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Chemistry Lab Resources (for CHM 1XX and 2XX Labs): Graphs/Tables

Here you can find tips about organizing your lab notebook, how to effectively create graphs and table for lab reports, places to locate protocols and property information, and how to properly cite resources.

Variables

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

  • categoric variable – described by a word label, not a number, e.g., different brands of paper towel
  • ordered variable – categoric variables that can be put in order, e.g., cool, warm, hot
  • discrete variable – described by whole numbers only, e.g., 1, 2, 3 teaspoons
  • continuous variable – described by any number or part number, e.g., 35.5°.

Original source: http://arb.nzcer.org.nz/supportmaterials/tables.php

Creating Tables

The independent variables (if they have been identified) go in the left hand columns, the dependent variables on the right.

The independent
variable
Type of paper towel
Amount of water
absorbed

(ml)
The dependent
 variable

  • Any column heading should have all the information needed to define the table's meaning.
  • A categoric variable should include a description of the class.
  • A discrete or continuous variable should identify units and any multipliers (e.g., hundreds of people, millions of dollars, kilometres).
  • The title summarizes what the talbe shows

What a table can tell you

  • A table helps organise information so it is easier to see patterns and relationships.
  • If a variable is continuous the table reveals a lot more information. It may show the range, interval, and number of readings.

Limitations

  • It can be difficult to see numerical relationships and patterns. A graph may make these clearer.

Original source: http://arb.nzcer.org.nz/supportmaterials/tables.php

Creating Graphs

Graphs are

  • a way of exploring the relationships in data
  • a way of displaying and reporting data, making it easier to report patterns and relationships, shapes of distributions, and trends.

Structure

Any graph used to report findings should show

  • the significant features and findings of the investigation in a fair and easily read way
  • the underlying structure of an investigation in terms of the relationships between and within the variables
  • the units of measurement
  • the number of readings (though sometimes these will be in the accompanying text)
  • the range and interval of readings, where appropriate.

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

Good Graph Bad Graph

Example of Bad Graph

BAD Graph!

Why?

  • There's no title.  What's it a graph of?  Who knows?
  • There are no labels on the x or y axis.  What are those numbers?  Who knows?
  • There are no units on the x or y axis.  Is this a graph of speed in miles per hour or a graph of temperature in Kelvins?  Who can tell?
  • Somebody played "connect the dots".  This should be a nice straight line which goes through the points or a curve that tends to follow them.

Example of Good Graph

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