# Experiment #1, Analyze Data using Excel, Calculator and Graphs.

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1 Physics Fall Experiment #1 1 Experiment #1, Analyze Data using Excel, Calculator and Graphs. 1 Purpose (5 Points, Including Title. Points apply to your lab report.) Before we start measuring physical quantities we want to spend a little time developing techniques for handling the numbers we will be generating. To that end this is a computer-based exercise in the fitting of non-linear data. The purpose of this Physics 182 experiment is to use Excel in the lab and a calculator for your report to analyze data that have linear and non-linear functional forms. You will plot and analyze graphs (Excel and one hand drawn graph for your lab report) to validate theory and to use Excel and a scientific calculator to do statistical calculations, including linear regression. Your lab report should not have an Excel graph. Nothing in this lab needs to be saved, printed or ed at the end of this lab. Feel free to use a USB device to save information on future labs. 2 Introduction When we make a measurement we want to be able to connect the quantities measured to the mathematical theories that we are trying to test. Usually the quantities we can measure; mass, speed, time, etc., are not the quantities that we are interested in knowing. But, they can be connected to our desired quantities by a theoretical model. Every measurement has associated with it intrinsic errors which distort what we measure from the theoretical ideal. One of the strongest tools we have for understanding how to extract the quantities of interest out of the random noise of these errors is Statistical Analysis. For a simple measurement of a single quantity we arrive at an experimental value by making several measurements and calculating their statistical mean value. At the same time we can also calculate the standard deviation from our data set and use it to derive the standard deviation of the mean which is considered the standard error on the measurement. Sometimes our measurements involve a relationship between something we measure, the dependent quantity, and something we control, the independent quantity or control parameter. In this case simple statistics cannot help us. If there is a linear relationship between dependant and independent variables then there is a more sophisticated statistical protocol, Linear Regression, which can give us an exact determination of the linear equation that describes the connection. For a non-linear relationship the situation is less clear cut. In general if we are trying to fit a non-linear relationship between dependent and independent variables we use what is called simplectic minimization. Unfortunately, this technique does not always provide a solution. The best way around this problem is to manipulate the data so that the relationship between control parameter and some function of the measured quantity fits a linear equation. For example, let us take the exponential decay of the amount of a drug present in a blood stream as a function of time after its administration. The concentration of drug, C, has a time dependence that takes the functional form, t C Ae where A is the initial concentration and is what is known as the decay time. If we measure C at several times we generate a set of data that should fit this functional form. To check this, using linear regression we would plot the natural logarithm of the concentration, Ln(C), as a function of time. Taking the natural logarithm of both sides of the above equation we get, 8/26/2014

2 2 t Ln(C) Ln(Ae ). Which can be rewritten using the properties of logarithms to take the form? t Ln( C) Ln( A) Ln( e ). And then, t t Ln( C) Ln( A) Ln( e) Ln( A). The above is now a linear expression of the form Y = mx + b. So if we plot the natural logarithm of our concentration (Y = Ln(C)) as a function of time (x = t) and perform linear regression on the resulting time series the intercept (b) that comes out of the calculation will be equal to the natural logarithm of the initial concentration. And the slope (m) will be equal to the negative reciprocal of the decay time, b Ln(A) m 1 Thus, by performing linear regression on our linearized data we can determine the two quantities that are important in our measurement. Students should study the appendix on error analysis in the supplementary material entitled Error Analysis which is located at where you will too access other material for this class. You will need this appendix to answer questions for this experiment. The calculator help sheet, which is also located at this Web site, will be used in this experiment. The course information sheet for Physics 182 contains information on the graphing of data and points attributed to grading. You are required to read this course information sheet the first week of this course. In addition, students should become familiar with the operation of their calculator to do statistical calculations, including linear regression. Sources available to learn how to use your calculator include: calculator manual, information from the Internet, and help from other students who use the same, or similar, type of calculator. TAs are not expected to help with the use of your calculator. There are too many types of calculators. Learning its use is your responsibility. 3 Theory The first data set (Part A) for this Excel experiment uses the Inverse Square Law of Light: I = r P 2 Equation (1) where I is the intensity measured at a distance r from the source (light), and P is the total power emitted by the source (light). The inverse square law states that the measured intensity of the light is proportional to the inverse square of the distance from the source.

4 = 0.05 cm (centimeters) for the distance r data. Enter this value into your data section. For your lab report, feel free to generate tables using a computer, and Excel if you have it, and pasting them into the report Malus s Law (Part B) Figure 1. Distance r and intensity I measurements entered in Excel. The following degree and intensity I data (Figure 2) will be used for the second part of the computer experiment. Your Excel sheet, when used, should resemble Figure 2 which contains this data. For this part of the experiment, the random error is the positioning the polarizer. This error, in the values for degree is given as is ± = 0.05 degrees. Enter this value into your data section. Figure 2. Degrees and intensity I measurements entered in Excel. Data in 5.1 and 5.2 should be entered into your lab report in tables similar to the above. All data has two numbers after the decimal point. Enter errors in measurements, x and in this section of your report. The digital meter may have a systematic error as nothing is perfectly calibrated. Include this comment in your analysis section for both 5.1 and 5.2.

5 Physics Fall Experiment #1 5 6 Calculations and Analysis (15 Points) and Graph (10 Points) Part A: 1. Enter, in an Excel sheet, your data for 5.1. Your Excel sheet should reflect Figure The first thing we want to do is investigate a possible error in the measurements. This can be done by graphing in Excel a linear representation of the data. To convert Equation (1) to a linear expression, use the following equation: r = P I Equation (3) In Excel, you now need to add a column for the inverse of the square root of the intensity I, or, (I) -.5. Note that (I) is the same as. So too is = entered I SQRT( I ) in Excel. The following Figure explains this process. Figure 3. Using Excel to calculate the inverse of the square root of intensity I. Now, in Excel, graph r vs (I) -.5, where r is the dependent variable Y in the general linear expression Y = mx + b. Here m is the slope, x = (I) -.5 is the independent variable and b is the intercept. See the calculator help sheet for additional information on this. Your Excel graph should be linear, as shown in the following Figure. 8/26/2014

6 6 Figure 4. Graph of distance r vs the inverse square root of intensity I. In the above Figure, we see that the coefficient of determination is The closer this value is to R 2 = , the more linear your data. Inspection of this graph shows that one data point appears not to be linear with the rest. This data point should be removed. The following Figure is the same graph with the non-linear point removed. You should do this graph (Figure 5) using Excel for this experiment. Figure 5. Graph of distance r vs the inverse square root of intensity I with the one data point, which appears to fail to be linear, removed. The coefficient of determination in Figure 5 is now , which indicates data that is very linear, and this suggest no large errors in their measurement. Make sure that

7 Physics Fall Experiment #1 7 R 2 is always entered with four numbers after the decimal point. Both in Excel and in your linear regression calculations. 3. Use Excel to graph Intensity I vs. Distance r using data in Figure 1. When you do this graph, be sure it is done with the non-linear data point (28.95, 8.64) removed. When you format the trend line, use the power function and not the linear one. Your graph should reflect the following. Figure 6. Graph of intensity I vs distance r. Using Figure 6, we have Y = 13, x Applying this to Equation (1), we have: P I = r P I = r where P = 13, (lux-cm 2 ) or where P = x 10 4 (lux-cm 2 ) The inverse square law states that the measured intensity of the light is proportional to the inverse square of the distance (1/r 2 ) from the source. Our result of 1/r indicates a strong experimental validation of this inverse square law. Note that R 2, the coefficient of determination, are identical in Figures 5 and 6. Our value of x would be different if we did not remove the non-linear data. See Figure below. 8/26/2014

10 10 7. From Equation (2), and Y = mx + b, we see that x = cos 2 is linear with respect to Y = I In Equation (2), the intercept b is zero. In Excel, do a graph of I vs cos 2. Your Excel graph should reflect the one in Figure 10 below. Figure 10. Graph of intensity I vs cos The following is a graph of the data from Figure 2. Note the non-linear relationship. You are not required to do this graph, Figure 11, in Excel, or for your report. Figure 11. Graph of intensity I vs Angle Theta. 9. For your lab report, you are required to use your calculator s linear regression function to find the slope, intercept and coefficient of determination for I vs cos 2 data as in Figure 10. Note in Figure 10 that the intercept approaches the value of zero as reflected in Equation (2). You are not required to do a hand drawn graph

12 or fail to account for relevant physical phenomena? A detailed discussion should include analysis of any experimental errors, instrumentation problems or mishaps that occurred, and how these may have impacted the results. Be thoughtful and think critically about these considerations. If an experiment was challenging, a discussion of exactly what made it challenging, and possibly, how it could be conducted differently, should be included. Or, if an experimental measurement went completely smoothly, this should also be discussed. Also this section may include discussion of how the insights from one particular experiment are related or complementary to other experiments conducted in the course. Remember that your discussion should be a thoughtful scientific analysis, not a discussion of how you enjoyed or did not enjoy the lab. 9 Conclusions (5 Points) The report should end with a clear conclusion statement. This is the bottom-line experimental result summarizing the main quantitative results of the experiment and the extent to which they are in agreement with theoretical predication and/or an established reference value. When the experiment results in a measurement of a constant (e.g., the acceleration due to gravity at the earth s surface), compare it with its established handbook values for the Boston area. Use percent error to quantify this comparison. To make this comparison meaningful, you should include the impact of the experimental error (random, systematic and any individual investigator mistakes) on your results. This includes errors in plotting and reading linear graphs when determining their slope and intercept. 12

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China Career Fair: To Know a Different Intel Time Participants Chat Transcript [ 13 年 12 月 06 日, 下 午 6 点 00 分 ] Participant Hi [ 13 年 12 月 06 日, 下 午 6 点 00 分 ] Intel Hosts 大 家 好! [ 13 年 12 月 06 日, 下 午

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