Help creating a specific smooth curve

In summary, the conversation discusses creating a smooth non-piecewise curve in 3-space with an intersection on the xy-plane consisting of integer coordinates. It also mentions the possibility of creating this curve in a rectangular sub-section of the xy-plane. Ideas are presented involving a two-variable function and a diagonal square curve, but no algebraic representation for a three-dimensional curve is found. Additional help is requested.
  • #1
StillNihilist
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Note: If this is the wrong sub-forum for this question please move it. I was not sure if this question should go in the General section or not.

Question:
I want to create a smooth non-piecewise curve in ℝ[itex]^{3}[/itex] (3-space) such that it's intersection with the xy-plane consists of the integer coordinates Z[itex]^{2}[/itex]. If this is impossible for the entire xy-plane, I'd like to be able to create such a curve with that intersection property for some arbitrary rectangular sub-section of the xy-plane.

I can visualize such a curve as being like a thread weaving up and down through the integer coordinates, either spiraling out from some initial point or maybe weaving back and forth across the grid. Furthermore, it's rather easy to do this for only 2-dimensions, for example, the curve c(t) = <t, sin([itex]\pi[/itex]t)>, works for two dimensions. However I can't find an algebraic representation of such a curve in 3-dimensions.

Any help is extremely appreciated. Thank you for your time and consideration. I hope you have a great day.
 
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  • #2
Not sure how much help this will be, just a couple of thoughts. If you take the two variable function ##f(x,y) = \sin(\pi x)\sin(\pi y)##, that surface is zero along lines ##x=n## and ##y=m## (integers), and nowhere else. Now think about the curve ##|x|+|y| = k## for some natural number ##k##. That will pass through integer coordinates forming a diagonal square. If you restrict the surface ##z=f(x,y)## to that square domain, you will get such a curve. It may not be smooth, and it doesn't solve your problem, but here's what it looks like for ##k=3##:
thread.jpg
 

1. How can I create a smooth curve from my data points?

There are several methods for creating a smooth curve from data points, including polynomial regression, cubic splines, and moving average smoothing. Each method has its own advantages and limitations, so it is important to choose the one that best fits your data and research goals.

2. Can I use Excel to create a smooth curve?

Yes, Excel has built-in tools for creating smooth curves from data points. These include the Trendline function and the Smoothed Line chart type. However, these methods may not always produce the most accurate results, so it is important to understand the limitations of using Excel for curve creation.

3. How do I choose the right degree for my polynomial regression curve?

The degree of a polynomial regression curve refers to the highest exponent in the equation. It is important to choose the degree carefully, as a higher degree may overfit the data and result in a less accurate curve. Generally, a degree of 2 or 3 is sufficient for most data sets, but it is best to experiment with different degrees and compare the results.

4. What is the difference between a smooth curve and a line of best fit?

A smooth curve is a line that is drawn through data points to represent the overall trend of the data. It is typically used for visualizing data and making predictions. A line of best fit, on the other hand, is a straight line that minimizes the distance between the line and all data points. It is used to find the relationship between two variables and make precise predictions.

5. Is it possible to create a smooth curve without removing outliers?

Yes, it is possible to create a smooth curve without removing outliers. However, the curve may not accurately represent the overall trend of the data if there are significant outliers. It is important to carefully consider whether to remove outliers or not, depending on the purpose of the curve and the nature of the data set.

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