Statistics: How to differentiate Type A and B uncertainty?

In summary, the equation Q= kL(h^n) results in a linear relationship between Q and h, where n represents the slope of the line. The calculation of n can be done by plotting ln (U(E)) against ln (U(D)).
  • #1
Joon
85
2
Homework Statement
For the task, I was given a flow rate equation Q= kL(h^n), where k is a constant and L (width of rectangular weir) was set to 0.625 ft. A table with Q values and corresponding h' and h values was provided, please refer to the picture attached. h in the equation= h'-h in the table.

There are two variables in the equation, Q and h, and I need to determine the experimental value of n. I have learnt that for Type A uncertainty statistical calculations should be done and for Type B the least square method. After I calculate the Type A and B uncertainties, I will be able to calculate n.

I think Q is the Type B one whose uncertainty can be calculated using the least square method but I am not sure. By definition, Type A is based on the statistical analysis of measurements and Type B is based on other sources of information such as data book values.

Could someone help me clarify this?
Relevant Equations
Q = kL(h^n)
I tried to find examples on the internet but I am still confused.
 

Attachments

  • Data given.png
    Data given.png
    5.5 KB · Views: 234
Physics news on Phys.org
  • #2
Joon said:
There are two variables in the equation, Q and h, and I need to determine the experimental value of n. I have learned that for Type A uncertainty statistical calculations should be done and for Type B the least square method. After I calculate the Type A and B uncertainties, I will be able to calculate n.
<snip>
Could someone help me clarify this?
Relevant Equations: Q = kL(h^n)
I have taught Statistics a number of times, but I don't recall the books talking about Type A and Type B uncertainties in this way. Can you elaborate a bit more on what a Type A uncertainty is? Saying that "uncertainty statistical calculations should be done" doesn't shed any light on whatever technique this type of uncertainty entails.

In any case, I'd probably want to use a least squares technique. Taking the natural log of both sides of your equation yields ##\ln Q = \ln k + \ln L + n\ln h##, with ##\ln k## and ##\ln L## being known values. This represents a linear relationship between ##\ln Q## and ##\ln h##, where ##n## represents the slope of the line.
 
  • #3
Thank you for your reply.

An example calculation was provided and I was told to use the same method for this task.

The example given was a crater created when a ball strikes sand from a vertical height h.
A table of results and additional information was provided- please refer to the attached file.
What was done for the example: from the equation D= cE^n, to find n, uncertainty calculations were made.

The calculations for U(E) and U(D) are shown in the files attached. As you can see from them, the calculation of U(E) is based on data book values and given data (scales etc) whereas the calculation of U(D) is based on statistical calculations.

A graph was then plotted with ln (U(E)) against ln (U(D)) to find the slope n.
In this example, D was Type A repeated measurements.

So, using the same method as the one used in this example, how should I start doing the calculations?
I was going to do something similar, from the equation Q= kL(h^n), I was thinking of seeing Q analogous to E in the example and h analogous to D in the example.
 

Attachments

  • Example.png
    Example.png
    130.5 KB · Views: 244
  • U(E) 1.png
    U(E) 1.png
    49.8 KB · Views: 231
  • U(E) 2 and U(D).png
    U(E) 2 and U(D).png
    54.1 KB · Views: 215

1. What is the difference between Type A and B uncertainty?

Type A uncertainty refers to the random error or variability in data that can be reduced by taking multiple measurements. Type B uncertainty, on the other hand, is due to systematic errors or biases that cannot be reduced by taking more measurements.

2. How can Type A and B uncertainty be differentiated?

Type A uncertainty can be differentiated by calculating the standard deviation or standard error of the mean from multiple measurements. Type B uncertainty can be differentiated by identifying and quantifying any potential sources of bias in the data.

3. Is one type of uncertainty more important than the other?

Both Type A and B uncertainty are important to consider in statistical analysis. However, Type B uncertainty is often more difficult to quantify and can have a larger impact on the overall uncertainty of the data.

4. Can Type A and B uncertainty be reduced?

Type A uncertainty can be reduced by taking more measurements and using statistical methods to calculate the mean and standard deviation. Type B uncertainty can be reduced by identifying and minimizing potential sources of bias in the data collection process.

5. How can Type A and B uncertainty affect the interpretation of statistical results?

Type A and B uncertainty can both affect the precision and accuracy of statistical results. It is important to consider and report both types of uncertainty to accurately interpret the significance and reliability of the data.

Similar threads

  • Calculus and Beyond Homework Help
Replies
26
Views
898
  • Set Theory, Logic, Probability, Statistics
Replies
28
Views
2K
  • Introductory Physics Homework Help
Replies
4
Views
256
  • Calculus and Beyond Homework Help
Replies
3
Views
1K
  • Calculus and Beyond Homework Help
Replies
4
Views
1K
  • Calculus and Beyond Homework Help
Replies
10
Views
477
  • Calculus and Beyond Homework Help
Replies
7
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
1K
Replies
1
Views
599
  • Calculus and Beyond Homework Help
Replies
4
Views
944
Back
Top