Help calculating uncertainty of slope & intercept of cal. curves. uncertainty ?s

In summary, the individual is seeking assistance in calculating error/uncertainty for their undergraduate thesis. They are specifically looking for help with calculating uncertainty in the slope and intercept of calibration curves and in a standard tank. They have attempted to use standard deviation, but their professor has suggested using the data analysis add-in for excel. By selecting Tools/Data Analysis... and choosing Regression, the individual can find the desired numbers under "Coefficient" and "Standard Error" in one of the tables that appears.
  • #1
mroldboy
7
0

Homework Statement



I need assistance in learning the proper way to calculate error/uncertainty in a few things for my undergrad thesis.

Homework Equations



1) how to calculate the uncertainty in the slope and intercept of calibration curves (peak area vs mol of compound) I have made via excel.
2) help calculating the uncertainty in a standard tank.

The Attempt at a Solution



I can post my excel files, but my basic attempt at this has been calculating thinds like stdev and others, but I have done this before in a course. What I may bee missing is some plugin for excel. My professor mentioned that I should use the data analysis add-in for excel. I installed this, but am unsure of how to use it.

Sorry if this is not what is typically asked here. The template makes it seem like homework questions should be posted here.
 
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  • #2
In excel, select Tools/Data Analysis..., then choose Regression in the dialog. Now select your x and y data for the "Input X range" and "Input Y range", check anything else you want (experiment with this), and click OK. The numbers you want are under "Coefficient" and "Standard Error" in one of the tables that appears.
 
  • #3
pmsrw3 said:
In excel, select Tools/Data Analysis..., then choose Regression in the dialog. Now select your x and y data for the "Input X range" and "Input Y range", check anything else you want (experiment with this), and click OK. The numbers you want are under "Coefficient" and "Standard Error" in one of the tables that appears.

I do remember this now. Thanks, I found this earlier by playing around with the data analysis. But was looking for uncertainty when all I wanted was standard error really.
 

What is the purpose of calculating uncertainty of slope and intercept for calibration curves?

The purpose of calculating uncertainty for calibration curves is to determine the accuracy and precision of the curve. This information is important for determining the reliability of the data and making informed decisions based on the results.

What factors contribute to the uncertainty of slope and intercept for calibration curves?

The factors that contribute to the uncertainty of slope and intercept for calibration curves include measurement errors, instrument limitations, and variability in the data. Other factors may also include environmental conditions and human error.

How is uncertainty of slope and intercept calculated for calibration curves?

The uncertainty of slope and intercept for calibration curves is typically calculated using statistical methods such as linear regression analysis. This involves plotting the data points on a graph and using mathematical formulas to determine the best fit line, and then calculating the uncertainty based on the variability of the data points around the line.

How can uncertainty of slope and intercept be reduced for calibration curves?

To reduce the uncertainty of slope and intercept for calibration curves, it is important to minimize sources of error and variability in the data. This can be achieved by improving measurement techniques, using more precise instruments, and increasing the number of data points to improve the accuracy of the curve.

How does calculating uncertainty for calibration curves impact the overall interpretation of the data?

The uncertainty of slope and intercept for calibration curves is an important factor in the overall interpretation of the data. It provides information about the reliability and accuracy of the curve, and can affect the confidence level in the results. A larger uncertainty may indicate a need for further analysis or adjustments to the experimental procedure.

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