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$\log_2 (3) \approx 1.58496$ as you can easily verify If the matrix x is a real matrix, the eigenvalues will either be all real, or else there will be complex conjugate pairs $ (\log_2 (3))^2 \approx (1.58496)^2 \approx 2.51211$

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$2 \log_2 (3) \approx 2 \cdot 1.58496 \approx 3.16992$ From what i understand so far, a good regression model minimizes the sum of the squared differences between $2^ {\log_2 (3)} = 3$

Do any of those appear to be equal

(whenever you are wondering whether some general algebraic relationship holds, it's a good idea to first try some simple numerical examples to see if it is even possible. So, when you square both sides of an equation, you can get extraneous answers because you are losing the negative sign That is, you don't know which one of the two square roots of the right hand side was there before you squared it. We can square both side like this

$ x^2= 2$ but i don't understand why that it's okay to square both sides What i learned is that adding, subtracting, multiplying, or dividing both sides by the same thing is okay But how come squaring both. We can't simply square both sides because that's exactly what we're trying to prove

$$0 < a < b \implies a^2 < b^2$$ more somewhat related details

I think it may be a common misconception that simply squaring both sides of an inequality is ok because we can do it indiscriminately with equalities. I took a look at square root Squaring the number means x^2 And if i understood the square root correctly it does a bit inverse of squaring a number and gets back the x

I had a friend tell me a while ago that log() is also opposite of exponent, wouldn't that mean that square root is like a variant of log () that only inverse a squared number? I just came across this annotation in my school's maths compendium The compendium is very brief and doesn't explain what this means.