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J595 Mathematical Reflection

J595 Mathematical Reflection  \(\sqrt[3]{(x-1)^2}  - \sqrt[3]{2(x-5)^2} + \sqrt[3]{(x-7)^2} = \sqrt[3]{4x} \) By Titu Adreescu. My Solution (potentially wrong): Let \(A = \sqrt[3]{(x-1)^2}\) , \(B = \sqrt[3]{2(x-5)^2}\), \(C = \sqrt[3]{(x-7)^2} \), \(D = \sqrt[3]{4x}\). Hence we have \(A + C = B + D\). Notice that \(A^3 + C^3 = B^3 + D^3 \). Also notice that \( (A+C)^3 = (B+D)^3\) yields \( AC = BD \). Also by factoring out \(A^3 + C^3 = (A+C)(A^2-AC-C^2)\) and doing the same for \(B^3+D^3\) gives us \(A^2 + C^2 = B^2 + D^2\). Lemma 1 : \((A^3 - C^3)^2 = (B^3 - D^3)^2\). Proof: Note that \((A^3+C^3)^2 - (A^3-C^3)^2 = 4A^3C^3 = 4B^3D^3 = (B^3+D^3)^2 - (B^3-D^3)^2\) and the conclusion follows. \( \square \) Using Lemma 1 we get \( ( (x-1)^2 - (x-7)^2)^2 = (2(x-5)^2 - 4x)^2\) and solving for x gives us \( x = (9 \pm 4\sqrt{2}) , (3 \pm 2\sqrt{2}) \). \(\square\)
Recent posts

Proving error bound of Simpson's rule

I feel like writing about some techniques I found online on how people prove the error bound of Simpson's rule for numerical integration. The Simpson's rule: \( \int_a^b f(x) dx \) can be approximated as \(\frac{b-a}{6}(f(a) + 4 f(\frac{a+b}{2}) + f(b))\). You can find more info about this rule on Wikipedia. The topic of today is: how do you prove the error bound for the Simpson's rule, which is given by: \[ -\frac{1}{90} \left( \frac{a-b}{2} \right)^5 f^{(4)}(c) \] for some constant \(c\). And I think this is the best proof I have found out there, which might probably be the standard treatment of proving such error bounds:  https://math.stackexchange.com/questions/1759326/proving-error-bound-on-simpsons-rule-numerical-integration Since the answer there is quite succinct, here I'm going to expand it for my own understanding and future reference. Also, something that I found quite interesting, turns out MVT is quite useful in coming up with error bounds such as this. I w

Chi-Squared Distribution Proof (Elementary)

 Here I'll provide a (potentially wrong) proof for the pdf of chi-squared Distribution. The proof will be elementary, i.e. it doesn't use sophisticated mechanisms or deep results unless truly necessary. Hence the proof might be rather long. Also, I don't use standard notations, so it might be painful to some eyes. Additionally, notations might not even be consistent, so it might also cause motion sickness. Definition of Chi Squared Distribution: Given \( X_1, X_2, \cdots, X_k  \) drawn independently from standard normal distribution \( N(0, 1) \), then the random variable \( Y = X_1^2 + X_2^2 + \cdots + X_k^2 \) follows the chi-squared distribution with k degrees of freedom. This distribution naturally arises when we want to study the distribution of the sample variance. The distribution itself is going to be useful later on for defining t-distribution... Ok, I'm not good at explaining what motivates the use of something, but I guess the Wikipedia page does a better job

p - p^2-2 / p+2

"95% Confidence interval" does not imply "a 95% probability that the mean is contained in the CI"

I was damned confused! I think I understand it now, so I'd like to share how to resolve this "paradox". First, to recap, suppose you have a process / experiment that produces a confidence interval [x, y], for which you say, if we repeat this experiment a lot of time, 95% of the cases we'll see that the [x, y] covers something that we are estimating, e.g. the true mean. We call this 95% Confidence Interval (CI) for the true mean. But then, your instructor, or the author of the book you are reading, will immediately follow up by the statement "Be careful! It's wrong to say that the CI you computed has 95% chance of containing the true mean". Confusion ensues. You lose sleep, you don't eat as much. Your parents, friends, and loved ones start to get worried because you don't look so well. You, on the other hand, has lost all confidence in your understanding of statistics and basics of probabilities. WHY on earth?? If 95% of the CIs produced by the re

Rudin PMA Ch 2 P6 Part i