Copy/paste from the PhD thesis into my future publication
Can I publish parts of the Ph.D thesis as a paper in a journal?
I need to analyse data and am unsure whether or not I should use a multinomial logistic regression analysis or a binary logistic regression analysis. I have just conducted a latent class analysis and am interested in seeing if my variables can predict class membership. There are only two classes and so my response variable only has two categories; however the variables range from having 2 to 3 to 4 and are mostly nominal categorical data. Add a link to the timeline of a post For the past year I have been quite active on SO and a bit on meta, but I had never heard of the timeline of a post until today in a comment on meta.  There was an  two years ago of this feature as "experimental". ( for that question).  This feature was , answer: this exists, but the link has to be built manually.  I think this could be quite interesting sometimes and I would like to use this, but I will not do so if I have to build the link each time by myself.  This feature has existed for two years and is still "experimental"? Who is using it? Who knows about it?  My feature request: Implement a link in the posts to their timeline. I am sure this nice feature will be used. Where can I find stock or custom ROMs for my Android device? Let $f$ be defined on $\mathbb{R}$ by $f(x) = e^{-1/x^2}$ for $x$ not equal to $0$. and $f(0)= 0$. Prove that $f^{(n)}(0)=0$ for all $n = 1, 2,3$ ...  Do I need to use Taylor expansion from calculus class?  Any hint would be appreciated. How do I remove "Ubuntu" in the bios boot menu? (UEFI) Tikz: Arrowheads in the center As much as I have tried, it doesn't seem possible to use separate labels when using a casesconstruction (from the amsmath package).  Ideally, I would like to be able to write something like  \documentclass{article} \usepackage{amsmath} \begin{document} \begin{equation} f(x)=\begin{cases}   1&amp;x\geq0\label{positive}\\   0&amp;x&lt;0\label{negative} \end{cases} \end{equation} \end{document}   and later refer to both cases at different places. What would be the best way to achieve this result? Inverse of a matrix is expressible as a polynomial? Let $A$ be an $n \times n$ matrix. Prove that if A is invertible, then there exists a polynomial $p$, such that $A^{-1}=p(A)$  Thus far:  Let $W$ denote the $k$ dimensional A-cyclic subspace spanned by a vector $v$.  Then, $I_n=\sum_{i=0}^{k} a_{i}A^i$ for some scalar $a_i$.  What I want to do is multiply both sides by $A^{-1}$, so that way it just falls into my hands, but Im not sure that is 'legal'. Accessing the web page's HTTP Headers in JavaScript “All you have to do is read” vs. “All you have to do is to read” Note: Suppose $f(z)$ is a polynomial or a proper/strictly proper rational function.   Can someone demonstrate why if all coefficient of $f(z)$ are real then relation $f(\bar z)$ = $\bar {f(z)}$ hold  Is there a theorem for this result? Is there a way to detect if a browser window is not currently active? Export objects to separate files in Illustrator What is $dx$ in integration? I am trying to prove that the affine hull of a set $X \subseteq \mathbb{R}^d$ is equal to the affine hull of the clojure of that set: $$ \text{aff}(X) = \text{aff}(\text{cl}(X)). $$  Proving $\text{aff}(X) \subseteq \text{aff}(\text{cl}(X))$ is easy, but containment in the other direction is harder.  I am assuming there is a sequence $\{ x_k \}_{k=1}^\infty \subseteq X$ such that $\lim_{k\to \infty} x_k = x^*$. The point $x^*$ is obviously contained in $\text{aff}(\text{cl}(X))$. But I am not sure how to show it is also in $\text{aff}(X)$, specifically because I am not sure how to show that $x^*$ lies on a line connecting two points in $X$.  Any suggestions on how to surmount this hurdle are welcome. Latex create a second keywordstyle using listing doesn't work Windows installer for Ubuntu 14.04 LTS onwards Atheros AR9462 WiFI very unstable & package loss I'm having severe issues w/ package-loss and a generally bad connection w/in a WiFi-network at my uni. I'm usually encountering a package loss of ~50% and aborting connections which makes working w/ SaaS impossible.  I'm using Ubuntu 13.04 and my network-adapter is an Atheros AR9462.  Applying the nohwcrypt=1-option doesn't change anything.  Also very noticable: The connection works as intended in other networks; the problematic network uses WPA2 w/ MSCHAPv2.  Anyone can help my out here?  $ sudo lshw -class network  *-network UNCLAIMED             description: Ethernet controller        product: NetLink BCM57780 Gigabit Ethernet PCIe        vendor: Broadcom Corporation        physical id: 0        bus info: pci@0000:04:00.0        version: 01        width: 64 bits        clock: 33MHz        capabilities: pm msi pciexpress bus_master cap_list        configuration: latency=0        resources: memory:d3500000-d350ffff   *-network        description: Wireless interface        product: AR9462 Wireless Network Adapter        vendor: Atheros Communications Inc.        physical id: 0        bus info: pci@0000:09:00.0        logical name: wlan0        version: 01        serial: e0:06:e6:76:ba:89        width: 64 bits        clock: 33MHz        capabilities: pm msi pciexpress bus_master cap_list rom ethernet physical wireless        configuration: broadcast=yes driver=ath9k driverversion=3.8.0-22-generic firmware=N/A ip=131.173.74.130 latency=0 link=no multicast=yes wireless=IEEE 802.11abgn        resources: irq:19 memory:d3400000-d347ffff memory:9fb00000-9fb0ffff Principal component analysis (PCA) is usually explained via an eigen-decomposition of the covariance matrix. However, it can also be performed via singular value decomposition (SVD) of the data matrix $\mathbf X$. How does it work? What is the connection between these two approaches? What is the relationship between SVD and PCA?  Or in other words, how to use SVD of the data matrix to perform dimensionality reduction? Transformation of Random Variable $Y = X^2$ I'm learning probability, specifically transformations of random variables, and need help to understand the solution to the following exercise:      Consider the continuous random variable $X$ with probability density function $$f(x) = \begin{cases} \frac{1}{3}x^2 \quad  -1 \leq x \leq 2, \\ 0 \quad \quad \text{elsewhere}. \end{cases}$$ Find the cumulative distribution function of the random variable $Y = X^2$.   The author gives the following solution:   For $0 \leq y \leq 1: F_Y(y) = P(Y \leq y) = P(X^2 \leq y) \stackrel{?}{=} P(-\sqrt y \leq X \leq \sqrt y) = \int_{-\sqrt y}^{\sqrt y}\frac{1}{3}x^2\, dx = \frac{2}{9}y\sqrt y.$  For $1 \leq y \leq 4: F_Y(y) = P(Y \leq y) = P(X^2 \leq y) \stackrel{?}{=} P(-1 \leq X \leq \sqrt y) = \int_{-1}^{\sqrt y}\frac{1}{3}x^2\, dx = \frac{1}{9} + \frac{1}{9}y\sqrt y.$  For $y &gt; 4: F_{Y}(y) = 1.$    Previous to this exercise, I've managed to follow the solutions of two similar (obviously simpler) problems for a strictly increasing and strictly decreasing function of $X$, respectively. However in this problem, I don't understand the computations being done, specifically:    How does the three intervals $0 \leq y \leq 1$, $1 \leq y \leq 4$ and $y &gt; 4$ are determined? In the two previous problems I've encountered, we only considered one interval which was identical to the interval where $f(x)$ was non-zero. In the case where $0 \leq y \leq 1$, why does $P(X^2 \leq y) = P(-\sqrt y \leq X \leq \sqrt y)$ and not $P(X \leq \sqrt y)$? I have put question marks above the equalities that I don't understand.   I think I have not understand the theory well enough. I'm looking for an answer that will make me understand the solution to this problem and possibly make the theory clearer.