Introduction Often in lab experiments it becomes necessary to make a graph but

Introduction often in lab experiments it becomes

This preview shows page 214 - 215 out of 232 pages.

Introduction: Often in lab experiments it becomes necessary to make a graph, but unfortunately, many of us have not been given procedures for making a proper graph. As a result, several common mistakes tend to occur; improper scales are chosen resulting in graphs that do not utilize the entire piece of graph paper; straight lines are drawn through only two points; axes and titles are not correctly labeled. If you have never been taught how to make a graph, it is nothing to be embarrassed about. In fact, perhaps it is the educational institutions that you have attended that ought to be embarrassed for having not taught you. In an attempt to ensure that no students go through chemistry without having had this instruction, I’ve written this little guideline for you broken up into individual sections for (hopefully) ease of understanding. If you have any questions, comments, or suggestions, please do not hesitate to call (298-3399 x-5658) or email ([email protected]) or stop by (G-12A) to see me. Determining Data Ranges: One of the most common mistakes made is choosing a scale that utilizes only a very small corner of the graph paper rather than the entire piece. This results in graphs that can be difficult to see and larger than necessary experimental error should it be necessary to find the slope, intercept and/or use in predictions. Therefore, it is important to be able to choose a scale for the graph that is appropriate. To begin, we have to decide what kind of graph we are making. There are three general categories; (1) graphs to cover only the experimental points; (2) graphs that must extend to some smaller number well beyond the smallest experimental point (often zero); and (3) graphs that must extend to some greater number well beyond the largest experimental point. Regardless of the type of graph that one must make, begin by examining your data. You’ll have a set of data that includes both “x” and “y” data. Generally, “x” is taken to be exact, while “y” is the measured quantity, that is, the quantity that has experimental error. For instance, suppose I were to make a graph of boiling points versus molecular weight. You’ll find a table of such data below in the appendix. The boiling points I measure experimentally. This implies that there could (and will) be some experimental error associated with this measurement. The molecular weight, however, can be easily calculated with very little error (much much smaller error than the boiling point). Therefore, I make my measurement with the greatest error (boiling point) the “y” axis, and the measurement with the smallest error (molecular weight) the “x” axis. For both the x and y axis, determine your largest and smallest value. If your graph must extend to some range other than that covered by the experimental data, use these as your limits instead. For instance, perhaps my graph of boiling points versus molecular weight must cover very small molecular weights, say down to the molecular weight of methane CH 4 , which has a molecular weight of 16 g/mol. However, my
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