Error propagation in calculations

In summary, JakeRue found that if he calculates average velocity using the conversion coefficient of 0.00167 hrs/min then the error will be at 1% for both minutes and hours. However, when he calculates average velocity using the conversion coefficient of 1 km/hr then the error is at 1 km/hr.
  • #1
jakerue
5
0
This is an issue I am running into at the beginning of my physics course.

Homework Statement



Given distance and time in minutes, calculate the time in hours (part of a larger average velocity question) and graph over 170minutes. Include error bars in the graph

Homework Equations



So if tm = 10.0 +/- 0.1min and I use min -> hr conversion as 1hr/60min = 0.0167

th = 10.0min * 0.0167 hrs/min = 0.167 min

The Attempt at a Solution



The error in min is +/- 0.1 min. Now if I am using 0.0167hrs/min as an exact constant factor I should use z=k*x and [itex]\Delta[/itex]z= k [itex]\Delta[/itex]x

So I will use hours = 0.0167 * minutes and my [itex]\Delta[/itex]hours = 0.0167 * 0.1min making the [itex]\Delta[/itex]hours = 0.00167hours.

By sig fig this value is 0.002 hours correct?

At 10 minutes th = 0.167 +/- 0.002hrs

at 120 minutes th = 2.00 hrs +/- 0.002 hrs

I think I am right but I want to make sure my answer is correct and that this error holds true for all values of minutes from 0-170min.

Thanks for any help, most appreciated.

JakeRue
 
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  • #2
Looks good, JakeRue. Certainly 10±0.1 minutes = 0.167±0.00167 hr
and it makes sense to round the .00167 to .002.
Very likely your 2 hours is ±.002 as well, though you haven't given any information from which that can be determined. The ±0.1 minute was given in the question for the 10 minute time. Was it also given in the question for the 2 hours? In reality it would depend on how the time was measured. Always a good idea to post the question exactly as given so we can check your interpretation of it.
 
  • #3
Thanks for the reply Delphi51.

The only errors given in the question are with the minutes (+0.1 min) and with the distance (+0.1 km). The question asks to convert minutes to hours and then graph distance over time.

I then use the km/h to calculate average velocity and best fit line.

I am asking about the 2 hours because the minutes is given in the form of 10.0min and then 120.0 min (3 sig fig, then 4 sig fig). If I calculate the error using only the conversion coefficient of 0.00167 * [itex]\Delta[/itex]tm then the error would stay the same no matter if the sig fig is 3 digits or 4 digits - correct?

What is tripping me up with the error propagation is that with minutes having a + of 6 seconds, or 10% error, the hour then has +0.00167 which is an error of less than 10% of the hour (I could be wrong here).

I don't understand how can I get less error when I multiply by a unit conversion coefficient (1/60)?
 
  • #4
The error is fixed at 0.1 minute for both 10 minutes and 120 minutes, so certainly the % error will be 12 times less for the 120 minutes. But the size of the error bars will be the same - 0.1 minute for all times.

Changing the units did not change the % error. You had .1 minute per 10 minutes, which is 1%. It converted to .00167 hr per .167 hr, which is also 1%.

How will you calculate the average velocity? You could draw a line of best fit and its slope would be the average velocity. Or just use the last point (total distance and total time).
 
  • #5
OK I can see the error being at 1% for both minutes and hours. So converting 120.0min +0.1min using the 0.0167 hrs/min conversion coefficient will give me 2.000 hours +0.002hrs.

I was doing velocity by rise/run from two points (does it matter which two I choose since it won't be the actual value?) and getting y-intercept from that.

When I get my slope I think I must multiply the error of both the time in hours (0.1km) and distance in km (0.002) to get a final average velocity.

(209.0 - 17.5km)/(2.00 - 0.167hrs) = 191.5km/1.833 hrs = 104.5km/hr and the error is

∆average velocity =[velocity] [([itex]\Delta[/itex]time/time + [itex]\Delta[/itex]distance/distance)]
∆average velocity = 104.5 [(0.0167/1.833hrs) + (0.1/191.5km)] = 104.5(0.00911+0.000522) = 104.5*0.009632

∆average velocity = 1.006544 then sig fig brings it to 1km/hr

So final average velocity = 104.5km/hr +1 km/hr
 

What is error propagation in calculations?

Error propagation is the process of determining how uncertainties in the input values of a calculation affect the uncertainty in the final result. This is important because all measurements and calculations are subject to some degree of error, and understanding the propagation of these errors is crucial for accurate and reliable scientific results.

Why is error propagation important in science?

Error propagation allows scientists to quantify the uncertainty in their measurements and calculations, which is crucial for determining the reliability and accuracy of their results. It also helps identify which sources of error are most significant and where improvements can be made in the measurement or calculation process.

What factors contribute to error propagation?

There are several factors that can contribute to error propagation, including the precision and accuracy of the measuring instruments, the variability of the measured quantity, and the mathematical relationships between the input values and the final result. Other factors such as human error and environmental conditions can also play a role.

How is error propagation calculated?

The most common method for calculating error propagation is through the use of the "error propagation formula," which uses partial derivatives to determine the uncertainty in the final result based on the uncertainties in the input values. This formula can be applied to simple equations as well as more complex mathematical models.

What are some limitations of error propagation?

While error propagation is a valuable tool for understanding and quantifying uncertainties in scientific calculations, it does have some limitations. For example, it assumes that all input uncertainties are independent of each other, which may not always be the case. It also does not account for systematic errors, which can significantly impact the accuracy of results. Additionally, it is only as accurate as the input values and their uncertainties, so it is crucial to minimize these uncertainties as much as possible through careful measurement and data collection methods.

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