ISCI 2001-2002
Graphing
I. Types of Graphs
(1). Line Graph
Plots continuous data as points and joins them with a line. More than one data set may be plotted
but a key or legend is needed.
(2). Bar Graph
Displays discrete data in separate columns. Double bar graphs can be used to compare two data
sets. They are used to compare distinct items or show single items at distinct intervals. They are
useful for comparing data items that are in competition, so it makes sense to place the longest
bars on top and the others in descending order beneath the longest one.
(3). Pie Chart
Displays data as a percentage of the whole. Each section should have a label and percentage. A
total data number should be used. They are used to show proportions of a whole. It is useful for
figures that relate to a larger sum, such as demographic data or budget information. It is said to
show a ‘snapshop’ of one moment in time.
(4). Scatter Plot
Displays the relationship between two factors of an experiment. A ‘line of best fit’ is used to
determine positive, negative, or no correlation. With this type of graph you plot data points
against their values.
II. How to Construct a proper Line Graph
1. Determine the variables
 Independent – on the X axes
 Dependent – on the Y axes
2. Determine the range of the two variables
 For each variable subtract the lowest data value from the highest data value.
If you wish the origin of the graph to start at zero, use the highest data value
for the range.
3. Determine the scale of the graph
 Count the number of squares (Large) horizontally and vertically on the
graph paper. Divide the range of the independent variable by the horizontal
number of squares, then round off to the next larger convenient number.


For example: If dividing gives 34.3, you may want to round to 50. Each
square to have a value of 50.
Determine the range of the dependent variable by dividing its range by the
number of vertical squares, then rounding up.
4. Number each axis
 Remember numbers will represent the lines not the boxes.
 Do not crowd numbers too close together. Eliminate some numbers if needed
5. Label each axis
 Label the variables
 Label the units in ( ).
6. Plot the data points
 Plot each point as precise as possible.
 If multiple data is plotted on the same graph, be sure to use different shapes,
colors, etc.
7. Draw the line
 If the data points are continuous and straight draw a straight line through
the points.
 Many dots may not line up straight so as to draw a perfectly straight line
through the points. Draw a line of best fit through the data points. Draw this
line as close as possible to as many data points as possible. Try to have about
the same number of data points on both sides of the straight line.
 Sometimes data lines may be drawn as a smooth curve.
8. Add a title
 The title should be selected to clearly but briefly tell what the graph is about.
Avoid cute teasers that attract attention! Only include critical or valuable
information.
III. Determining the Slope of a Line
After the line of best fit is drawn pick two points on the graph to analyze. After
each point is picked, determine the slope by using the following method:
Slope = Y/X or (Y2 – Y1)/(X2 – X1)
IV. Relationships between Variables
1. The slope of a line and the shape of the graph allows us to interpret the relationship
between the two variables.
Look at Graph A
 As X variable increases, Y variable also increases


Positive or proportional relationship (Direct)
A straight line indicates the increase is at a constant rate.
Look at Graph B
 As the X increase the Y variable decreases.
 Indirect or inversely related
Look at Graph C
 As the X variable increases, the Y variable is not affected
 No relationship between the variables
Look at Graph D
 As the X increases, the Y increase then decreases
 This shows a changing rate
Graph A
Graph B
Graph D
Graph C
A proper line graph. Shows the calculation of the slope (rate) between 10 and 20 seconds.
When the data points form a straight line on the graph, the linear relationship between the
variables is stronger and the correlation is higher (Figure 1).
Figure 1. Strong linear relationship of variables
Positive or direct relationships
Figure 2. Positive or direct relationships between variables
This graph also shows a direct relationship between the variables. It also shows a line of best
fit drawn through the data points.
Non-linear patterns
Very low or zero correlation may result from a non-linear relationship between two variables. If
the relationship is, in fact, non-linear (i.e., points clustering around a curve, not a straight line),
the correlation coefficient will not be a good measure of the strength of the relationship (Figure
5).
Figure 5. Very low or zero correlation