# Using DataStudioTM to Graph Experimental Data - Part 1

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## Purpose:

This lab is a little different. It's purpose is to show how the Pasco DataStudioTM software can be used to analyze experimental data graphically. Very soon, you will be using this software to collect data (using sensors of various types) as well as analyze it, so this laboratory exercise can serve as an introduction to part of the DataStudioTM software.

## Discussion:

Calculating and graphing experimental data is vitally important, but it is generally time-consuming and tedious, too. (You might have noticed that in the beginning labs of this course...) While it is important that you be able to make calculations from data and draw graphs from data "by hand", the computer provides a powerful tool that takes a great deal of the drudgery out of data analysis. In this laboratory exercise, you will re-graph, using the computer, the experimental data from the "Force vs. Stretch for a Spring" lab - just to see how it is done.

## Equipment:

 DataStudioTM software the DataStudioTM file "f_vs_x_graph_ds" your data from experiment 1 - "Force vs. Stretch for a Spring"

## Setup:

1. Open the DataStudioTM software. Select "Open Activity" from the initial screen, as shown below.
2. Open the file "f_vs_x_graph_ds". Ask your instructor where you can find this file. Your screen should look like the picture below:

## Procedure:

1. First, open the graph settings dialog by clicking its icon () in the graph's tool bar (right side). The graph settings dialog will open, as shown below:
2. Click on the "Error Estimates" tab to bring up the Error Estimates dialog. Notice that default values have already been added for you 0.2 cm for stretch and 2% for force. If these values are not acceptable, you can edit them now.
3. Now, it's time to type your data into the data table. For this exercise, we will only process data for one spring - your choice. Enter your data (calculations, actually) for force and stretch in the appropriate columns of the data table. Notice that as you add data, it is automatically graphed, and the graph's scale is adjusted to show all of the relevant data so far.

## Results:

1. When all of your data is entered, pull down the submenu, and select "Linear". The software will automatically draw the "best-fit straight line" through your data points and calculate its equation - "m" and "b" are the slope and y-intercept of the best-fit line (in the equation y = mx + b). The value of "r", called the correlation coefficient, is a statistical measure of how closely the data points fit the line. The closer that the value of "r" is to 1 (or -1, for a negative slope), the better the fit.
2. Before you print your graph, you can add an identifying note by pressing in the graph's toolbar. Then, move the mouse to the position where you want the note to appear, and click. The Note Dialog (shown below) will appear. (You might want to select "No Pointer" from the Data Pointer menu, or your note will point to your graphed line. It's not a big deal, however.)
3. To print the data table, select it with the mouse, then choose "Print" from the file menu.

## Conclusions:

You don't need to restate your original conclusions, but what do you think about computerized graphing (as compared to manual graphing)?

[Lab Index]

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last update July 25, 2003 by JL Stanbrough