Solving Error Analysis for Acceleration: Troubleshooting with Simple Formula

In summary, the two errors are always positive and add together, just like the standard deviation of a population.
  • #1
flower76
51
0
I'm having a problem with something I know should be simple, but my answer is off so I'm doing something wrong.

I need to find the amount of error for an acceleration that was found using the formula a=2d/t^2. Where d represents distance travelled. There is no uncertainty in the distance measurement, only the time.

Could someone please help.
 
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  • #2
[tex]\Delta a=\left|\Delta \left(\frac{2d}{t^{2}}\right)\right| =4dt^{-3} \Delta t[/tex]

Daniel.
 
  • #3
dextercioby said:
[tex]\Delta a=\left|\Delta \left(\frac{2d}{t^{2}}\right)\right| =4dt^{-3} \Delta t[/tex]

Daniel.
Just in case you're also interested in the SIGN of the error "Δa" in "a" for a given error "Δt" in "t":

[tex] 1: \ \ \ \ \Delta a \ = \ \Delta \left(\frac{2d}{t^{2}}\right) \ = \ \left ( \frac{\color{red} \mathbf{-} \color{black} 4d}{t^{3}} \right ) \Delta t [/tex]


~~
 
  • #4
There are no such things as negative errors.Errors always add...

I'm not interested in that minus...

Daniel.
 
  • #5
Yes, reported errors are standard deviations (or they should be), and hence are always positive (the definition of standard deviation of [itex]X[/itex] is [itex]\sqrt{\mbox{Var} X}[/itex]).
 
  • #6
dextercioby said:
There are no such things as negative errors.Errors always add...

I'm not interested in that minus...

Daniel.
The term "error" alone can be ambiguous. "Standard Deviation" and "Variance" are much more specific, and they are always positive and always "add":

[tex] 1: \ \ \ \ \ \ \ \color{blue}\mbox{Var(a)}\color{black} \ = \ \overline { \left ( \Delta a \right )^{2}} \ = \ \overline{ \left ( \Delta \left(\frac{2d}{t^{2}}\right) \right )^{2} }\ = \ \left ( \frac{-4d}{t^{3}} \right )^{2} \overline{ \left ( \Delta t \right )^{2} } \ \ + \ \ \left ( \frac{2}{t^{2}} \right )^{2} \overline{ \left ( \Delta d \right )^{2} } [/tex]

[tex] : \hspace{9cm} \left ( For \ \ \overline{\Delta a} = \overline{\Delta t} = \overline{\Delta d} = \overline{\Delta t \Delta d} = 0 \right ) [/tex]

[tex] 2: \ \ \ \ \color{red}(\mbox{Standard Deviation})\color{black} \ = \ +\sqrt{\color{blue} \mbox{Var(a)}} [/tex]


The question here is what the OP had in mind. (We don't know what the OP originally meant by the term in Msg #1.) You :wink: may not be interested in the (-) sign, but the OP might have been ... thus the clarification in Msg #3.


~~
 

FAQ: Solving Error Analysis for Acceleration: Troubleshooting with Simple Formula

What is a simple error analysis problem?

A simple error analysis problem is a type of mathematical problem that involves calculating and analyzing the errors or uncertainties associated with measurements or experimental data. It typically involves identifying the sources of error and determining their magnitude and impact on the final result.

Why is error analysis important in science?

Error analysis is important in science because it allows us to evaluate the reliability and accuracy of our experimental results. By understanding the sources of error and their potential impact, we can improve the experimental design, make more accurate conclusions, and identify areas for further investigation.

What are the common types of errors in a simple error analysis problem?

The common types of errors in a simple error analysis problem include random errors, systematic errors, and human errors. Random errors are caused by unpredictable variations in measurements, while systematic errors are consistent and repeatable inaccuracies in the measurement process. Human errors are mistakes made by the experimenter, such as reading a measurement incorrectly or using faulty equipment.

How do you calculate the total error in a simple error analysis problem?

The total error in a simple error analysis problem is calculated by adding all the individual errors together. This can be done using the root sum square (RSS) method, where the square root of the sum of the squares of each error is taken. Alternatively, the total error can be calculated by finding the absolute value of the difference between the measured value and the accepted value.

How can we reduce errors in a simple error analysis problem?

There are several ways to reduce errors in a simple error analysis problem. These include using precise and accurate measuring instruments, taking multiple measurements and calculating the average, minimizing human error by carefully following procedures, and identifying and accounting for potential sources of error in the experimental design. Conducting repeated experiments and comparing results can also help to reduce errors and increase the reliability of the data.

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