RMS uncertainty using a Trend Line

In summary: N.In summary, to find the acceleration of gravity, we used the trendline equation and the data values to calculate the slope of the line, which is the acceleration of gravity. To find the RMS uncertainty for the force, we used the formula for RMS to calculate the average of the force values, and then took into account the uncertainty in the slope of the trendline to calculate the final RMS uncertainty for the force.
  • #1
salal
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Homework Statement



Find the acceleration of gravity. Using the trendline equation of Force v. hanging mass, find the RMS uncertainty for the force.

Trend line equation: y = 9.8968x - 0.0342 R² = 0.9493

Data Values:

Hanging mass is the first number. Force is the second number.
0.40 3.839928
0.39 3.690708
0.37 3.720552
0.35 3.720552
0.30 2.765544
0.20 1.949808


Homework Equations



F=ma
RMS = Sqrt((X1)2+(X2)2+(X3)2+...+(XN)2/N)


The Attempt at a Solution



I got 9.8968 as the accleration of gravity.

I got 3.35414 as the RMS of the Force values.

I also got 6.566915632 as the RMS of the Force-Force (trendline)

I just don't understand how to calculate the RMS uncertainty of force. I don't know what numbers should i use.

Thanks
 
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  • #2
for your post! It seems like you are on the right track with your solution. Let me provide some clarification on how to calculate the RMS uncertainty for the force.

First, let's define the terms used in the problem. The trendline equation provided is a linear equation of the form y = mx + b, where y is the dependent variable (force), x is the independent variable (hanging mass), m is the slope of the line (acceleration of gravity), and b is the y-intercept. The R² value represents the goodness of fit of the trendline to the data, with a value of 1 indicating a perfect fit.

To calculate the acceleration of gravity (m), we can rearrange the trendline equation to solve for m: m = (y - b)/x. Plugging in the values from the data points given, we get m = (3.839928 - (-0.0342))/0.40 = 9.8968 m/s². This is the same value you obtained.

Now, let's focus on calculating the RMS uncertainty for the force. RMS stands for root mean square, and it is a way to calculate the average of a set of values. In this case, we are interested in finding the average of the force values given in the data. The formula for RMS is RMS = √((X1²+X2²+X3²+...+XN²)/N), where X1, X2, X3, etc. are the individual force values and N is the total number of values.

Using the data given, we can plug in the values for X1, X2, X3, etc. and calculate the RMS for the force. This gives us RMS = √((3.839928²+3.690708²+3.720552²+3.720552²+2.765544²+1.949808²)/6) = 3.35414 N.

Finally, to calculate the RMS uncertainty for the force, we need to take into account the uncertainty in the slope of the trendline (m) as well. This uncertainty is represented by the RMS of the Force-Force (trendline) value you calculated, which is 6.566915632 N. Thus, the RMS uncertainty for the force is calculated as RMS uncertainty = √(RMS² + (m x RMS slope)²) =
 

1. What is RMS uncertainty?

RMS uncertainty, or root mean square uncertainty, is a measure of the overall error or variability in a set of data points. It takes into account both the magnitude and direction of the errors, making it a more comprehensive measure compared to other types of uncertainty.

2. How is RMS uncertainty calculated?

RMS uncertainty is calculated by taking the square root of the sum of squared differences between each data point and the trend line. This is then divided by the number of data points to get an average value. The result is the RMS uncertainty.

3. What is the significance of using a trend line in calculating RMS uncertainty?

A trend line is used to represent the general trend or pattern in a set of data points. By using a trend line, we can better understand the relationship between the data points and identify any outliers or discrepancies. This helps to improve the accuracy of the RMS uncertainty calculation.

4. How is RMS uncertainty different from standard deviation?

Standard deviation measures the spread of data points around the mean, while RMS uncertainty takes into account the relationship between the data points and a trend line. Standard deviation can be affected by outliers, while RMS uncertainty is more robust and less influenced by extreme values.

5. What are some potential sources of error in calculating RMS uncertainty using a trend line?

Some potential sources of error in calculating RMS uncertainty using a trend line include inaccuracies in the data points, incorrect assumptions about the relationship between the data points and the trend line, and the choice of the trend line equation. It is important to carefully select and validate the trend line to minimize errors in the calculation.

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