Independent Variable - The variable you can control and manipulate in some cases. In other cases, you may not be able to manipulate the independent variable. It may be fixed like color, kind, or time (http://www.ncsu.edu/labwrite/po/independentvar.htm)
Dependent Variable - What you measure in an experiment and what is affected during the experiment. It "depends" on the independent variable
Example: You are interested in how stress affects heart rate in humans. Your independent variable would be the stress and the dependent variable would be the heart rate. You can directly manipulate stress levels in your human subjects and measure how those stress levels change heart rate (http://www.ncsu.edu/labwrite/po/dependentvar.htm).
However, sometimes there is no obvious connection between the variables. In other situations we are interested in how the many variables interact with each other.
There are 4 main types of variables
Original source: http://arb.nzcer.org.nz/supportmaterials/tables.php
The independent variables (if they have been identified) go in the left hand columns, the dependent variables on the right.
variable |
|
absorbed (ml) |
variable |
Original source: http://arb.nzcer.org.nz/supportmaterials/tables.php
Graphs are
Any graph used to report findings should show
Original source: http://arb.nzcer.org.nz/supportmaterials/tables.php
Tips for Good Graphs
1. Give your graph a title. Something like "The dependence of (your dependent variable) on (your independent variable)."
2. The x-axis is your independent variable and the y-axis is your dependent variable.
3. LABELyour x-axis and y-axis. GIVE THE UNITS!!
4. When graphing data from lab, make line graphs because they tell you how one thing changes under the influence of some other variable.
5. NEVER connect the dots on your line graph.
Why? When you do an experiment, you always make mistakes. It's probably not a big mistake, and is frequently not something you have a lot of control over. However, when you do an experiment, many little things go wrong, and these little things add up. As a result, experimental data never makes a nice straight line. Instead, it makes a bunch of dots which kind of wiggle around a graph.
To show that you're a clever young scientist, your best bet is to show that you KNOW your data is sometimes lousy. You do this by making a line (or curve) which seems to follow the data as well as possible, without actually connecting the dots. Doing this shows the trend that the data suggests, without depending too much on the noise. As long as your line (or curve) does a pretty good job of following the data, you should be A-OK.
Original source: http://misterguch.brinkster.net/graph.html
BAD Graph!
Why?
GOOD Graph!
Why?
Well, look at it! It has a title, labeled axes with units and a line of best fit to show the trend of the data.
Original source: http://misterguch.brinkster.net/graph.html