An incomplete (maybe wrong) proof about R^2

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In summary, the conversation discusses proving the existence of r and w such that z=rw, where z is a complex number, r is a positive real number, and w is a complex number with a magnitude of 1. It is also asked whether w and r are always uniquely determined by z. The attempt at a solution involves defining z as the product of r and w, and choosing a w that satisfies |w|=1. The proof is unclear and it is suggested to consider writing z in polar form.
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1. Homework Statement [/b]
If z is a complex number, prove that there exists an r > 0 and a complex number w with |w|=1 such that z=rw. Are w and r always uniquely determined by z?


Homework Equations


n/a


The Attempt at a Solution


As C is a set closed under multiplication, we can define z=rw where r=(a,0) and w=(x,y). Hence z=(a,0)(x,y)=(ax,ay).
We can choose a w such that |w|=1; sqrt{(x+yi)(x-yi)}=1 = x^2+y^2=1 and clearly, this is the equation of the unit circle. Therefore, w must be determined uniquely by z so that they could intersect.
I don't know if my proof is valid and I'm aware of that I didn't say anything about r but I think it is related to z=(a,0)(x,y)=(ax,ay) somehow. Help please...
 
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Perhaps the difficulty is that you think this has something to do with R^2. It doesn't. What do you get if you write z in polar form?
 

FAQ: An incomplete (maybe wrong) proof about R^2

What is R^2?

R^2 is a statistical measure that represents the proportion of the total variation in a dependent variable that is explained by the independent variables in a regression model.

How is R^2 calculated?

R^2 is calculated by taking the ratio of the sum of squared errors (SSE) to the total sum of squares (SST). It is expressed as a percentage, with a higher value indicating a better fit of the model to the data.

What does an incomplete (maybe wrong) proof about R^2 mean?

This means that the proof for R^2 may be missing some key components or may contain errors. It is important to carefully evaluate any incomplete proofs before using them in statistical analyses.

How is R^2 interpreted?

R^2 is typically interpreted as the percentage of the variation in the dependent variable that is explained by the independent variables in the model. For example, an R^2 value of 0.80 means that 80% of the variation in the dependent variable is explained by the independent variables in the model.

What are the limitations of using R^2?

R^2 is not a perfect measure and has some limitations. It does not indicate the causality between variables, and a high R^2 value does not necessarily mean that the model is the best fit for the data. Additionally, it may be influenced by outliers and can be misleading when used with non-linear models.

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