|
Bar Graphs Table of Contents 4. Histogram Introduction Bar
graphs are a very common type of graph best suited for a qualitative
independent variable. Since there is no uniform distance between levels of a
qualitative variable, the discrete nature of the individual bars are well
suited for this type of independent variable. Though you can extract trends
between bars (e.g., they are gradually getting longer or shorter), you cannot
calculate a slope from the heights of the bars. One
Independent and One Dependent Variable
Here the Factory is our independent variable, since there
is no unit of measurement for factories and no 'order' to the factories, the
independent variable is nominal. The dependent variable is scalar, measured
in defects/1,000 cars. Since the scalar dependent variable has a natural zero
point (i.e., absolute or ratio), all of the bars are anchored to the
horizontal axis, giving a common point of measurement.
Bar graphs can be shown with the dependent variable on the
horizontal scale. This type of bar graph is typically referred to as a
horizontal bar graph. Otherwise the layout is similar to the vertical bar
graph. Note in the example above, that when you have well-defined zero point
(ratio and absolute values) and both positive and negative values, you can
place your vertical (independent variable) axis at the zero value of the
dependent variable scale. The negative and positive bars are clearly
differentiated from each other both in terms of the direction they point and
their color.
Range bar graphs represents the dependent variable as
interval data. The bars rather than starting at a common zero point, begin at
first dependent variable value for that particular bar. Just as with simple
bar graphs, range bar graphs can be either horizontal or vertical. Notice in
the horizontal example above, a reference line is used to indicate a common
key dependent variable value.
Histograms are similar to simple bar graphs except that
each bar represents a range of independent variable values rather than just a
single value. What makes this different from a regular bar graph is that each
bar represents a summary of data rather than an independent value. For this
type of graph, the dependent variable is almost always a scalar scale
representing the count, or number, of how many of a sample fall within each
range of the independent variable. In the example above, the sample is all
the females in the U.S. The independent variable is age, which as been
grouped into ranges of 5 years each. You should try and keep the ranges for
each bar uniform (5 years in this case), with the exception possibly being
the first and/or last range. Two
(or more) Independent and One Dependent Variable
Here, we have taken the same graph seen above and added a
second independent variable, year. The initial independent variable, factory,
is nominal. The second independent variable, year, can be treated as being
either as ordinal or scalar. This is often the case with larger units of
time, such as weeks, months, and years. Since we have a second independent
variable, some sort of coding is needed to indicate which level (year) each
bar is. Though we could label each bar with text indicating the year, it is
more efficient to use color. We will need a legend to explain the color
coding scheme. Note that all of the bars for each level of factory are
touching each other, indicating visually that they are grouped together.
Another alternative for a bar graph with two independent
variables is to have the bars stacked rather than side-by-side. This
arrangement is useful when the summation of all the levels of the second
independent variable is as or more important than the values for each level.
In the upper example, it is very easy to read the summed weight of all of the
different materials in each sample. There are, however, tradeoffs. The
stacking of the bars means there is no common baseline for the individual bar
elements, making it hard to make direct comparisons for the subcategories.
For example, it is hard to compare the iron content of the three samples. A
particularly powerful use for the composite bar graph is when the sum of all
the dependent variable values for each bar is the same, such as when the
values are a fraction of a whole. In the bottom example, the sum of the three
different types of fats will always equal 100 percent. With this layout it is
easier to see the relative portions, if not the absolute values, of a
particular fat type across oils. For
information on creating bar graphs with Excel, go to the Bar Graphs Module, or go to the Excel Tutorial Main
Menu
for a complete list of modules. Specific tips for bar
graphs ·
Vertical
bar graphs are called column graphs in Excel ·
Horizontal
bar graphs are called bar graphs in Excel ·
The
clustered sub-type will do single bar and grouped bar graphs ·
The
stacked subtype will do composite bar graphs ·
Range
bars cannot easily be done in Excel without additional custom graph types
loaded ·
Double-clicking
on the bars will allow you to set your preferences for bar display. Under the
Options tab you can set the ratio of the bar width to gap between bars |
||
|
|
|
|
|
|
© Copyright NC State University 2004 Site design by Rosa Wallace Rev. RW 5/16/05 |
|
