Lab #1 - Graphing

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Jan 9, 2024

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Physics 4A Lab #1: Graphical Representation of Experimental Data Lab Group Members: 1. _________________________ 2. _________________________ 3. _________________________ Type all responses, including equations

INTRODUCTION: Plots are the most common way of presenting data. Often, it is the most effective way to present the results of an investigation and the easiest way to understand and interpret these results. This is true because a plot visually displays the relationship between two physical quantities, such as pressure and temperature in this experiment. For a plot to be easily understood by all readers it must follow a standardized format; the quintessential idea being that the plot be clear and concise in their labeling. For our class we will be using the follow set of instructions to create plots throughout the semester: Instructions for Constructing a Data Plot ORIENTATION • Plots should be presented in landscape orientation and not portrait orientation, as shown below in FIGURE 1 . FIGURE 1 – Two possible page orientations. TITLE • Describes which part of the experiment that the data comes from. o Use words, not abbreviations or mathematical symbols. AXES • Describes the plotted variable. o Use words, not abbreviations or mathematical symbols. • Describes the units, within parentheses, of the plotted variable. o Use the appropriate abbreviation or mathematical symbols, not words. CURVE FITTING • Don’t use a series of straight-line segments to connect successive data points, as shown below in FIGURE 2 . o This is often done in business but is never done in science.

FIGURE 2 – Sample business graph with connected data points. • An equation, in the form of a single and continuous curve, will attempt to match or “fit” the data points to the desired mathematical behavior. o This curve, called a best-fit line or a trendline, usually doesn’t pass through every data point in the plot as seen in FIGURE 3 below. FIGURE 3 – Sample trendline (in red) to plotted data points (in green). o Computer software, such as Microsoft Excel, Google Sheets, and PASCO Capstone, will automatically display the numerical values for all coefficients.

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PROCEDURE 1. Log in to the laptop computer. 2. Create the first plot with the following steps: a. Create a Microsoft Excel or Google Sheets plot of pressure versus temperature using the data in the data table given in the next page. • Properly format the graph. o Give the plot an appropriate title, such as “Non-Ideal Gas Law Data”. • Insert a linear trendline that isn’t forced to go through the origin. • Display the trendline equation displayed and the ࠵? -squared value directly beneath the graph’s title. b. Attach the plot in the Data Analysis section. 3. Create the second and third plots with the following steps: a. Open the “PHYS 4A PASCO Files” folder on the desktop. b. Click on the “Graphing.cap” file. c. Populate the second graph tab using the data in the data table. • Properly scale the graph. • Insert a quadratic trendline. d. Click on the third graph tab. • This data table should already be populated. • Properly scale the graph. • Insert a cubic trendline. e. Attach both plots in the Data Analysis section. 4. Attach all three graphs in the Data Analysis section. DATA TABLE: GAS DATA VAPOR TEMPERATURE ࠵? ( ℃ ) VAPOR PRESSURE ࠵? ( ࠵?࠵? ) 10.4 19 20.3 29 30.2 37 39.5 71 49.5 112 60.9 155 68.7 265 78.0 369 83.0 522 90.3 753

DATA ANALYSIS • No calculations required for this experiment. [Insert Plots Here]

Questions Answer the following questions below using complete sentences: 1. ࠵? -squared is one statistical measure of how close the plotted data are to the linear trendline. R - squared has a minimum value of 0 and a maximum value of 1. When the value is less than or equal to 0 .9 , the trendline’s connection to the data is qualified as being “weak”. When the value is greater than 0 .9 , the trendline’s connection to the data is qualified as being “strong”. For your first graph, qualify the trendline’s connection to the data. 2. ࠵?࠵?࠵?࠵? is another statistical measure of how close the plotted data are to the linear trendline. The lower its numerical value, the better its associated trendline matches the plotted data. Considering your second and third plots, which trendline best matches the data? 3. Below is a scanned image of a plot based on data collected by a student during a different experiment. It has been rotated into portrait orientation and scaled-down to a smaller size to fit within this question . Identify three (3) mistakes that the student made in creating this plot. i. ii. iii.

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