What is Causing the Exponential Curve in My Velocity Squared vs. Radius Graph?

In summary: So the fact that it is linear is actually a good sign.In summary, the conversation is about a student encountering a problem with the velocity squared vs. radius graph in their physics lab report on centripetal force. Despite the expectation of a root curve, the regression utilities used produce an exponential curve. The student also notes that their velocity vs. radius graph, which should also show a root curve, actually does. After discussing the issue, it is determined that the relationship between velocity squared and radius is actually linear due to the constant ratio of mass and centripetal force.
  • #1
FireLight07
3
0
Hey--

I'm writing up a physics lab report on centripetal force; at the moment I've hit a problem with the velocity squared vs. radius graph. The graph *should* show a root curve (v^2 = Fr/m) but all of the regression utilities I've used churn out an exponential curve. Here are the four points I have:

Radius (m)
0.25
0.5
0.75
1

Velocity Squared (m^2/s^2)
43.52321446
105.3350938
174.1416131
227.3593065

Adding to the enigma is the fact that my velocity vs. radius graph, which should also show a root curve, actually DID. All I did to the velocities was square them for the v squared graph.

Any idea what's going on?
Thanks!

Pat
 
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  • #2
Are you sure you kept F constant for all those measurements?
 
  • #3
I think you are making too much of this. The graph of [itex]v^2\ vs\ r[/itex] looks quite linear to me. If you analyze data with numerical facilities you can end up with all sorts of wonderful relationships between the variables. Such facilities are used when the relationship between the variables are complicated and cannot be described easily. We tend to use the simplest relationships that seems to fit the data, which in this case is linear. Your analysis may show effect of drag on the velocity though, since the velocities are getting quite high.
 
  • #4
You're right, actually, it WAS linear. Turns out I'm looking at v^2 as a single variable, which makes the relation a simple direct proportion. :-) Thanks for the help!

Pat

(BTW: Force was constant throughout, so it wasn't a factor...we rigged up a very rough centripetal force apparatus with some string and a straw and some other stuff, so it isn't very accurate--but force was constant. :-p )
 
  • #5
Actually, the fact that the graph seems to be linear implies that you kept the ratio of the mass and centripetal force constant, otherwise it would not have been linear.
 
Last edited:

What are root regression problems?

Root regression problems refer to statistical models that aim to predict the relationship between a dependent variable (usually denoted as 'y') and one or more independent variables (usually denoted as 'x'). The term 'root' in this context refers to the root mean square error (RMSE) used to measure the accuracy of the model.

What is the difference between root regression and linear regression?

Root regression is a type of regression analysis that involves using a mathematical model to predict a continuous dependent variable. Linear regression, on the other hand, is a specific type of root regression that assumes a linear relationship between the dependent and independent variables.

What are the common assumptions made in root regression?

Some common assumptions made in root regression include linearity (the relationship between variables can be described by a straight line), homoscedasticity (the variability of the dependent variable is constant across all values of the independent variable), and normality (the residuals follow a normal distribution).

What is the root mean square error (RMSE) and how is it used in root regression?

The root mean square error (RMSE) is a measure of the difference between the predicted values of the dependent variable and the actual values. It is calculated by taking the square root of the average of the squared differences between the predicted and actual values. In root regression, the goal is to minimize the RMSE to create the most accurate model.

What techniques can be used to improve the accuracy of root regression models?

Some techniques that can be used to improve the accuracy of root regression models include data preprocessing (such as handling missing values and outliers), feature selection (choosing the most relevant independent variables), and model selection (choosing the best model among different types of root regression models).

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