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Drift-corrected quadratic calibration. Click to see larger figure. Cubic calibration. Log-log calibration. It will be best if you get appropriate headings for the data columns. If the data requires a specific order, you should sort by one of the columns. You can have your data arranged in X-value and Y-value columns. The next step involves creating the calibration curve.
To create the curve, you should start by highlighting all the data that you wish to use. First, select the column cells for the x-value.
You can click on the heading in the right-hand column of the data labeled as x. Left-click and hold the mouse down and drag the mouse pointer down to the last cell in the column.
Next, click on the Ctrl key and select the column cells containing the y-value. Drag the mouse pointer down to the last cell in the column. Next, go to the "Insert" tab on the Microsoft Excel sheet. When the option is open, navigate to the "Charts" menu and then go to the "Scatter" dropdown. When you click on the Scatter icon, it will open to display a variety of scatter chart variations.
Go to the "Markers Only" option to get a scatter chart with a marker representing each data pair. When you open the scatter option, you'll get a chart with data points from the x and y columns.
For example:. Next, you should click on one of the blue points to select the series. Once the series is selected, Microsoft Excel will outline the points. You should select one of the outlined points and then click on "Add Trendline" from the dropdown menu.
You'll get a straight line that will appear on the chart. Next, you'll get a "Format Trendline" menu that will appear on the right side of the screen. On the menu, you should check the boxes on "Display R-squared value on chart" and "Display Equation on chart. The R-squared is a static value that informs you of how closely the trendline fits the data.
The best R-squared static value is 1. As the variance between the data points and the line grows, the R-squared value drops. The independent data is plotted on the x-axis, whereas the dependent data is plotted on the y-axis, on a scatter plot.
For this example, I will use data generated from the BCA protein assay kit to estimate total protein concentrations. Here is the data:. What we have here is the average absorbances of each standard next to the corresponding known concentrations.
In this instance, before we can proceed we need to first correct the data for the background noise. This will give background-corrected absorbance values. See the end column in the above image. The data is now good to create a standard curve in Excel. Hopefully a scatter plot will now be displayed. We next need to create a line of best fit through each of the points in the graph to create the standard curve.
Your graph should now have a linear straight line fit running through it. Hopefully it passes through most of your points.
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