Exploring the Relationship Between Theorem 4.5 and p's and q's

  • Thread starter g.lemaitre
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In summary, the conversation discusses theorem 4.5, which deals with three arbitrary vectors in a given vector space. The question at hand involves determining if a certain condition is met by two given second-order polynomials. The condition being tested is the distributive property, which states that (u+v)*w = u*w + v*w. The method for testing this is to plug in a third "vector" (another second-order polynomial) and evaluate both sides to determine if they are equal.
  • #1
g.lemaitre
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Homework Statement



Screenshot2012-07-19at24040AM.png


here is theorem 4.5

Screenshot2012-07-19at23441AM.png



The Attempt at a Solution



How can theorem 4.5 even relate to the question? The question deals with p's and q's and the theorem deals with u v w.
 
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  • #2
The short version of what I said in the other thread is this:

The theorem deals with three arbitrary vectors that are elements of a given vector space. They do not need to be called u, v, and w.
 
  • #3
well how do i even find out if
p(x) = a0 + a1x + a2x2
q(x) = b0 + b1x + b2x2 and P2
meets the condition (u+v) *w = u*w + v*w?
What's the first question I ask myself? I don't even know what step 1 is, much less 2 and 3
 
  • #4
[itex]P_2[/itex] denotes the space of second-order polynomials. All you need to test this condition is to come up with a third "vector" (another second-order polynomial) that you can plug in as w.

This condition you're testing is the distributive property. It's the same thing as saying that (5+10)x6 = 5x6 + 10x6. I can plug in any three numbers there instead of 5, 10, and 6 and it works because the real line is a (very simple) vector space of sorts.

What you do to test this is simple. If I want to test the distributive property on real numbers, I go to what I just wrote and say, hm, is (5+10)x6 -> 15x6 really equal to 5x6 + 10x6?

At that point, you just evaluate both sides until you get something that's obviously (un)equal.
 
  • #5
ok, i got it.
 

What is linearity in scientific measurements?

Linearity refers to the relationship between the input and output of a measurement system. In a linear system, the output is directly proportional to the input, meaning that a change in the input will result in a proportional change in the output.

Why is it important to check for linearity in scientific measurements?

Checking for linearity is important because it ensures the accuracy and reliability of the measurements. If a measurement system is not linear, it may produce incorrect or inconsistent results, leading to incorrect conclusions and decisions.

How is linearity checked in scientific measurements?

Linearity can be checked by plotting a graph of the input versus the output and examining if the data points fall on a straight line. Another method is to perform a linear regression analysis, which calculates the correlation coefficient and determines if the relationship between the variables is linear.

What are some common sources of non-linearity in scientific measurements?

Non-linearity can occur due to various factors, such as non-linear calibration curves, non-uniformities in the measurement system, or environmental factors that affect the input or output. Improper instrument calibration or malfunctioning equipment can also introduce non-linearity in measurements.

How can non-linearity in scientific measurements be corrected?

If non-linearity is detected, it can be corrected by adjusting the calibration curve or making necessary adjustments to the measurement system. In some cases, non-linearity may be unavoidable, and it is important to understand and account for it in the data analysis and interpretation process.

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