# Using Excel (Microsoft Office 2007 Version) for Graphical Analysis of Data

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1 Using Excel (Microsoft Office 2007 Version) for Graphical Analysis of Data Introduction In several upcoming labs, a primary goal will be to determine the mathematical relationship between two variable physical parameters. Graphs are useful tools that can elucidate such relationships. First, plotting a graph provides a visual image of data and any trends therein. Second, via appropriate analysis, they provide us with the ability to predict the results of any changes to the system. An important technique in graphical analysis is the transformation of experimental data to produce a straight line. If there is a direct, linear relationship between two variable parameters, the data may be fitted to the equation of line with the familiar form y = mx + b through a technique known as linear regression. Here m represents the slope of the line, and b represents the y-intercept, as shown in the figure below. This equation expresses the mathematical relationship between the two variables plotted, and allows for the prediction of unknown values within the parameters. y data best-fit line y Best-fit line Equation: y = mx + b b = y-intercept b x m = slope = y/ x = y 2 -y 1 /x 2 -x 1 x data Computer spreadsheets are powerful tools for manipulating and graphing quantitative data. In this exercise, the spreadsheet program Microsoft Excel will be used for this purpose. In particular, students will learn to use Excel in order to explore a number of linear graphical relationships. Please note that although Excel can fit curves to nonlinear data sets, this form of analysis is usually not as accurate as linear regression. Using Excel for Graphical Analysis of Data Page 1 of 12

2 Part 1: Simple Linear Plot Scenario: A certain experiment is designed to measure the volume of 1 mole of helium gas at a variety of different temperatures, while keeping the gas pressure constant at 758 torr. The data that was collected is given below. Temperature (K) Volume of Helium (L) a) Launch the program Excel Go to the Start button (at the bottom left on the screen), then click Programs, followed by Microsoft Excel. If you are using a computer running Excel 2003, please print out the alternate version of these instructions. b) Enter the above data into the first two columns in the spreadsheet. Reserve the first row for column labels. The x values must be entered to the left of the y values in the spreadsheet. Remember that the independent variable (the one that you, as the experimenter, have control of) goes on the x-axis while the dependent variable (the measured data) goes on the y-axis. c) Highlight the set of data (not the column labels) that you wish to plot (Figure 1). Figure 1 Click on the Insert tab at the top left, followed by Scatter (Figure 2). Figure 2 Choose the scatter graph that shows data points only, with no connecting lines the option labeled Scatter with Only Markers (Figure 3). Figure 3 Using Excel for Graphical Analysis of Data Page 2 of 12

3 You should now see a scatter plot on your Excel screen, which provides a preview of your graph (Figure 4). Figure 4 If all looks well, it is time to add titles and label the axes of your graph (Figure 5). First, click inside the chart. Click on the Layout tab, at the top right section of the toolbar. Click on Chart Title to add a title. The graph should be given a meaningful, explanatory title that starts out Y versus X followed by a description of your system. Click on Axis Titles (select Primary Horizontal Axis Title and Primary Vertical Axis Title ) to add labels to the x- and y-axes. Note that it is important to label axes with both the measurement and the units used. Figure 5 Using Excel for Graphical Analysis of Data Page 3 of 12

4 To change the titles, click the text box for each title, highlight the text and type in your new title (Figure 6). Figure 6 d) Your next step is to add a trendline to these plotted data points. A trendline represents the best possible linear fit to your data. To do this you first need to "activate" the graph. Do this by clicking on any one of the data points. When you do this, all the data points will appear highlighted. Now click on Trendline on the top toolbar (in the Analysis section) and choose More Trendline Options. The Format Trendline window should now appear (Figure 7). Figure 7 Notice that the Linear button is already selected. Now select the Display Equation on Chart box and the Display R-squared value on Chart box (Figure 7). Then click the Close button. Using Excel for Graphical Analysis of Data Page 4 of 12

6 Part 2: Using Functions Scenario: An experiment is designed to measure how quickly helium gas diffuses from a source to a detector ten meters away, as a function of the temperature of the source. The data collected are given below: Temperature (K) Time of Diffusion (ms) a) Enter this new data on a fresh page in Excel by clicking on the Sheet 2 tab at the bottom left of the page. Then create a plot of Time versus Temperature (as learned in Part 1). Why is time plotted on the y-axis? Which set of data is placed on the left in the Excel worksheet? b) Examination of your graph will reveal that the plotted data are not linear. Your goal is to linearize these data, by operating on one column of your data values with a function. One of the following functions of temperature will give a straight line when plotted against time: ln(temp) = the natural log of temperature (Temp) ½ = the square root of temperature (Temp) ½ = the inverse of the square root of temperature c) Using functions to manipulate data values in Excel is quite straightforward. As an example, follow the procedure below to obtain a column of ln(temp) values: Pick an empty column to the right of your data. In the same row as your old data begins, type "=ln(a2)" where a2 represents the cell in which the first temperature value exists (a1 should contain your column label). When you hit return, the program returns the natural log of that value and puts it into the new cell. To operate on the rest of the values, click on the cell with the formula near the bottom right corner so that the cursor appears as a solid black cross (not a white cross). Hold the mouse button down and drag down the cell until you are in the same row as the last data point. When you release the mouse, all the cells will fill in with the ln of each temperature value in the correct rows. Note that this is simply a shorthand way to paste the function into many cells at once. Be sure to add a label to this new column of data. Now graph the new function versus time and fit the data to see if it is linear. Note that you will have to re-enter the time data in the column beside the new ln(temp) data before creating the new graph. d) Repeat this procedure for the other two functions of temperature. For formulas, type =(a2)^0.5 to obtain the square root of temperature, and =(a2)^-0.5 to obtain the inverse square root of temperature. Then fit a trendline to whichever plot is determined to be linear. e) Record on your report: which function of temperature produces a linear relationship to time the slope of the trendline fitted to your linear plot. Using Excel for Graphical Analysis of Data Page 6 of 12

7 Part 3: Two Data Sets with Overlay Scenario: In a certain experiment, a spectrophotometer is used to measure the light absorbance of several solutions containing different quantities of a red dye. The two sets of data collected are presented in the table below. Data A Data B Amount of Dye (mol) Absorbance (unitless) Amount of Dye (mol) Absorbance (unitless) You would like to see how these two sets of data relate to each other. To do this you will have to place both sets of data, as independent relationships, on the same graph. Note that this process only works when you have the same axis values and magnitudes. a) Enter this new data on a fresh page (Sheet 3) in Excel. Be sure to label your data columns A and B. Again, remember to enter the x values to the left of the y values. b) First, plot Data A only as an XY Scatter plot. Fit a trendline to this data using linear regression, and obtain the equation of this line. c) Now add Data B to this graph. Activate the graph by clicking on one of the plotted data points. Click on the Design tab and click on the Select Data icon. The Select Data Source window should appear (Figure 8). Figure 8 Using Excel for Graphical Analysis of Data Page 7 of 12

8 Click the Add tab (Figure 8), and type Data B for the Series Name. Click the little icon under Series X values, then highlight the x-axis values of Data B. Press enter, then repeat this procedure for the Series Y Values, highlighting the y-axis values of Data B. For each of these steps, you should see a display similar to what is shown in Figure 9. Click OK twice to return to the main Excel window. Figure 9 At this point you should see the new data points (labeled as Series 2) as shown in Figure 10. You can now independently analyze this data set by inserting a trendline as before. Figure 10 d) Record the following information on your report: the equation of the best-fit trendline for Data A, the equation of the best-fit trendline for Data B, If these trendlines were extrapolated, they would intersect. Determine the values of x and y for the point of intersection using simultaneous equations. Using Excel for Graphical Analysis of Data Page 8 of 12

9 Part 4: Choosing the Correct Parameters for Graphing Scenario: The following data was collected from an experiment which measures the rate constant (k) of a first order hydrolysis reaction as a function of temperature. Temperature (K) Rate Constant, k (s -1 ) x x x x x 10-1 a) k (the rate constant) and T (the temperature) are related via the following equation: k = Ae E a RT A is called the frequency factor (units are identical to those of k, s -1 ), E a is the energy of activation (units of kj/mol), and R is the thermodynamic gas constant (8.31 x 10-3 kj/k mol). Since the relationship between k and T is an exponential one, the data set when plotted will not be linear. Your goal is to linearize it. To do this, you must perform different functions on both k and T before you plot the data. b) How do you take an exponential function and transform it into a linear function? Clearly you have to get rid of the "e" or the exponential term. To do this, simply take the natural logarithm of both sides of the equation. This gives the new, linearized equation below. ln k Ea = ln A RT It is this function that you must plot to get a straight line. But exactly what are you going to plot on the x-axis and y-axis to create this linear graph? If you rearrange the previous equation, it might become evident: Ea 1 ln k = + ln A R T Identify y, x, m and b within this equation, and record them on your report. Check with your instructor to make sure you have figured it out correctly. Only then should you go ahead and construct your linear plot. c) Enter the data on a fresh page (Sheet 4) in Excel, and use the appropriate functions (as learned in Part 2) to linearize it. Then plot these variables to obtain a linear graph. Be sure to add a trendline to your data set, and obtain the equation of this line. d) Record the equation of the fitted trendline on your report. Then use information obtained from this equation to calculate: the value of E a in kj/mol. the value of A, in s -1. Using Excel for Graphical Analysis of Data Page 9 of 12

10 Part 5: Statistical Analysis and Simple Scatter Plots Scenario: When many independent measurements are made for one variable, there is inevitably some scatter (noise) in the data. This is usually the result of random errors over which the experimenter has little control. For example, the data sets below show measurements of the sulfate ion concentration in a sample of water by ten different students at two different colleges. College #1 College # Simple statistical analyses of these data sets might include calculations of the mean and median concentration, and the standard deviation. The mean ( x ) is simply the average value, defined as the sum (Σ) of each of the measurements (x i ) in a data set divided by the number of measurements (N): x = The median (M) is the midpoint value of a numerically ordered data set, where half of the measurements are above the median and half are below. The median location of N measurements can be found using: N x i M = ( N +1) 2 When N is an odd number, the formula yields a integer that represents the value corresponding to the median location in an ordered distribution of measurements. For example, in the set of numbers ( ) the median location is (7 + 1) / 2, or the 4 th value. When applied to the numerically ordered set ( ), the number 5 is the 4 th value and is thus the median three scores are above 5 and three are below 5. Note that if there were only 6 numbers in the set ( ), the median location is (6 + 1) / 2, or the 3.5 th value. In this case the median is half-way between the 3 rd and 4 th values in the ordered distribution, or 4.5. Standard deviation (s) is a measure of the variation in a data set, and is defined as the square root of the sum of squares divided by the number of measurements minus one: ( xi x) s = N 1 So to find s, subtract each measurement from the mean, square that result, add it to the results of each other difference squared, divide that sum by the number of measurements minus one, then take the square root of this result. The larger this value is, the greater the variation in the data, and the lower the precision in the measurements. While the mean, median and standard deviation can be calculated by hand, it is often more convenient to use a calculator or computer to determine these values. Microsoft Excel is particularly well suited for such statistical analyses, especially on large data sets. 2 Using Excel for Graphical Analysis of Data Page 10 of 12

12 Note that data sets with a greater degree of scatter will have a higher standard deviation and consist of less precise measurements than data sets with a small degree of scatter. Experimental Measurements Data set 1 Data set 2 Figure 11 To obtain such a plot using Excel, all the x values for each data set must be identical. Thus, let the College #1 measurements be assigned x = 1.0, and let x = 2.0 for the College #2 measurements. Measurements by Students from College #1 Measurements by Students from College #2 College [SO -2 4 ] () College [SO -2 4 ] () d) Enter the data as shown above into the first four columns of your spreadsheet. Plot the College #1 data set as an XY Scatter Plot. Now add the College #2 data set to this graph applying the same steps you used to create your earlier graph in the section Two Data Sets with Overlay (Part 3). Add appropriate axis labels and a title. You may also want to adjust the x-axis and y- axis scales to improve the final look of your graph. e) Print out a full size copy of this prepared graph and staple it to your report. Analyze your scatter plot. Which data set (from College #1 or College #2) has the greater standard deviation? Which data set consists of the more precise measurements? Using Excel for Graphical Analysis of Data Page 12 of 12

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