How to Linearize Data for Solar System Motion Analysis?

In summary, the conversation discusses the process of linearizing a data set that includes information about the motion of objects in the solar system. The data includes the periods of these objects (listed in Earth years) and their average distances (reported in astronomical units). After attempting to plot the data and linearize it, the speaker is stuck and unable to determine the relationship between the period and average distance. They then mention using logarithmic scales on the axes to potentially help with this task.
  • #1
Faye716
3
0
Hi,
I'm supposed to linearize this set of data:

"Below is a data set which includes information about the motion of the objects in the solar system. Note: the periods are listed in Earth years (time it takes the Earth to complete one orbit around the Sun) and the average distances are reported in astronomical units (1 au is a an average distance from Earth to the Sun)."

Period (yrs)

0.241

0.615

1.00

1.88

11.8

29.5

84.0

165

248

Average Distance (au)

0.39

0.72

1.00

1.52

5.20

9.54

19.18

30.06

39.44

So I plotted it, and it turned out looking like this:
upload_2017-8-24_20-29-36.png

Since this looks like a square root graph to me, I took the square root of the data on the horizontal axis and plotted that. However, that ended up looking like a quadratic. In order to linearize that I would generally square the data on the horizontal axis, however that would just give me the original data again. I am stuck and do not know what to do.

After I graph and linearize it, I am supposed to "Create the mathematical model (equation) that describes the relationship between the period of an object and its average distance from the Sun. Show all work." and since I cannot linearize the data I do not know the relationship between the Period and Average distance.

Thanks in advance for the help!

Ps: Sorry if this is in the wrong forum I was not sure where to put it
 
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  • #2
Change the scales on the axes to logarithmic scales. This can be done automatically by your graphics program.
 
  • #3
If the teacher has not told you about the law, and he wants you to find it out for yourself ...
This law is one of the great achievements of the human mind. Look at my avatar. (I can not help you more without spoiling the task)
 
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Likes epenguin
  • #4
PERIOD.png
 

1. How do I determine if my data needs to be linearized?

To determine if your data needs to be linearized, you can plot it on a graph and see if the points form a straight line. If the points appear to follow a curve or are scattered, then your data may need to be linearized. You can also perform a statistical test, such as the Pearson correlation coefficient, to determine the linearity of your data.

2. What are some common methods for linearizing data?

Some common methods for linearizing data include taking the logarithm or square root of the data, using a power transformation, or fitting a curve to the data using a regression model. Another method is to plot the data on a log-log scale, which can help reveal any underlying linear relationships.

3. How do I choose the best method for linearizing my data?

The best method for linearizing data will depend on the specific characteristics of your data. It is important to consider the shape and distribution of your data, as well as the purpose of your analysis. You may need to try different methods and compare the results to determine which one works best for your data.

4. Can I use linear regression to linearize my data?

Linear regression can be used to fit a straight line to your data, but it is not always the best method for linearizing data. If your data is highly nonlinear, then a linear regression model may not accurately capture the relationship between the variables. It is important to assess the linearity of your data before deciding on a method for linearization.

5. What are some potential limitations of linearizing data?

Linearizing data can help make relationships between variables more apparent and easier to analyze, but it is not always a perfect solution. Some potential limitations of linearizing data include the loss of information or accuracy, as well as the potential for misinterpretation of the results. It is important to carefully consider the implications of linearizing data before applying it to your analysis.

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