Data
There are two types of data:
Discreet data: Data that can only take certain values (whole numbers), like
the number of students in a class (we cannot have half a student). Discrete
data is counted.
Continuous data: Data that can take any value (decimals), like height,
weight, time length etc. Continuous data is measured.
Graphed data
The data provided is that of the world spice trade which is estimated to be in the
following tons per year:
Pepper = 132000, Capsicum = 88000, Seed spices = 60000, Tree spices =
56000, Turmeric = 32000, Ginger = 24000, Cardamom = 16000, Vanilla = 8000
Bar Graph

Is a graphical display of data using bars of different heights but equal widths
to represent different groups or discreet data categories.

It is a good way to show relative sizes of many different things. For example
we can see which types of spices are traded most and which are traded the
least, at a glance.
Annual world spice trade
Amount of spice traded in tons
140000
120000
100000
80000
60000
40000
20000
0
Pepper
Capsicum Seed Spices Tree Spices
Turmeric
Spice names
Bar Graphs must have:





A Title.
Axis labels (with units like kg, mm, etc. if
necessary).
A scale of equal intervals on the vertical axis.
Spaces between the bars
Names of the categories on the horizontal axis.
Ginger
Cardamom
Vanilla
Histogram

It is similar to a Bar graph, but a histogram groups numbers into ranges not
categories.

Is used for continuous data.

Shows the frequency that an event occurs.
Annual world spice trade
Number of Spices in ton range
4
3
2
1
0
30000
60000
90000
120000
Tons
Differences between a bar graph and a histogram:
150000
Pie Chart

Also a good way to show relative sizes.

Data is in percentages because a full circle is 100% of the measured data.

To convert data to % take the data you want, divided by the total data,
multiplied by 100.
Data
x 100 = %
Total

Pepper = 132000 out of the total data which is 400000.
132000
x 100 = 33%
400000
Annual world spice trade
6%
Pepper
Capsicum
4%2%
8%
33%
Seed spices
Tree spices
Turmeric
14%
Ginger
Cardamom
Vanilla

15%
22%
The labels can be inside the pie chart if there is space, otherwise make a key
indicating which slice which is, as indicated in the picture above.
Line Graph

Shows the relationship or connection of data.

Is useful for showing trends in data and for making predictions.
Ice cream sales in 2013
Number of Ice creams sold
12000
10000
8000
6000
4000
2000
0
Summer
Autumn
Winter
Spring
Seasons
Mean, Mode and Median
Dataset: 8; 8; 4; 8; 6; 2
Mean: The mean is the average of the numbers. It is easy to
calculate: add up all the numbers, then divide by how many numbers
there are. Using the above dataset: (8 + 8 + 4 + 8 + 6 + 2) ÷ 6 = 6
Mode: The value that appears the most in the dataset. The mode will be
the number 8 in the above dataset.
Median: The value that is exactly in the middle of the dataset when the
dataset is in ascending order (from small to large). The median of the
above set: 2; 4; 6; 8; 8; 8, both 6 and 8 are in the middle of the dataset
so we take their average: (6+8) ÷ 2 = 7. Therefore 7 is the median.
If our dataset was 2; 4; 6; 8; 8, our median would have been 6.
Graph Activity
Ask every student in class (including yourself) whether their school lunch
matches the following criteria and complete the Tally table:
Name of category
Number of students
Frequency
Candy
Chocolate
Bread
Juice
Water
Milk
Fruit
Chips
Salad
Other
Total
Complete the bar graph based on your Tally table data:
School lunch items of Grade 6 students
20
18
Number of students
16
14
12
10
8
6
4
2
0
Candy Chocolate Bread
Juice
Water
Milk
School lunch items
Fruit
Chips
Salad
Other
Convert the above data into percentages:
Name of category
Frequency
Frequency ÷ Total x 100 = %
Candy
Chocolate
Bread
Juice
Water
Milk
Fruit
Chips
Salad
Other
Total
Based on the above information from the bar graph and tally tables:
1. Which lunch item was the most frequent?
2. Why do you think this item was most frequent?
3. Which was the least frequent?
4. Why do you think it was the least frequent?
5. Does the composition of lunch items conform to a healthy diet?
Explain.
6. What alternative items could students rather bring to school to
improve the health quality of their lunch?
7. Is the data discreet or continuous? Explain.
8. What other graphs could have been used to represent this data?