 Do it before the experiment
- don’t wait until you start the experiment to
figure out how to record your data, do it as part
of the plan before you start
 Where do the variables go?
-
independent on the LEFT
-
dependent on the RIGHT
 No units in the tables
- DO NOT include labels in the table, only
include them in the title boxes!
When you are recording data it is important to remember to be specific, and record
everything! It is better to record too much, and then not need to use the data, than
to not record enough information!
QUANTITATIVE means a measured quantity.
 Deals with numbers.
 Data which can be measured.
 Length, height, area, volume, weight, speed, time, temperature, humidity, sound
levels, cost, members, ages, etc.
 Quantitative → Quantity
QUANTITATIVE means describing a
“quality” such as color, smell, shape, etc
 Deals with descriptions.
 Data can be observed but not measured.
 Colors, textures, smells, tastes,
appearance, beauty, etc.
 Qualitative → Quality
Continuous data
 data that could be any number on a continuum
 changes over time are usually continuous (imagine the slope of a hill)
Discreet data data that has only certain options (imagine a set of steps)
 number of people, shoe size, type of exercise are all types of discreet data
 whenever you create groups you create discreet data, i.e. - 0-5minutes, 6-
10minutes, 11-15minutes are discreet
 groups even though time is usually continuous
 Organise raw data in a table.
 One example…
Independent
variable
Dependent variable
trials
average
 Example:
 Effect of the type of fertiliser in plant growth.
Compost or
fertiliser
Height (cm)
Trial 1
Trial 2
Trial 3
Trial 4
Trial 5
Compost
8
6
8
7
9
Fertiliser
5
7
5
3
5
control
4
3
5
8
0
 After you have completed your experiment you will need to process your raw data.
Do you need to find the average? Maybe a percentage, total, orvdifference is best?
It will depend on your data!
 Explain in words
include a few written sentences to explain why you chose the formula you did don’t
just say, “because I have to process my data”!
After you have processed your data, you need to present it in a second table. This
will be the table that you use to make your graph, and your conclusion.
New table
- create a second table after your data processing section
Smaller table
- yes, it is going to be smaller than the raw data table
Variables
- independent variable in the left column
- dependent variable in the right column(s)
 Example:
 Effect of the type of fertiliser in plant growth.
Compost or
fertiliser
Average
Height
(cm)
Compost
8
Fertiliser
5
control
4
 We use data collected in our experiments to make graphs.
 To understand data better.
 To identify patterns in data
 A correct collection of data is essential to make correct graphs.
 Sometimes data need to be manipulated. Average…
 Use your processed data to create a graph that shows the results of your
experiment. It should be neat, including proper titles, and must be the proper type
of graph!
Type of graph depends on the type of data your independent variable produces
 Line graphs or scatter plots : continous data.
 Bar graphs: discreet data, compare groups.
 Pie charts: use for percentages or parts of a whole.
 Used when data produced by the independent variable is discreet.
 When one of the data variables is “time”, it goes on the x axis. Generally
independent variable goes in the x axis.
 Many line graphs show changes over time or the change of one variable
(responding variable) due to the change of another variable (manipulated
variable).
Growth of Plant A Over Time
6
Plant Height (cm)
5
4
3
2
1
0
5
10
15
20
25
30
35
Time (Days)
40
45
50
55
Chocolate MIlk Sold
120
Amount Sold
100
80
60
40
20
0
Monday
Tuesday
Wednesday
Day
Chocolate
Thursday
Friday
 Used for discreet data.
 Scatterplots contain a line of best fit, which is a straight line drawn through the
center of the data points that best represents the trend of the data. Scatterplots
provide a visual representation of the correlation, or relationship between the two
variables.
Types of Correlation
 Positive correlation: Both variables move in the same direction. In other words, as
one variable increases, the other variable also increases. As one variable
decreases, the other variable also decreases.
 i.e., years of education and yearly salary are positively correlated.
 Negative correlation: The variables move in opposite directions. As one variable
increases, the other variable decreases. As one variable decreases, the other
variable increases.
 i.e., hours spent sleeping and hours spent awake are negatively correlated.
No Correlations
It means that there is no apparent relationship
between the two variables. For example, there is no
correlation between shoe size and salary. This means
that high scores on shoe size are just as likely to occur
with high scores on salary as they are with low scores
on salary.
 Use it when a set of measurements can be split into discrete and comparable
groups
 To show the relative change between these groups
Average Plant Growth over 50 Days
Average Growth in Centimeters
6
5
Plant A
(Control)
Plant B (F ertilizer
Added)
4
3
Plant C
(Compost
Added)
2
1
0
Plant A
Plant B
Plant C
Chocolate Milk Sold
120
112
Amount Sold
100
80
76
72
60
53
40
33
20
0
Monday
Monday
Wednesday
Friday
Tuesday
Tuesday
Thursday
Wednesday
Day
Thursday
Friday
 When showing parts of a whole..i.e. percentages
Chocolate Milk Sold
Monday
Tuesday
Wednesday
Thursday
Friday
Chocolate Milk Sold
120
112
Amount Sold
100
80
76
72
60
53
40
33
20
0
Monday
Monday
Wednesday
Friday
Tuesday
Tuesday
Thursday
Wednesday
Thursday
Friday
Day
On what day did they sell the most chocolate milk?
a. Tuesday b. Friday c. Wednesday
Chocolate
Monday
Tuesday
Wednesday
Thursday
Friday
On what day was the least amount of chocolate milk sold?
a. Monday b. Tuesday c. Thursday
Chocolate
120
Amount Sold
100
80
60
40
20
0
Monday
Tuesday
Wednesday
Thursday
Friday
Day
Chocolate
On what day did they have a drop in chocolate milk sales?
a. Thursday b. Tuesday c. Monday
Don’t forget to include...
 title
 x and y axis
 axis titles including units
 proper scale of numbers
X-axis horizontal
Y-axis vertical
Y Axis
(label with the
name of the
variable you are
going to represent
here, and the
X Axis
(label with the
name of the
variable you are
going to represent
here, and the
unit.)
Teachers’s Favorite
Singer
T - Title
Teachers’s Favorite
Singer
T - Title
A - Axis
Teachers’s Favorite
Singer
Decide on an
appropriate scale for
each axis.
Choose a scale that lets
you make the graph as
large as possible for
your paper and data
T - Title
A – Axis
S – Scale
Favorite
Singer
Number of
Teachers
Toby Keith
22
Madonna
15
Elvis
11
Sting
5
Sinatra
2
 Scale is determined by your
highest & lowest number.
 In this case your scale would
be from 2 – 22.
Favorite
Singer
Number of
Teachers
 The interval is decided
Toby Keith
22
 In this case your scale
Madonna
15
Elvis
11
Sting
5
Sinatra
2
by your scale.
would be from 2 – 22
and you want the scale
to fit the graph.
 The best interval
would be to go by 5’s.
Teachers’s Favorite Singer
The amount of space between
one number and the next or
one type of data and the next
on the graph.
The interval is just as
important as the scale
Choose an interval that lets
you make the graph as large
as possible for your paper and
data
T – Title
A – Axis
I – Interval
S – Scale
Teachers’s Favorite Singer
25
T – Title
20
15
10
5
A – Axis
I – Interval
0
S – Scale
Teachers’s Favorite Singer
25
T – Title
Number of Teachers
20
15
10
5
0
LABEL
your bars
Singers
or data points
Give the
Label
your
bars
Y Axis.
a general
Whatlabel.
do
What numbers
those
do those words
mean?mean?
A – Axis
I – Interval
L – Labels
S – Scale
 Given the following data collected from measuring the growth of a plant’s root and
stem over the weeks:
Time
(weeks)
Independent variable
Growth
Root
(cm)
1
2
3
4
5
6
7
Growth
Stem
(cm)
1
1.5
2
2
2
2.5
2.5
1.5
2
3
3.5
4
5
6.5
Use a grid
2.
Write a title
3.
Label the axis (magnitude and unit). Choose what you are going to represent on
each of them (Remember that time always goes on the x axis)
4.
Choose a scale. That is, the value you are going to give to each square of your
grid, for each variable
Growth (cm)
1.
Plant growth
7
6
5
4
3
2
1
0
0
0.5
1
1.5
2
2.5
time (weeks)
3
 Plot the first set of data (root growth) on the graph with dots.
Growth (cm)
7
6
5
4
3
2
1
0
0
0.5
1
1.5
2
2.5
time (weeks)
3
Growth (cm)
 Join the dots with a line
Plant Growth
7
6
5
4
3
2
1
0
0
0.5
1
1.5
2
2.5
time (weeks)
3
 If there where to sets of data, just plot the second one following the same steps
with a different colour.
PATTERNS
 When making your conclusion you need to first identify the patterns in the data. Is the
dependent variable increasing or decreasing? Is there a linear relationship, or
exponential? How exactly are the variables related or not related?
 Increase, decrease, or constant
 data does not go “up”, it increases
 data does not go “down”, it decreases
 data does not stay the same, it is constant
 sometimes data does 1, 2, or all 3 of these at different points
Relationships between variables
 - direct = both increase, or both decrease
 - indirect = they are opposite
 Recall the purpose of the type of graph used and its advantages and disadvantages.
 Read the TITLE . The TITLE briefly describes the data represented in the graph.
 Read the footer or summary of the graph is included.
 Read the labels of the axes. The independent or manipulated variable is usually on the
x axis and the dependent or responding variable on the y axis.
 Read the units of the axes. Ensure you know the quantity measured and the multiple
or submultiple of the units used. Understanding the units used helps you to quantify
relationships between variables.
 Read the scales of the axes.. Is the range a small or large one? Many students take in
the shape of the graph with out first considering the scale. This of course leads to
erroneous conclusions.
 Examine the symbols and the Key/Legend used. Sometimes the curves or columns are
labelled.