability to understand the situation. For example, if the first question in
the list was re-phrased to be: ”Does your home have electricity?” then if you answered yes, but
you were getting your electricity from a neighbour, then this would give the wrong impression
that you did not need an independent supply of electricity.
15.2.2 Methods of Data Collection
The method of collecting the data must be appropriate to the question being asked. Some
examples of data collecting methods are:
1. Questionnaires, surveys and interviews
2. Experiments
3. Other sources (friends, family, newspapers, books, magazines and the Internet)
The most important aspect of each method of data collecting is to clearly formulate the question
that is to be answered. The details of the data collection should therefore be structured to take
your question into account.
For example, questionnaires, interviews or surveys would be most appropriate for the list of
questions in Section 15.2.1.
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CHAPTER 15. STATISTICS - GRADE 10 15.3
15.2.3 Samples and Populations
Before the data collecting starts, an important point to decide upon, is how much data is needed
to make sure that the results give an accurate reflection to the answers that are required for the
study. Ideally, the study should be designed to maximise the amount of information collected
while minimising the effort. The concepts of populations and samples is vital to minimising
effort.
The following terms should be familiar:
Population describes the entire group under consideration in a study. For example, if you
wanted to know how many learners in your school got the flu each winter, then your
population would be all the learners in your school.
Sample describes a group chosen to represent the population under consideration in a study.
For example, for the survey on winter flu, you might select a sample of learners, maybe
one from each class.
Random sample describes a sample chosen from a population in such a way that each member
of the population has an equal chance of being chosen.
Choosing a representative sample is crucial to obtaining results that are unbiased. For example,
if we wanted to determine whether peer pressure affects the decision to start smoking, then the
results would be different if only boys were interviewed, compared to if only girls were interviewed,
compared to both boys and girls being interviewed.
Therefore questions like: ”How many interviews are needed?” and ”How do I select the subjects
for the interviews?” must be asked during the design stage of the interview process.
The most accurate results are obtained if the entire population is sampled for the survey, but
this is expensive and time-consuming. The next best method is to randomly select a sample of
subjects for the interviews. This means that whatever the method used to select subjects for the
interviews, each subject has an equal chance of being selected. There are various methods of
doing this but all start with a complete list of each member of the population. Then names can
be picked out of a hat or can be selected by using a random number generator. Most modern
scientific calculators have a random number generator or you can find one on a spreadsheet
program on a computer.
If the subjects for the interviews, are randomly selected then it does not matter too much how
many interviews are conducted. So, if you had a total population of 1 000 learners in your school
and you randomly selected 100, then that would be the sample that is used to conduct your
survey.
15.3 Example Data Sets
The remainder of this chapter deals with the mathematical details that are required to analyse
the data collected.
The following are some example sets of data which can be used to apply the methods that are
being explained.
15.3.1 Data Set 1: Tossing a Coin
A fair coin was tossed 100 times and the values on the top face were recorded.
15.3.2 Data Set 2: Casting a die
A fair die was cast 100 times and the values on the top face were recorded. The data are recorded
in Table 15.3.2.
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15.3 CHAPTER 15. STATISTICS - GRADE 10
H T T H H T H H H H
H H H H T H H T T T
T T H T T H T H T H
H H T T H T T H T T
T H H H T T H T T H
H T T T T H T T H H
T T H T T H T T H T
H T T H T T T T H T
T H T T H H H T H T
T T T H H T T T H T
Table 15.1: Results of 100 tosses of a fair coin. H means that the coin landed heads-up and T
means that the coin landed tails-up.
3 5 3 6 2 6 6 5 5 6 6 4 2 1 5 3 2 4 5 4
1 4 3 2 6 6 4 6 2 6 5 1 5 1 2 4 4 2 4 4
4 2 6 4 5 4 3 5 5 4 6 1 1 4 6 6 4 5 3 5
2 6 3 2 4 5 3 2 2 6 3 4 3 2 6 4 5 2 1 5
5 4 1 3 1 3 5 1 3 6 5 3 4 3 4 5 1 2 1 2
1 3 2 3 6 3 1 6 3 6 6 1 4 5 2 2 6 3 5 3
1 1 6 4 5 1 6 5 3 2 6 2 3 2 5 6 3 5 5 6
2 6 6 3 5 4 1 4 5 1 4 1 3 4 3 6 2 4 3 6
6 1 1 2 4 5 2 5 3 4 3 4 5 3 3 3 1 1 4 3
5 2 1 4 2 5 2 2 1 5 4 5 1 5 3 2 2 5 1 1
Table 15.2: Results of 200 casts of a fair die.
15.3.3 Data Set 3: Mass of a Loaf of Bread
A loaf of bread should weigh 800g. The masses of 10 different loaves of bread were measured
at a store for 1 week. The data is shown in Table 15.3.
”The Trade Metrology Act requires that if a loaf of bread is not labelled, it must weigh 800g,
with the leeway of five percent under or 10 percent over. However, an average of 10 loaves must
be an exact match to the mass stipulated. - Sunday Tribune of 10 October 2004 on page 10”
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
802.39 787.78 815.74 807.41 801.48 786.59 799.01
796.76 798.93 809.68 798.72 818.26 789.08 805.99
802.50 793.63 785.37 809.30 787.65 801.45 799.35
819.59 812.62 809.05 791.13 805.28 817.76 801.01
801.21 795.86 795.21 820.39 806.64 819.54 796.67
789.00 796.33 787.87 799.84 789.45 802.05 802.20
788.99 797.72 776.71 790.69 803.16 801.24 807.32
808.80 780.38 812.61 801.82 784.68 792.19 809.80
802.37 790.83 792.43 789.24 815.63 799.35 791.23
796.20 817.57 799.05 825.96 807.89 806.65 780.23
Table 15.3: Masses (in g) of 10 different loaves of bread, from the same manufacturer, measured
at the same store over a period of 1 week.
15.3.4 Data Set 4: Global Temperature
The mean global temperature from 1861 to 1996 is listed in Table 15.4. The data, obtained from
http://www.cgd.ucar.edu/stats/Data/Climate/, was converted to mean temperature in
degrees Celsius.
http://lib.stat.cmu.edu/DASL/
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CHAPTER 15. STATISTICS - GRADE 10 15.4
Year Temperature Year Temperature Year Temperature Year Temperature
1861 12.66 1901 12.871 1941 13.152 1981 13.228
1862 12.58 1902 12.726 1942 13.147 1982 13.145
1863 12.799 1903 12.647 1943 13.156 1983 13.332
1864 12.619 1904 12.601 1944 13.31 1984 13.107
1865 12.825 1905 12.719 1945 13.153 1985 13.09
1866 12.881 1906 12.79 1946 13.015 1986 13.183
1867 12.781 1907 12.594 1947 13.006 1987 13.323
1868 12.853 1908 12.575 1948 13.015 1988 13.34
1869 12.787 1909 12.596 1949 13.005 1989 13.269
1870 12.752 1910 12.635 1950 12.898 1990 13.437
1871 12.733 1911 12.611 1951 13.044 1991 13.385
1872 12.857 1912 12.678 1952 13.113 1992 13.237
1873 12.802 1913 12.671 1953 13.192 1993 13.28
1874 12.68 1914 12.85 1954 12.944 1994 13.355
1875 12.669 1915 12.962 1955 12.935 1995 13.483
1876 12.687 1916 12.727 1956 12.836 1996 13.314
1877 12.957 1917 12.584 1957 13.139
1878 13.092 1918 12.7 1958 13.208
1879 12.796 1919 12.792 1959 13.133
1880 12.811 1920 12.857 1960 13.094
1881 12.845 1921 12.902 1961 13.124
1882 12.864 1922 12.787 1962 13.129
1883 12.783 1923 12.821 1963 13.16
1884 12.73 1924 12.764 1964 12.868
1885 12.754 1925 12.868 1965 12.935
1886 12.826 1926 13.014 1966 13.035
1887 12.723 1927 12.904 1967 13.031
1888 12.783 1928 12.871 1968 13.004
1889 12.922 1929 12.718 1969 13.117
1890 12.703 1930 12.964 1970 13.064
1891 12.767 1931 13.041 1971 12.903
1892 12.671 1932 12.992 1972 13.031
1893 12.631 1933 12.857 1973 13.175
1894 12.709 1934 12.982 1974 12.912
1895 12.728 1935 12.943 1975 12.975
1896 12.93 1936 12.993 1976 12.869
1897 12.936 1937 13.092 1977 13.148
1898 12.759 1938 13.187 1978 13.057
1899 12.874 1939 13.111 1979 13.154
1900 12.959 1940 13.055 1980 13.195
Table 15.4: Global temperature changes over the past x years. Is there a warming of the planet?
15.3.5 Data Set 5: Price of Petrol
The price of petrol in South Africa from August 1998 to July 2000 is shown in Table 15.5.
15.4 Grouping Data
One of the first steps to processing a large set of raw data is to arrange the data values together
into a smaller number of groups, and then count how many of each data value there are in each
group. The groups are usually based on some sort of interval of data values, so data values that
fall into a specific interval, would be grouped together. The grouped data is often presented
graphically or in a frequency table. (Frequency means “how many times”)
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15.4 CHAPTER 15. STATISTICS - GRADE 10
Table 15.5: Petrol prices
Date Price (R/l)
August 1998 R 2.37
September 1998 R 2.38
October 1998 R 2.35
November 1998 R 2.29
December 1998 R 2.31
January 1999 R 2.25
February 1999 R 2.22
March 1999 R 2.25
April 1999 R 2.31
May 1999 R 2.49
June 1999 R 2.61
July 1999 R 2.61
August 1999 R 2.62
September 1999 R 2.75
October 1999 R 2.81
November 1999 R 2.86
December 1999 R 2.85
January 2000 R 2.86
February 2000 R 2.81
March 2000 R 2.89
April 2000 R 3.03
May 2000 R 3.18
June 2000 R 3.22
July 2000 R 3.36
Worked Example 61: Grouping Data
Question: Group the elements of Data Set 1 to determine how many times the coin
landed heads-up and how many times the coin landed tails-up.
Answer
Step 1 : Identify the groups
There are two unique data values: H and T. Therefore there are two groups, one for
the H-data values and one for the T-data values.
Step 2 : Count how many data values fall into each group.
Data Value Frequency
H 44
T 56
Step 3 : Check that the total of the frequency column is equal to the total
number of data values.
There are 100 data values and the total of the frequency column is 44+56=100.
15.4.1 Exercises - Grouping Data
1. The height of 30 learners are given below. Fill in the grouped data below. (Tally is a
convenient way to count in 5’s. We use llll to indicate 5.)
142 163 169 132 139 140 152 168 139 150
161 132 162 172 146 152 150 132 157 133
141 170 156 155 169 138 142 160 164 168
216
CHAPTER 15. STATISTICS - GRADE 10 15.5
Group Tally Frequency
130 ≤ h < 140
140 ≤ h < 150
150 ≤ h < 160
160 ≤ h < 170
170 ≤ h < 180
2. An experiment was conducted in class and 50 learners were asked to guess the number of
sweets in a jar. The following guesses were recorded.
56 49 40 11 33 33 37 29 30 59
21 16 38 44 38 52 22 24 30 34
42 15 48 33 51 44 33 17 19 44
47 23 27 47 13 25 53 57 28 23
36 35 40 23 45 39 32 58 22 40
A Draw up a grouped frequency table using intervals 11-20, 21-30, 31-40, etc.
15.5 Graphical Representation of Data
Once the data has been collected, it must be organised in a manner that allows for the information
to be extracted most efficiently. One method of organisation is to display the data in the form
of graphs. Functions and graphs have been studied in Chapter ??, and similar techniques will
be used here. However, instead of drawing graphs from equations as was done in Chapter ??,
bar graphs, histograms and pie charts will be drawn directly from the data.
15.5.1 Bar and Compound Bar Graphs
A bar chart is used to present data where each observation falls into a specific category and where
the categories are unrelated. The frequencies (or percentages) are listed along the y-axis and
the categories are listed along the x-axis. The heights of the bars correspond to the frequencies.
The bars are of equal width and should not touch neighbouring bars.
A compound bar chart (also called component bar chart) is a variant: here the bars are cut
into various components depending on what is being shown. If percentages are used for various
components of a compound bar, then the total bar height must be 100%. The compound bar
chart is a little more complex but if this method is used sensibly, a lot of information can be
quickly shown in an attractive fashion.
Examples of a bar and a compound bar graph, for Data Set 1 Table 15.1, are shown in Figure 15.2.
According to the frequency table for Data Set 1, the coin landed heads-up 44 times and tails-up
56 times.
15.5.2 Histograms and Frequency Polygons
It is often useful to look at the frequency with which certain values fall in pre-set groups or
classes of specified sizes. The choice of the groups should be such that they help highlight
features in the data. If these grouped values are plotted in a manner similar to a bar graph, then
the resulting graph is known as a histogram. Examples of histograms are shown in Figure 15.3
for Data Set 2, with group sizes of 1 and 2.
Groups 0 < n ≤1 1 < n ≤2 2 < n ≤3 3 < n ≤4 4 < n ≤5 5 < n ≤6
Frequency 30 32 35 34 37 32
Table 15.6: Frequency table for Data Set 2, with a group size of 1.
The same data used to plot a histogram are used to plot a frequency polygon, except the pair of
data values are plotted as a point and the points are joined with straight lines. The frequency
polygons for the histograms in Figure 15.3 are shown in Figure 15.4.
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15.5 CHAPTER 15. STATISTICS - GRADE 10
0
10
20
30
40
50
60
70
80
90
100
Heads Tails
Relative Frequency (%)
Bar Graph
0
10
20
30
40
50
60
70
80
90
100
Total Relative Frequency (%)
Compound Bar Graph Heads Tails
Figure 15.2: Examples of a bar graph (left) and compound bar graph (right) for Data Set 1.
The compound bar graph extends from 0% to 100%.
Groups 0 < n ≤2 2 < n ≤4 4 < n ≤6
Frequency 62 69 69
Table 15.7: Frequency table for Data Set 2, with a group size of 2.
0 1 2 3 4 5 6
0
10
20
30
40
50
60
Frequency
Histogram - group size=1
0 2 4 6
0
10
20
30
40
50
60
Frequency
Histogram - group size=2
Figure 15.3: Examples of histograms for Data Set 2, with a group size = 1 (left) and a group
size = 2 (right). The scales on the y-axis for each graph are the same, and the values in the
graph on the right are higher than the values of the graph on the left.
0 1 2 3 4 5 6
0
10
20
30
40
50
60
Frequency
Histogram - group size=1
b
b
b b
b
b
0 2 4 6
0
10
20
30
40
50
60
Frequency
Histogram - group size=2
b
b
b b
b
Figure 15.4: Examples of histograms for Data Set 2, with a group size = 1 (left) and a group
size = 2 (right). The scales on the y-axis for each graph are the same, and the values in the
graph on the right are higher than the values of the graph on the left.
Unlike histograms, many frequency polygons can be plotted together to compare several fre-
218
CHAPTER 15. STATISTICS - GRADE 10 15.5
quency distributions, provided that the data has been grouped in the same way.
15.5.3 Pie Charts
A pie chart is a graph that is used to show what categories make up a specific section of the
data, and what the contribution each category makes to the entire set of data. A pie chart is
based on a circle, and each category is represented as a wedge of the circle or alternatively as
a slice of the pie. The area of each wedge is proportional to the ratio of that specific category
to the total number of data values in the data set. The wedges are usually shown in different
colours to make the distinction between the different categories easier.
Heads
Tails
Figure 15.5: Example of a pie chart for Data Set 1. Pie charts show what contribution each
group makes to the total data set.
Method: Drawing a pie-chart
1. Draw a circle that represents the entire data set.
2. Calculate what proportion of 360◦
each category corresponds to according to
Angular Size =
Frequency
Total
× 360◦
3. Draw a wedge corresponding to the angular contribution.
4. Check that the total degrees for the different wedges adds up to close to 360◦
.
Worked Example 62: Pie Chart
Question: Draw a pie chart for Data Set 2, showing the relative proportions of each
data value to the total.
Answer
Step 1 : Determine the frequency table for Data Set 2.
Total
Data Value 1 2 3 4 5 6 –
Frequency 30 32 35 34 37 32 200
Step 2 : Calculate the angular size of the wedge for each data value
Data Value Angular Size of Wedge
1
Frequency
Total × 360◦ =
30
200 × 360 = 54◦
2
Frequency
Total × 360◦ =
32
200 × 360 = 57,6
◦
3
Frequency
Total × 360◦ =
35
200 × 360 = 63◦
4
Frequency
Total × 360◦ = 34
200 × 360 = 61,2
◦
5
Frequency
Total × 360◦ =
37
200 × 360 = 66,6
◦
6
Frequency
Total × 360◦ =
32
200 × 360 = 57,6
◦
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15.5 CHAPTER 15. STATISTICS - GRADE 10
Step 3 : Draw the pie, with the size of each wedge as calculated above.
Pie Chart for Data Set 2
1
2
3
4
5
6
Note that the total angular size of the wedges may not add up to exactly 360◦ because of
rounding.
15.5.4 Line and Broken Line Graphs
All graphs that have been studied until this point (bar, compound bar, histogram, frequency
polygon and pie) are drawn from grouped data. The graphs that will be studied in this section
are drawn from the ungrouped or raw data.
Line and broken line graphs are plots of a dependent variable as a function of an independent
variable, e.g. the average global temperature as a function of time, or the average rainfall in a
country as a function of season.
Usually a line graph is plotted after a table has been provided showing the relationship between
the two variables in the form of pairs. Just as in (x,y) graphs, each of the pairs results in a
specific point on the graph, and being a LINE graph these points are connected to one another
by a LINE.
Many other line graphs exist; they all CONNECT the points by LINES, not necessarily straight
lines. Sometimes polynomials, for example, are used to describe approximately the basic relationship
between the given pairs of variables, and between these points.
0
1
2
3
b b b
b b
b b b
b
b
b b b
b
b
b b b
b
b
b
b
b
b
Petrol Price (R/l)
August 1998
October 1998
December 1998
February 1999
April 1999
June 1999
August 1999
October 1999
December 1999
February 2000
April 2000
June 2000
Figure 15.6: Example of a line graph for Data Set 5.
Worked Example 63: Line Graphs
Question: Clawde the cat is overweight and her owners have decided to put her
on a restricted eating plan. Her mass is measured once a month and is tabulated
220
CHAPTER 15. STATISTICS - GRADE 10 15.5
below. Draw a line graph of the data to determine whether the restricted eating
plan is working.
Month Mass (kg)
March 4,53
April 4,56
May 4,51
June 4,41
July 4,41
August 4,36
September 4,43
October 4,37
Answer
Step 1 : Determine what is required
We are required to plot a line graph to determine whether the restricted eating plan
is helping Clawde the cat lose weight. We are given all the information that we need
to plot the graph.
Step 2 : Plot the graph
0
1
2
3
4
b b
b
b b
b
b
b
Mass (kg)
March
April
May
June
July
August
September
October
Step 3 : Analyse Graph
There is a slight decrease of mass from March to October, so the restricted eating
plan is working, but very slowly.
15.5.5 Exercises - Graphical Representation of Data
1. Represent the following information on a pie chart.
Walk 15
Cycle 24
Train 18
Bus 8
Car 35
Total 100
2. Represent the following information using a broken line graph.
Time 07h00 08h00 09h00 10h00 11h00 12h00
Temp (◦C) 16 16,5 17 19 20 24
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15.6 CHAPTER 15. STATISTICS - GRADE 10
3. Represent the following information on a histogram. Using a coloured pen, draw a frequency
polygon on this histogram.
Time in seconds Frequency
16 - 25 5
26 - 35 10
36 - 45 26
46 - 55 30
56 - 65 15
66 - 75 12
76 - 85 10
4. The maths marks of a class of 30 learners are given below, represent this information using
a suitable graph.
82 75 66 54 79 78 29 55 68 91
43 48 90 61 45 60 82 63 72 53
51 32 62 42 49 62 81 49 61 60
5. Use a compound bar graph to illustrate the following information
Year 2003 2004 2005 2006 2007
Girls 18 15 13 12 15
Boys 15 11 18 16 10
15.6 Summarising Data
If the data set is very large, it is useful to be able to summarise the data set by calculating a
few quantities that give information about how the data values are spread and about the central
values in the data set.
15.6.1 Measures of Central Tendency
An average is simply a number that is representative of a set of data. Specifically, it is a measure
of central tendency which means that it gives an indication of the main tendency of the set of
data. Averages are useful for comparing data, especially when sets of different sizes are being
compared.
There are several types of average. Perhaps the simplest and most commonly used average is
the mean of a set of data. Other common types of average are the median and the mode.
Mean
The mean, (also known as arithmetic mean), is simply the arithmetic average of a group of
numbers (or data set) and is shown using the bar symbol¯. So the mean of the variable x is x¯
pronounced ”x-bar”. The mean of a set of values is calculated by adding up all the values in
the set and dividing by the number of items in that set. The mean is calculated from the raw,
ungrouped data.
Definition: Mean
The mean of a data set, x, denoted by x¯, is the average of the data values, and is calculated
as:
x¯ =
sum of all values
number of values (15.1)
Method: Calculating the mean
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CHAPTER 15. STATISTICS - GRADE 10 15.6
1. Find the total of the data values in the data set.
2. Count how many data values there are in the data set.
3. Divide the total by the number of data values.
Worked Example 64: Mean
Question: What is the mean of x = {10,20,30,40,50}?
Answer
Step 1 : Find the total of the data values
10 + 20 + 30 + 40 + 50 = 150
Step 2 : Count the number of data values in the data set
There are 5 values in the data set.
Step 3 : Divide the total by the number of data values.
150 ÷ 5 = 30
Step 4 : Answer
∴ the mean of the data set x = {10,20,30,40,50} is 30.
Median
Definition: Median
The median of a set of data is the data value in the central position, when the data set has
been arranged from highest to lowest or from lowest to highest. There are an equal number
of data values on either side of the median value.
The median is calculated from the raw, ungrouped data, as follows.
Method: Calculating the median
1. Order the data from smallest to largest or from largest to smallest.
2. Count how many data values there are in the data set.
3. Find the data value in the central position of the set.
Worked Example 65: Median
Question: What is the median of {10,14,86,2,68,99,1}?
Answer
Step 1 : Order the data set from lowest to highest
1,2,10,14,68,85,99
Step 2 : Count the number of data values in the data set
There are 7 points in the data set.
Step 3 : Find the central position of the data set
The central position of the data set is 4.
Step 4 : Find the data value in the central position of the ordered data set.
14 is in the central position of the data set.
Step 5 : Answer
∴ 14 is the median of the data set {1,2,10,14,68,85,99}.
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15.6 CHAPTER 15. STATISTICS - GRADE 10
This example has highlighted a potential problem with determining the median. It is very easy
to determine the median of a data set with an odd number of data values, but what happens
when there is an even number of data values in the data set?
When there is an even number of data values, the median is the mean of the two middle points.
Important: Finding the Central Position of a Data Set
An easy way to determine the central position or positions for any ordered data set is to take
the total number of data values, add 1, and then divide by 2. If the number you get is a whole
number, then that is the central position. If the number you get is a fraction, take the two whole
numbers on either side of the fraction, as the positions of the data values that must be averaged
to obtain the median.
Worked Example 66: Median
Question: What is the median of {11,10,14,86,2,68,99,1}?
Answer
Step 1 : Order the data set from lowest to highest
1,2,10,11,14,68,85,99
Step 2 : Count the number of data values in the data set
There are 8 points in the data set.
Step 3 : Find the central position of the data set
The central position of the data set is between positions 4 and 5.
Step 4 : Find the data values around the central position of the ordered data
set.
11 is in position 4 and 14 is in position 5.
Step 5 : Answer
∴ the median of the data set {1,2,10,11,14,68,85,99} is
(11 + 14) ÷ 2 = 12,5
Mode
Definition: Mode
The mode is the data value that occurs most often, i.e. it is the most frequent value or
most common value in a set.
Method: Calculating the mode Count how many times each data value occurs. The mode is
the data value that occurs the most.
The mode is calculated from grouped data, or single data items.
Worked Example 67: Mode
Question: Find the mode of the data set x = {1, 2, 3, 4, 4, 4, 5, 6, 7, 8, 8, 9,10,10}
Answer
Step 1 : Count how many times each data value occurs.
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CHAPTER 15. STATISTICS - GRADE 10 15.6
data value frequency data value frequency
1 1 6 1
2 1 7 1
3 1 8 2
4 3 9 1
5 1 10 2
Step 2 : Find the data value that occurs most often.
4 occurs most often.
Step 3 : Answer
The mode of the data set x = {1, 2, 3, 4, 4, 4, 5, 6, 7, 8, 8, 9,10,10} is 4.
A data set can have more than one mode. For example, both 2 and 3 are modes in the set 1, 2,
2, 3, 3. If all points in a data set occur with equal frequency, it is equally accurate to describe
the data set as having many modes or no mode.
15.6.2 Measures of Dispersion
The mean, median and mode are measures of central tendency, i.e. they provide information on
the central data values in a set. When describing data it is sometimes useful (and in some cases
necessary) to determine the spread of a distribution. Measures of dispersion provide information
on how the data values in a set are distributed around the mean value. Some measures of
dispersion are range, percentiles and quartiles.
Range
Definition: Range
The range of a data set is the difference between the lowest value and the highest value in
the set.
Method: Calculating the range
1. Find the highest value in the data set.
2. Find the lowest value in the data set.
3. Subtract the lowest value from the highest value. The difference is the range.
Worked Example 68: Range
Question: Find the range of the data set x = {1, 2, 3, 4, 4, 4, 5, 6, 7, 8, 8, 9,10,10}
Answer
Step 1 : Find the highest and lowest values.
10 is the highest value and 1 is the lowest value.
Step 2 : Subtract the lowest value from the highest value to calculate the
range.
10 − 1 = 9
Step 3 : Answer
For the data set x = {1, 2, 3, 4, 4, 4, 5, 6, 7, 8, 8, 9,10,10}, the range is 9.
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15.6 CHAPTER 15. STATISTICS - GRADE 10
Quartiles
Definition: Quartiles
Quartiles are the three data values that divide an ordered data set into four groups containing
equal numbers of data values. The median is the second quartile.
The quartiles of a data set are formed by the two boundaries on either side of the median, which
divide the set into four equal sections. The lowest 25% of the data being found below the first
quartile value, also called the lower quartile. The median, or second quartile divides the set into
two equal sections. The lowest 75% of the data set should be found below the third quartile,
also called the upper quartile. For example:
Data
Items
22 24 48 51 60 72 73 75 80 88 90
↓ ↓ ↓
Lower
quartile
Median Upper
quartile
(Q1) (Q2) (Q3)
Method: Calculating the quartiles
1. Order the data from smallest to largest or from largest to smallest.
2. Count how many data values there are in the data set.
3. Divide the number of data values by 4. The result is the number of data values per group.
4. Determine the data values corresponding to the first, second and third quartiles using the
number of data values per quartile.
Worked Example 69: Quartiles
Question: What are the quartiles of {3,5,1,8,9,12,25,28,24,30,41,50}?
Answer
Step 1 : Order the data set from lowest to highest
{1, 3, 5, 8, 9, 12, 24, 25, 28, 30, 41, 50}
Step 2 : Count the number of data values in the data set
There are 12 values in the data set.
Step 3 : Divide the number of data values by 4 to find the number of data
values per quartile.
12 ÷ 4 = 3
Step 4 : Find the data values corresponding to the quartiles.
1 3 5 k 8 9 12 k 24 25 28 k 30 41 50
Q1 Q2 Q3
The first quartile occurs between data position 3 and 4 and is the average of data
values 5 and 8. The second quartile occurs between positions 6 and 7 and is the
average of data values 12 and 24. The third quartile occurs between positions 9 and
10 and is the average of data values 28 and 30.
Step 5 : Answer
The first quartile = 6,5. (Q1)
The second quartile = 18. (Q2)
The third quartile = 29. (Q3)
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CHAPTER 15. STATISTICS - GRADE 10 15.6
Inter-quartile Range
Definition: Inter-quartile Range
The inter quartile range is a measure which provides information about the spread of a data
set, and is calculated by subtracting the first quartile from the third quartile, giving the
range of the middle half of the data set, trimming off the lowest and highest quarters, i.e.
Q3 − Q1.
The semi-interquartile range is half the interquartile range, i.e. Q3−Q1
2
Worked Example 70: Medians, Quartiles and the Interquartile Range
Question: A class of 12 students writes a test and the results are as follows: 20, 39,
40, 43, 43, 46, 53, 58, 63, 70, 75, 91. Find the range, quartiles and the Interquartile
Range.
Answer
Step 1 :
20 39 40 k 43 43 46 k 53 58 63 k 70 75 91
Q1 M Q3
Step 2 : The Range
The range = 91 - 20 = 71. This tells us that the marks are quite widely spread.
Step 3 : The median lies between the 6th and 7th mark
i.e. M = 46+53
2 = 99
2 = 49,5
Step 4 : The lower quartile lies between the 3rd and 4th mark
i.e. Q1 =
40+43
2 =
83
2 = 41,5
Step 5 : The upper quartile lies between the 9th and 10th mark
i.e. Q3 =
63+70
2 =
133
2 = 66,5
Step 6 : Analysing the quartiles
The quartiles are 41,5, 49,5 and 66,5. These quartiles tell us that 25% of the marks
are less than 41,5; 50% of the marks are less than 49,5 and 75% of the marks are
less than 66,5. They also tell us that 50% of the marks lie between 41,5 and 66,5.
Step 7 : The Interquartile Range
The Interquartile Range = 66,5 - 41,5 = 25. This tells us that the width of the
middle 50% of the data values is 25.
Step 8 : The Semi-interquatile Range
The Semi-interquartile Range = 25
2 = 12,5
Percentiles
Definition: Percentiles
Percentiles are the 99 data values that divide a data set into 100 groups.
The calculation of percentiles is identical to the calculation of quartiles, except the aim is to
divide the data values into 100 groups instead of the 4 groups required by quartiles.
Method: Calculating the percentiles
1. Order the data from smallest to largest or from largest to smallest.
2. Count how many data values there are in the data set.
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15.6 CHAPTER 15. STATISTICS - GRADE 10
3. Divide the number of data values by 100. The result is the number of data values per
group.
4. Determine the data values corresponding to the first, second and third quartiles using the
number of data values per quartile.
15.6.3 Exercises - Summarising Data
1. Three sets of data are given:
A Data set 1: 9 12 12 14 16 22 24
B Data set 2: 7 7 8 11 13 15 16 16
C Data set 3: 11 15 16 17 19 19 22 24 27
For each one find:
i. the range
ii. the lower quartile
iii. the interquartile range
iv. the semi-interquartile range
v. the median
vi. the upper quartile
2. There is 1 sweet in one jar, and 3 in the second jar. The mean number of sweets in the
first two jars is 2.
A If the mean number in the first three jars is 3, how many are there in the third jar?
B If the mean number in the first four jars is 4, how many are there in the fourth jar?
C If the mean number in the first n jars is n, how many are there in the n jar?
3. Find a set of five ages for which the mean age is 5, the modal age is 2 and the median
age is 3 years.
4. Four friends each have some marbles. They work out that the mean number of marbles
they have is 10. One of them leaves. She has 4 marbles. How many marbles do the
remaining friends have together?
Worked Example 71: Mean, Median and Mode for Grouped Data
Question:
Consider the following grouped data and calculate the mean, the modal group and
the median group.
Mass (kg) Frequency
41 - 45 7
46 - 50 10
51 - 55 15
56 - 60 12
61 - 65 6
Total = 50
Answer
Step 1 : Calculating the mean
To calculate the mean we need to add up all the masses and divide by 50. We do not
know actual masses, so we approximate by choosing the midpoint of each group.
We then multiply those midpoint numbers by the frequency. Then we add these
numbers together to find the approximate total of the masses. This is show in the
table below.
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CHAPTER 15. STATISTICS - GRADE 10 15.7
Mass (kg) Midpoint Frequency Midpt × Freq
41 - 45 (41+45)/2 = 43 7 43 × 7 = 301
46 - 50 48 10 480
51 - 55 53 15 795
56 - 60 58 12 696
61 - 65 63 6 378
Total = 50 Total = 2650
Step 2 : Answer
The mean = 2650
50 = 53.
The modal group is the group 51 - 53 because it has the highest frequency.
The median group is the group 51 - 53, since the 25th and 26th terms are contained
within this group.
Exercise: More mean, modal and median group exercises.
In each data set given, find the mean, the modal group and the median group.
1. Times recorded when learners played a game.
Time in seconds Frequency
36 - 45 5
46 - 55 11
56 - 65 15
66 - 75 26
76 - 85 19
86 - 95 13
96 - 105 6
2. The following data were collected from a group of learners.
Mass in kilograms Frequency
41 - 45 3
46 - 50 5
51 - 55 8
56 - 60 12
61 - 65 14
66 - 70 9
71 - 75 7
76 - 80 2
15.7 Misuse of Statistics
In many cases groups can gain an advantage by misleading people with the misuse of statistics.
Common techniques used include:
• Three dimensional graphs.
• Axes that do not start at zero.
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15.7 CHAPTER 15. STATISTICS - GRADE 10
• Axes without scales.
• Graphic images that convey a negative or positive mood.
• Assumption that a correlation shows a necessary causality.
• Using statistics that are not truly representative of the entire population.
• Using misconceptions of mathematical concepts
For example, the following pairs of graphs show identical information but look very different.
Explain why

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