Say, I have two normally distributed random variables:  $X \sim \mathcal N(\mu_x, \sigma_x^2), Y \sim \mathcal N(\mu_y, \sigma_y^2)$.  I want to test if $\mu_x = \mu_y$. Further, I assume that these variables are independent of each other. Then, I believe the test statistic would be:  ${\mu_x - \mu_y}\over{\sqrt{\sigma_x^2 + \sigma_y^2)}}$  EDIT:  I want to be a bit more specific. In a journal article they test if two regression coefficients from two DIFFERENT regressions are equal.   So, I want to test if two estimated OLS coefficients from DIFFERENT regression models (the sample size is identical) are different from each other. I assume that the coefficients are independent.
This seems to be a basic issue, but I just realized that I actually don't know how to test equality of coefficients from two different regressions. Can anyone shed some light on this?   More formally, suppose I ran the following two regressions:  $$ y_1 = X_1\beta_1 + \epsilon_1 $$ and  $$ y_2 = X_2\beta_2 + \epsilon_2 $$ where $X_i$ refers to the design matrix of regression $i$, and $\beta_i$ to the vector of coefficients in regression $i$. Note that $X_1$ and $X_2$ are potentially very different, with different dimensions etc. I am interested in for instance whether or not $\hat\beta_{11} \neq \hat\beta_{21}$.   If these came from the same regression, this would be trivial. But since they come from different ones, I am not quite sure how to do it. Does anyone have an idea or can give me some pointers?   My problem in detail: My first intuition was to look at the confidence intervals, and if they overlap, then I would say they are essentially the same. This procedure does not come with the correct size of the test, though (i.e. each individual confidence interval has $\alpha=0.05$, say, but looking at them jointly will not have the same probability). My "second" intuition was to conduct a normal t-test. That is, take   $$ \frac{\beta_{11}-\beta_{21}}{sd(\beta_{11})} $$  where $\beta_{21}$ is taken as the value of my null hypothesis. This does not take into account the estimation uncertainty of $\beta_{21}$, though, and the answer may depend on the order of the regressions (which one I call 1 and 2).   My third idea was to do it as in a standard test for equality of two coefficients from the same regression, that is take  $$ \frac{\beta_{11}-\beta_{21}}{sd(\beta_{11}-\beta_{21})} $$   The complication arises due to the fact that both come from different regressions. Note that   $$ Var(\beta_{11}-\beta_{21}) = Var(\beta_{11}) + Var(\beta_{21}) -2 Cov(\beta_{11},\beta_{21}) $$ but since they are from different regressions, how would I get $Cov(\beta_{11},\beta_{21})$?  This led me to ask this question here. This must be a standard procedure / standard test, but I cound not find anything that was sufficiently similar to this problem. So, if anyone can point me to the correct procedure, I would be very grateful!
Intuition behind the Definition of Conditional Probability (for 2 Events) Why do gaps appear in my mesh when I animate it? As I attempt to complete a "Walk cycle" with my model, the mesh at the pants line separates from the skin as the model moves or as I rotate the controls. This happens in many places on my model and it reveals the model interior. It is a one piece mesh, though, so I don't understand why this happens. Is this caused by weight painting problems? How can I fix this? Not a problem, but have been seeing questions recently of people not being offered upgrade from 18.04 to 20.04.  I thought these upgrades were offered back in June.  I upgraded to 20.04 from 19.10 and this was offered back in May.  Imagine that different versions paths have different schedules, but google does not help explaining(that I found).  Does someone know the schedules? What is the difference between logistic regression and Fractional response regression? As far as I know, the difference between logistic model and fractional response model (frm) is that the dependent variable (Y) in which frm is [0,1], but logistic is {0, 1}. Further, frm uses the quasi-likelihood estimator to determine its parameters. Normally, we can use glm to obtain the logistic models by glm(y ~ x1+x2, data = dat, family = binomial(logit)). For frm, we change family = binomial(logit) to family = quasibinomial(logit). I noticed we can also use family = binomial(logit) to obtain frm's parameter since it gives the same estimated values. See the following example library(foreign) mydata &lt;- read.dta(&quot;k401.dta&quot;)   glm.bin &lt;- glm(prate ~ mrate + age + sole + totemp,                 data = mydata, family = binomial('logit')) summary(glm.bin)  return,  Call: glm(formula = prate ~ mrate + age + sole + totemp,      family = binomial(&quot;logit&quot;),      data = mydata)  Deviance Residuals:      Min       1Q   Median       3Q      Max   -3.1214  -0.1979   0.2059   0.4486   0.9146    Coefficients:               Estimate Std. Error z value Pr(&gt;|z|)     (Intercept)  1.074e+00  8.869e-02  12.110  &lt; 2e-16 *** mrate        5.734e-01  9.011e-02   6.364 1.97e-10 *** age          3.089e-02  5.832e-03   5.297 1.17e-07 *** sole         3.636e-01  9.491e-02   3.831 0.000128 *** totemp      -5.780e-06  2.207e-06  -2.619 0.008814 **  --- Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  (Dispersion parameter for binomial family taken to be 1)      Null deviance: 1166.6  on 4733  degrees of freedom Residual deviance: 1023.7  on 4729  degrees of freedom AIC: 1997.6  Number of Fisher Scoring iterations: 6  And for family = quasibinomial('logit'), glm.quasi &lt;- glm(prate ~ mrate + age + sole + totemp,   data = mydata ,family = quasibinomial('logit')) summary(glm.quasi)  return,  Call: glm(formula = prate ~ mrate + age + sole + totemp,      family = quasibinomial(&quot;logit&quot;),      data = mydata)  Deviance Residuals:      Min       1Q   Median       3Q      Max   -3.1214  -0.1979   0.2059   0.4486   0.9146    Coefficients:               Estimate Std. Error t value Pr(&gt;|t|)     (Intercept)  1.074e+00  4.788e-02  22.435  &lt; 2e-16 *** mrate        5.734e-01  4.864e-02  11.789  &lt; 2e-16 *** age          3.089e-02  3.148e-03   9.814  &lt; 2e-16 *** sole         3.636e-01  5.123e-02   7.097 1.46e-12 *** totemp      -5.780e-06  1.191e-06  -4.852 1.26e-06 *** --- Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  (Dispersion parameter for quasibinomial family taken to be 0.2913876)      Null deviance: 1166.6  on 4733  degrees of freedom Residual deviance: 1023.7  on 4729  degrees of freedom AIC: NA  Number of Fisher Scoring iterations: 6  The estimated Beta from both `family` are the same, but the difference is the SE values.  However, to obtain the correct SE, we have to use `library(sandwich)` as in this [post][1].  Now, my questions:  1. What is the difference between these two codes? 2. Is frm about to obtain robust SE?  If my understanding is not correct, please give some suggestions.      [1]: https://stackoverflow.com/questions/37584715/fractional-response-regression-in-r Is a DNA molecule a single strand of polynucleotide or two of them linked together? How can tagged PDFs be created that support Universal Accessibility and reflowing? How to create tagged PDF such that they will be:   good enough for PDF/UA "reflowable" on smaller screens and ebook readers   Any syntax &amp; any engine are of interest, but I'm most interested in using LaTeX with [Xe|pdf]latex engines. Are there any examples of Humans and Aliens producing offspring in Star Wars? The title is pretty self explanatory, although to expand on the question further I am looking for any examples of inter-species breeding in the Star Wars universe, not just inter-species (ahem) "relations". How to check type of variable in Java? Whether and how to thank a girlfriend or boyfriend in the acknowledgements of thesis? This is exercise 1.6.1 of   Suppose that 501 distinct integers are selected from 1 . . . 1000. Show that there are distinct selected integers $a$ and $b$ such that $a | b$. Show that this is not always true if 500 integers are selected.  This seems like it should be simple but it's has been stumping me for a few days.  Here's my idea for a proof:  Let $S$ be a set of 500 distinct integers between 1 and 1000 that don't divide each other. Let $S_{1-500}$ and $S_{501-1000}$ partition $S$ into sets containing the elements ≤ and > than 500, respectively. Then consider $2 S_{1-500}$ and $\frac{1}{2}S_{501-1000}$, the result of multiplying the elements of these sets by 2 and 1/2, respectively. Because elements of S are coprime, $S_{1-500}$, $S_{501-1000}$, $2 S_{1-500}$, and $\frac{1}{2}S_{501-1000}$, are disjoint. Also note that each of these sets is a subset of the integers from 1 to 1000. One can deduce that the cardinality of the union of these sets is 1000, and therefore they form a partition of the integers from 1 to 1000.  Now, I know this demonstrates that you can't add another number to $S$ that is coprime with all the elements in $S$, which means that a coprime subset of the integers from 1 to 1000 can have at most 500 elements. Can someone articulate why this is true for me? Also, this was in a section on the Pigeonhole principle, can someone rework the proof to utilize that, and possibly make it simpler? Thanks. Dot Product Intuition I'm searching to develop the intuition (rather than memorization) in relating the two forms of a dot product (by an angle theta between the vectors and by the components of the vector ).      For example, suppose I have vector $\mathbf{a} = (a_1,a_2)$ and vector $\mathbf{b}=(b_1,b_2)$.  What's the physical or geometrical meaning that   $$a_1b_1 + a_2b_2 = |\mathbf{a}||\mathbf{b}|\cos(\theta)\;?$$  Why is multiplying $|\mathbf{b}|$ times $|\mathbf{a}|$ in direction of $\mathbf{b}$ the same as multiplying the first and second components of $\mathbf{a}$ and $\mathbf{b}$ and summing ?       I know this relationship comes out when we use the law of cosines to prove, but even then i cant get a intuition in this relationship.  This image clarifies my doubt:    Thanks How to force a table into page width? I have the following table:  \begin{table}[htb] \begin{tikzpicture} \node (table) [inner sep=0pt] { \begin{tabular}{ l | l }   {\bf Symptom} &amp; {\bf Metric} \\ \hline Class that has many accessor methods and accesses a lot of external data &amp; ATFD is more than a few\\ Class that is large and complex &amp; WMC is high\\ Class that has a lot of methods that only operate on a proper subset of the instance variable set &amp; TCC is low\\ \end{tabular} }; \draw [rounded corners=.5em] (table.north west) rectangle (table.south east); \end{tikzpicture} \caption{God class symptoms} \label{tbl:god_class} \end{table}   Now I want to force the width of the table to be the same as the \textwidth, either by linewrapping of table text or by scaling. How can I achieve that? I asked a question regarding a very specific feature of a program and whether or not it works with a larger program.  Within hours someone had marked it with "This question may already have an answer here of a similar question, but not related to my question.  The question I asked IS NOT ADDRESSED in the referred-to question. It's frustrating to spend half an hour putting together an intelligent and specific question, and then have someone mark it as a duplicate (or possible duplicate), which apparently causes people to skip over my question. I haven't received any more responses since the question was flagged.   Two questions:  (1) How do I get this flagging removed and (2) what's the best way to prevent these "helpful" flaggings in the future? What are the differences between proofs based on simulation and proofs based on games? I've been trying to play with a rigid body simulations.  I prepared a project of a building made of domino tails. I've placed all objects very tight, but as soon as I start animation it looks like there was some tiny gap between objects, so they all fall down a little bit. For a high building it leads to a construction disaster as the whole building colapses.  Here is an example:  (I'd suggest to use 0.25x speed).  How can I fix it? What's the purpose of cycling gloves? I haven't cycled for many years already and long ago when I was cycling I didn't pay much attention to equipment. Now I see that many cyclists wear a certain kind of gloves - that only cover the palm and leave the fingers uncovered.  What's the purpose of wearing such gloves (and not usual gloves for example)? In what non-cycling activities can such gloves be useful? How many different permutations are there of the sequence of letters in "MISSISSIPPI"? What was Tauriel doing during the events of The Lord of the Rings movies? [Yes, The Hobbit movies are nonsense. But how could I call myself a fantasy fan if I didn't demand my nonsense be as logical as possible?]  Tauriel is presented as a highly-skilled fighter and a combatant for the side of good. Despite this, she apparently did nothing during Sauron's war on the free peoples.  Was she somehow     still mourning Killi, after all these decades?   Was she dead? Or is there another reason she didn't participate? I have seen similar questions on the matter (, ) of adding vertical space and not breaking vertical lines, the solutions suggested are to add space after the \\ using \\[0.1cm] for example or changing the \arraystretch which I do not want to do. Using \\[0.1cm] allows me to add space just before a \hline but not after as well.  How can I add vertical space after a \hline without breaking vertical lines in a table?  Please find my MWE below:  \documentclass{scrreprt} \usepackage{setspace} \usepackage[left=2.5cm,right=2.5cm,top=3cm,bottom=3cm]{geometry} \usepackage{array,tabularx} \doublespacing  \begin{document} \begin{table}[htb]   \centering   \onehalfspacing   \caption{Table of info.}      \begin{tabular}{c | c c}  &amp; C/S Analyser &amp; O/N/H Analyser \\  &amp; EMIA 320 V2 &amp; EMGA 830 \\[0.1cm]    \hline    \noalign{\vskip 0.1cm} Sensitivity (ppm) &amp; 0.1 &amp; 0.001 \\ Furnace type &amp; Induction &amp; Impulse \\ Carrier gas &amp; Pure O2 &amp; Pure He \\[0.1cm]    \hline     \end{tabular}    \doublespacing   \label{tab:chem} \end{table} \end{document} limit of integral $n\int_{0}^{1} x^n f(x) \text{d}x$ as $n\rightarrow \infty$ I am trying to solve the following problem at the level of a senior undergrad analysis level. So, the problem is as follows: We are given a function $f$ which is continuous on the interval $\left [ 0,1 \right ]$, and the question is to find the limit: $$\lim_{n\rightarrow \infty}\int_{0}^{1}x^{n}f(x)dx\;.$$ The second part of the problem is to deduce the following limit: $$\lim_{n\rightarrow \infty}n\int_{0}^{1}x^{n}f(x)dx\;.$$  For the first part: I just did the following: For every $0\leq x&lt; 1$: $x\leq M$, where $0&lt; M&lt; 1$. Then: $$\int_{0}^{1}x^{n}f(x)dx\leq M^{n}\int_{0}^{1}f(x)dx\;.$$ Then: $$\lim_{n\rightarrow \infty }\int_{0}^{1}x^{n}f(x)dx\leq \lim_{n\rightarrow \infty }M^{n}\int_{0}^{1}f(x)dx= 0.\int_{0}^{1}f(x)dx=0\;,$$ so $$\lim_{n\rightarrow \infty }\int_{0}^{1}x^{n}f(x)dx=\lim_{n\rightarrow \infty }f(1)\int_{0}^{1}1dx=f(1)\;.$$ Does that make sense? If not, please show me the correct one.  As for the second part, I have no idea what to do. Any help?