Predicting the Shape of Complicated Functions

In summary, there is a method for drawing graphs in 3D using level sets and cross sections, but confusion may arise when given a function like e^{-x^2} or sin(x^2). Tricks for quickly drawing these graphs without going through the curve sketching process in single variable calculus are not known. The first function approaches zero in a concave down manner, contrary to what one might expect. The curvature of the graph changes from negative to positive as you move away from x=0. The second function is an even function with zeroes at x=\sqrt{n\pi}. The properties mentioned in the first descriptions were not simply stated, but rather a way of thinking about the functions. The purpose of this conversation is unclear.
  • #1
the_dialogue
79
0
This isn't a homework question-- it is a general question about curve sketching in multivariable calculus.

In order to draw graphs in 3D, we use level sets, then-- if the level sets are something "nice" like circles or parallel lines-- we take a cross section when y=0 and then transform the cross section on to the level sets.

However, it is at the stage of the cross sections where I begin to be confused. We are given a function such as [tex]e^{-x^2}[/tex]. I do not already know what this function looks like.
What is a quick way to draw this graph (or any other) without going through the whole curve sketching process (learned in single variable calculus)?
Are there any tricks to drawing graphs quickly that you don't already know?
Another example is [tex]sin(x^2)[/tex]. (I already know what this looks like-- but how would I figure it out quickly if I didn't?).

Thank you.
 
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  • #2
The first one: "Something that goes to zero dreadfully fast to each side, starting at x=0 with the value 1"

Second: Even function that oscillates faster and faster. The zeroes of the function is given by [itex]x=\sqrt{n\pi}[/itex] where n is some whole number.
 
  • #3
For the first one, you would think that it approaches zero in the same way that [tex]e^{-x}[/tex] does-- i.e. concave up.
However, in reality, it is concave down. How do you explain this without taking the second derivative?
 
  • #4
Well, you see that in a region around x=0, the curvature of the graph is negative, whereas further out on either side, the curvature changes sign and becomes positive.

So you are incorrect in believing it is always negative.
 
  • #5
I know that it changes... however, it still doesn't look like [tex]e^{-x}[/tex].
And, referring to your first descriptions, my question was how you think of those properties-- I didn't want you to just tell me the properties.
 
  • #6
1. I don't know what YOU mean by "look like"
2. I don't see the point of this silly thread.
 

FAQ: Predicting the Shape of Complicated Functions

1. What is the purpose of predicting the shape of complicated functions?

Predicting the shape of complicated functions allows us to understand the behavior and patterns of complex data. It can help us make accurate predictions and analyze relationships between different variables.

2. What methods are commonly used to predict the shape of complicated functions?

Some common methods used to predict the shape of complicated functions include regression analysis, machine learning algorithms, and data visualization techniques.

3. How do scientists determine the accuracy of predicted shapes for complicated functions?

Scientists typically use statistical measures such as root mean square error and coefficient of determination to assess the accuracy of predicted shapes for complicated functions. These measures compare the predicted values to the actual values of the data.

4. Can the shape of complicated functions change over time?

Yes, the shape of complicated functions can change over time due to various factors such as external influences and changes in underlying patterns. It is important to continuously analyze and update predictions to account for these changes.

5. How can predicting the shape of complicated functions be applied in real-world situations?

Predicting the shape of complicated functions has various real-world applications, such as predicting stock market trends, weather patterns, and customer behavior. It can also be used in fields such as medicine, economics, and environmental science to make informed decisions and improve outcomes.

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