I have a windows 7 pc but ubuntu does not allow me to go to a normal windows screen so i want to remove ubuntu from my pc.
I have absolutely no experience with Linux, and I desperately need to get my computer back up and running again with Windows.  How do I remove Ubuntu and reinstall Windows?    Editor's note: many of the answers are about removing Ubuntu from dual-boot but keeping Windows (which is a bit complicated), while other answers are about removing Ubuntu from single-boot (which is easy: basically just format the disk while installing Windows). The question as written is ambiguous between dual-boot or single-boot.
Why 'mkpasswd -m sha-512' produce different result every time it is called? I am using this function to create a password in the shadow file  $ mkpasswd -m sha-512 password $6$o50n4EfNOBzfs$K5l5AcOI5e0I9n2df0LmR11OLSPKSVM6AGBOnWVfDDfMubb46MrAj0zehdLNEoYzD2fj5q4XUdDwexj/dWLHy/ $ mkpasswd -m sha-512 password $6$ADmDOhDpW$gsYQuiKPpV1ewaUpRq2VGAvrcocffLQ8XGF94e6LNLZfKIjy6Ku.cZoR/5exeCi1ESjPB9TRM6HxCiY5BCNCV0 $ mkpasswd -m sha-512 password $6$Efj1agaI$HGkny3q1OTYT4KREh18gueHJae/3Bvil0iOEhfXj8bD.qy9Lg2UIQJMuBcq0XtG3xzueK.7cp0GfKr7tEo5YI/ $ mkpasswd -m sha-512 password $6$zdVPlCPai4Y$x3CBvlP99xZXZcr4PTiE..YLpZx39h5OHDxqazd9wFLImPuwsXF0M6KmqLzlCCrnQhI2lmEPdCzfmHA/fDiOz.   It makes me wonder how does the output value work as a password I have been reading Terry Tao's notes on Real Analysis and there's a part he just says, but does not really explain, so I am wondering if someone here would. The notes are  and my particular question is from Section 4, Corollary 3. It goes as follows,  Let $f_n \rightarrow f$ in $L^1$ then there exists a sub sequence $(f_{n_j}) \subset (f_n)$ such that $f_{n_j} \rightarrow f$ pointwise a.e. Moreover $(f_{n_j})$ converges almost uniformly to $f$.  The proof he gives is simply that since $\|f_n-f\|_1 \rightarrow 0$ as $n \rightarrow \infty$ we can pick a sub sequence such that $\|f_{n_j}-f\|_1&lt;2^{-j}$ which is enough to show pointwise a.e and almost uniform convergence. But what allows you to pick such a sub sequence is it maybe some Cauchy property or is it some weird construction? Then how do you go from that to pointwise a.e and even almost uniform convergence. I am assuming that for almost uniform, you do something similar to Egorov's theorem without the assumption the domain of $f$ has finite measure. Also I am aware that if you get almost uniform, you immediately have pointwise a.e, but I'd like to see how to get to both. Thank you. Differences in the behaviour of pinching a garden hose and closing a tap Let's say you have a garden hose connected to an ordinary water tap which is opened fully. If you pinch the end of the hose, water leaves the hose at a higher speed (and this can be useful while watering plants, to reach pots which are further away). However when a tap (with no hose connected) is opened only slightly, water flows out at a low speed, possibly even in drops.  The actions of pinching the end of a hose and of almost-closing an open tap seem similar, so why the difference in behaviour? I have heard several pseudoscientific explanations about the  and find them hard to believe.  As a mathematician mainly focusing on functional analysis, I have a considerable interest in this matter. Although I still have no idea about it, I aim to truly understand it one day. For now, the question is:  Can the Heisenberg Uncertainty Principle be explained so that a non-scientist can at least get a correct idea of what it says and what not?  One of the most heard explanations is the one that says naïvely that while trying to determine the position of a particle you send energy (light) so you modify its speed, therefore making it impossible to determine the speed. If I understand well this should only be a some sort of metaphor to explain it, right? I am unable to find the link for downloading Ubuntu 16.04 onto my   desktop/laptop. I am able to see the link only for 18.04, but I need 16.04. How to remove all subviews? Is it possible to to take a screenshot of a webpage with JavaScript and then submit that back to the server?  I'm not so concerned with browser security issues. etc. as the implementation would be for . But is it possible? How do I find the right size of filters for a lens? I want to show that if a function $f:[a,b]\rightarrow \mathbb R$ satisfies a Hölder condition of order $\alpha &gt; 1 $ then it is constant.  The way I think of it is as follows:  $$|f(x) - f(y)| &lt; K|x-y|^\alpha$$  $$\frac{|f(x) - f(y)|} {|x-y]}  &lt; K|x-y|^{\alpha -1}$$  $$\lim_{y\rightarrow x} \frac{|f(x) - f(y)|} {|x-y]} \le \lim_{y\rightarrow x}  K|x-y|^{\alpha -1} =0 $$ As the limit is $0$, we can remove the modulus, so we get:  $$\lim_{y\rightarrow x} \frac{f(x) - f(y)} {x-y} = 0$$ So $f$ is derivable and $f'(x) = 0$ for all $x$ in $[a,b]$. Note that the reason we can add the limit $y\rightarrow x$ is because $[a,b]$ is closed in $\mathbb R$.  However, the question gives as a hint using the mean value theorem. I am not sure why one should do that. You would first have to prove that $f$ is derivable in a similar manner to what I did, and then prove that $f$ is constant. Or is there a simpler way to do it and I am missing it?  Also please inform me of any mistakes I did in the proof (if any)/ Thank you! A smooth vector field on $S^2$ vanishing at a single point Remove spaces from std::string in C++ What is the preferred way to remove spaces from a string in C++? I could loop through all the characters and build a new string, but is there a better way? "batch" tag turns into "batch-file" Background: I'm giving a presentation to colleagues at work on hypothesis testing, and understand most of it fine but there's one aspect that I'm tying myself up in knots trying to understand as well as explain it to others.  This is what I think I know (please correct if wrong!)   Statistics that would be normal if variance was known, follow a $t$-distribution if the variance is unknown CLT (Central Limit Theorem): The sampling distribution of the sample mean is approximately normal for sufficiently large $n$ (could be $30$, could be up to $300$ for highly skewed distributions) The $t$-distribution can be considered Normal for degrees of freedom $&gt; 30$   You use the $z$-test if:   Population normal and variance known (for any sample size) Population normal, variance unknown and $n&gt;30$ (due to CLT) Population binomial, $np&gt;10$, $nq&gt;10$   You use the $t$-test if:   Population normal, variance unknown and $n&lt;30$ No knowledge about population or variance and $n&lt;30$, but sample data looks normal / passes tests etc so population can be assumed normal   So I'm left with:    For samples $&gt;30$ and $&lt;\approx 300$(?), no knowledge about population and variance known / unknown.   So my questions are:   At what sample size can you assume (where no knowledge about population distribution or variance) that the sampling distribution of the mean is normal (i.e. CLT has kicked in) when the sampling distribution looks non-normal? I know that some distributions need $n&gt;300$, but some resources seem to say use the $z$-test whenever $n&gt;30$... For the cases I'm unsure about, I presume I look at the data for normality. Now, if the sample data does looks normal do I use the $z$-test (since assume population normal, and since $n&gt;30$)? What about where the sample data for cases I'm uncertain about don't look normal? Are there any circumstances where you'd still use a $t$-test or $z$-test or do you always look to transform / use non-parametric tests? I know that, due to CLT, at some value of $n$ the sampling distribution of the mean will approximate to normal but the sample data won't tell me what that value of $n$ is; the sample data could be non-normal whilst the sample mean follows a normal / $t$.  Are there cases where you'd be transforming / using a non-parametric test when in fact the sampling distribution of the mean was normal / $t$ but you couldn't tell? I'm trying to write an automated test of an application that basically translates a custom message format into an XML message and sends it out the other end.  I've got a good set of input/output message pairs so all I need to do is send the input messages in and listen for the XML message to come out the other end.  When it comes time to compare the actual output to the expected output I'm running into some problems.  My first thought was just to do string comparisons on the expected and actual messages.  This doens't work very well because the example data we have isn't always formatted consistently and there are often times different aliases used for the XML namespace (and sometimes namespaces aren't used at all.)  I know I can parse both strings and then walk through each element and compare them myself and this wouldn't be too difficult to do, but I get the feeling there's a better way or a library I could leverage.   So, boiled down, the question is:  Given two Java Strings which both contain valid XML how would you go about determining if they are semantically equivalent?  Bonus points if you have a way to determine what the differences are. How can I download an APK file from the Play Store? How to automatically hyphenate within \texttt? This question was phrased as a bug report for LaTeX and I thought it would be helpful to be presented here (including an answer).  The person had a lot of "common words" in typewriter font sprinkled around his text and he was surprised that this text would not automatically hyphenate and he had to manually insert \- to mark hypenation points. Maximum power transfer proof I have the following homework problem.  Consider a power supply with fixed emf $ε$ and internal resistance $r$ causing current in a load resistance $R$. In this problem, $R$ is fixed and $r$ is a variable. The efficiency is defined as the energy delivered to the load divided by the energy delivered by the emf.  When the internal resistance is adjusted for maximum power transfer, what is the efficiency?  The answer is 50% but I'm confused how this is calculated. Here is my thought process so far.  The power dissipated in the load is $I^2R$ and $\displaystyle I = \frac{ε}{r+R}$ so $\displaystyle P = \frac{ε^2R}{(r+R)^2} = \frac{ε^2}{\frac{r^2}{R} + 2r + R}$ and so power transferred to the load will be maximum when $\displaystyle \frac{r^2}{R} + 2r + R$ is a minimum. Taking the first derivative $\displaystyle \frac {d}{dr}(\frac {r^2}{R} + 2r + R) = (\frac{2r}{R} + 2)$ and this equals 0 when r = -R. The second derivative is $\displaystyle \frac{2}{R} &gt; 0$ so this point would be a minimum and therefore max power transferred when r = -R.  But having $r = -R$ doesn't make sense to me. From what I read, for maximum power transfer $r$ should equal $+R$. So I've probably done something wrong in my calculations or assumptions in the previous paragraph. Also wouldn't max power be transferred when the internal resistance is as close to zero as possible?  Can someone please show me a proof that shows why $r = R$ for max power transfer and then how to calculate the efficiency. 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