Chem 1A Scientific Graphing Lab Scientific Graphing Lab Objectives: 1

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Chem 1A Scientific Graphing Lab Scientific Graphing Lab Objectives: 1. To become more proficient at using Excel to process scientific data 2. To develop proper graphing technique using Excel Introduction: One of the most important aspects of being a successful scientist is the ability to clearly and concisely display information. Most data obtained in a laboratory setting comes in the form of mixed data points, such as those seen in the table below (Table 1). Table 1 shows how much carbon dioxide (CO2), a notorious greenhouse gas, is in the air at the Mauna Loa collection site in Hawaii as a function of month in 2012. Table 1: Concentration of CO2 by month Month CO2 (in ppm) January 393.07 February 393.34 March 394.37 April 396.45 May 396.87 June 395.88 July 394.52 August 392.54 September 391.14 October 391.02 November 392.99 December 394.39 Judging from this table, some general trends can be identified, such as the highest value of CO2 in the atmosphere is in the month of May. However, to better visualize trends, a graph of CO2 concentration versus month may be clearer. Graph 1 below is a visualization of the above data. Chem 1A Scientific Graphing Lab CO2 concentration (ppm) Concentration of CO2 in the atmosphere at the Mauna Loa site in 2012 versus month 398 397 396 395 394 393 392 391 390 1 3 5 7 9 11 Month (numerically assigned) Graph 1: The concentration of CO2 at the Mauna Loa site versus the month the measurement was recorded is displayed for 2012. The previous graph highlights a few points that are true in all graphs (hand or computer): • The graph consists of two axes at right angles. The horizontal axis is referred to as the xaxis. The vertical axis is referred to as the y-axis. • The x-axis displays the variable that a scientist directly manipulates in an experiment (typically called the independent variable). The y-axis displays the variable that changes as a function of the experiment (typically called the dependent variable). Always include units in any graph you create. A number without units is mostly meaningless in chemistry. • The title of the graph gives all relevant information pertaining to what is displayed in the graph. When instructed to make a graph, the y-axis is listed first, followed by the xaxis. (i.e. Concentration of CO2 vs. Month). • Choose a scale for the x and y axes that allow for a maximum portion of the graph to be used. Note: you do NOT have to start a graph at zero and the x and y axes do not have to have the same scale. In this lab, there are two types of graphs that we will be making: smooth curve plots and linear plots. Graph 1 is an example of a smooth curve plot. Smooth curve plots draw a smooth curve through the data; do not play connect the dots with straight lines between the data points. A linear plot is better represented if we take a step back and look at the concentration of CO2 at Mauna Loa over a longer time period than just one year. Graph 2 below shows data from 19802012. Remember that this data comes from a huge data set, much, much larger than table 1. Imagine trying to visualize the below graph if just given the data points! Chem 1A Scientific Graphing Lab Concentration of CO2 in the atmosphere at the Mauna Loa site between 1980-2012 versus month CO2 concentration (ppm) 400 390 380 370 360 y = 0.142x + 335.98 R² = 0.9767 350 340 330 0 50 100 150 200 250 300 350 400 450 Month (numerically assigned) Graph 2: The concentration of CO2 at the Mauna Loa site versus month over the period from 1980-2012 shows a linear trend. Linear graphs are represented by individual "scatter plot" points that are then connected by a trendline (the black straight line in graph 2). The trendline is crucial in establishing a relationship between your independent and dependent variables. A computer graphing program can be used to create a trendline and the corresponding linear equation. This equation takes the form of y = mx + b, where m is the slope of your line, and b is the y-intercept of the graph. This equation allows you to calculate any y-value given any x-value. Additionally, the computer program generates an R2 value that describes how well the linear equation fits your experimental data. An R2 value close to 1 indicates an excellent linear fit, whereas an R2 value