In the movie The Lord of the Rings it said that Sauron somehow gave (presumably by proxy) several rings to Elves, Dwarves, and Men. There's also another race called Hobbits, who became quite important in the story. Why didn't he give them any rings so that he could control them as well?  Forgive my ignorance here; I never read the books and I'm only watching the movie for the 2nd time in 16 years.
Simple: every other race in Middle Earth has rings, why do Hobbits and Orcs not?
Prove that if $d$ divides $n$ then $φ(d)$ divide $φ(n)$ for $φ$ denotes Euler’s $φ$-function.  I know that $d|n$ mean there exists some integer $k$ such that $n=kd$, but how can I use this to prove $φ(d)$ divide $φ(n)$ The normal JOIN ... ON ... syntax is well known. But it is also possible to position the ON clause separately from the JOIN that it corresponds to. This is something that is rarely seen in practice, not found in tutorials and I have not found any web resource that even mentions that this is possible.  Here is a script to play around with:  SELECT * INTO #widgets1 FROM (VALUES (1), (2), (3)) x(WidgetID)   SELECT * INTO #widgets2 FROM (VALUES (1, 'SomeValue1'), (2, 'SomeValue2'), (3, 'SomeValue3')) x(WidgetID, SomeValue)  SELECT * INTO #widgetProperties FROM (VALUES     (1, 'a'), (1, 'b'),     (2, 'a'), (2, 'b')) x(WidgetID, PropertyName)   --q1 SELECT w1.WidgetID, w2.SomeValue, wp.PropertyName FROM #widgets1 w1 LEFT JOIN #widgets2 w2 ON w2.WidgetID = w1.WidgetID LEFT JOIN #widgetProperties wp ON w2.WidgetID = wp.WidgetID AND wp.PropertyName = 'b' ORDER BY w1.WidgetID   --q2 SELECT w1.WidgetID, w2.SomeValue, wp.PropertyName FROM #widgets1 w1 LEFT JOIN #widgets2 w2 --no ON clause here JOIN #widgetProperties wp  ON w2.WidgetID = wp.WidgetID AND wp.PropertyName = 'b'  ON w2.WidgetID = w1.WidgetID ORDER BY w1.WidgetID   --q3 SELECT w1.WidgetID, w2.SomeValue, wp.PropertyName FROM #widgets1 w1 LEFT JOIN (     #widgets2 w2 --no SELECT or FROM here     JOIN #widgetProperties wp     ON w2.WidgetID = wp.WidgetID AND wp.PropertyName = 'b') ON w2.WidgetID = w1.WidgetID ORDER BY w1.WidgetID   q1 looks normal. q2 and q3 have these unusual positionings of the ON clause.  This script does not necessarily make much sense. It was hard for me to contrive a meaningful scenario.  So what do these unusual syntax patterns mean? How is this defined? I noticed that not all positions and orderings for the two ON clauses are allowed. What are the rules governing this?  Also is it ever a good idea to write queries like this? In the calculus of variations, particularly Lagrangian mechanics, people often say we vary the position and the velocity independently. But velocity is the derivative of position, so how can you treat them as independent variables? Restrictions on the variables for this identity are as follows: $a \geq m \geq 0$ and $n \geq b \geq 0$.  I have not gotten far with this, but these are my very rudimentary thoughts: We need to define a special element, somehow, and then carve up the $m + n$ remaining elements to be chosen out of the remaining pool of $a + b$ elements by writing $a + b = a - i + b + i$. From the $a - i$ pile, we choose $m$ things. From the $b + i$ pile, we choose $n$ things. My problem is, what is going on with this special element? How is it being defined so as to split things in this precise way?  A hint would be great for the moment. Find $\sum_{n=1}^{\infty}$ $\frac{n^{2}}{\left(n+1\right)\left(n+2\right)\left(n+3\right)\left(n+4\right)}$ $$\sum_{n=1}^{\infty}\frac{n^{2}}{\left(n+1\right)\left(n+2\right)\left(n+3\right)\left(n+4\right)}$$   MY Approach$\sum_{n=1}^{\infty}$$\frac{n^{2}}{\left(n+1\right)\left(n+2\right)\left(n+3\right)\left(n+4\right)}$ = Lim$_{k\rightarrow\infty}$$\sum_{k=1}^{n}\frac{k^{2}}{\left(k+1\right)\left(k+2\right)\left(k+3\right)\left(k+4\right)}$  $\frac{k^{2}}{\left(k+1\right)\left(k+2\right)\left(k+3\right)\left(k+4\right)}$= $\frac{1}{6\left(n+1\right)}$-$\frac{2}{\left(n+2\right)}$+$\frac{9}{\left(n+3\right)2}$-$\frac{8}{\left(n+4\right)3}$  I don't think i can telescope it, And i don't know any other method I want to make a full focus on the body, for example: I need to have a clear focus on the each balls in the photo. I used just single point focus.    In this picture I used:   shutter speed: 8 aperture: F/4 ISO: 200 2 flashes, one in the right and one on the top of project camera: nikon d300s lens : 105mm micro 2.8   Thanks Stance on repost question instead of editing to invalidate answers? Run cURL commands from Windows console Is there a way to install cURL in windows in order to run cURL commands from the command prompt? How exactly does a "random effects model" in econometrics relate to mixed models outside of econometrics? I used to think that "random effects model" in econometrics corresponds to a "mixed model with random intercept" outside of econometrics, but now I am not sure. Does it?  Econometrics uses terms like "fixed effects" and "random effects" somewhat differently from the literature on mixed models, and this causes a notorious confusion. Let us consider a simple situation where $y$ linearly depends on $x$ but with a different intercept in different groups of measurements:  $$y_{it} = \beta x_{it} + u_i + \epsilon_{it}.$$  Here each unit/group $i$ is observed at different timepoints $t$. Econometricians call it "panel data".    In mixed models terminology, we can treat $u_i$ as  a fixed effect or as a random effect (in this case, it's random intercept). Treating it as fixed means fitting $\hat \beta$ and $\hat u_i$ to minimize squared error (i.e. running OLS regression with dummy group variables). Treating it as random means that we additionally assume that $u_i\sim\mathcal N(u_0,\sigma^2_u)$ and use maximum likelihood to fit $u_0$ and $\sigma^2_u$ instead of fitting each $u_i$ on its own. This leads to the "partial pooling" effect, where the estimates $\hat u_i$ get shrunk toward their mean $\hat u_0$.  R formula when treating group as fixed:    y ~ x + group R formula when treating group as random:   y ~ x + (1|group)  In econometrics terminology, we can treat this whole model as a fixed effects model or as a random effects model. The first option is equivalent to the fixed effect above (but econometrics has its own way of estimating $\beta$ in this case, called "within" estimator). I used to think that the second option is equivalent to the random effect above; e.g. @JiebiaoWang in his highly upvoted answer to  says that      In econometrics, the random-effects model may only refer to random intercept model as in biostatistics    Okay --- let us test if this understanding is correct. Here is some random data generated by @ChristophHanck in his answer to  (I put  for those who do not use R):    @Christoph does two fits using econometrics approaches:    fe &lt;- plm(stackY~stackX, data = paneldata, model = "within") re &lt;- plm(stackY~stackX, data = paneldata, model = "random")   The first one yields the estimate of beta equal to -1.0451, the second one 0.77031 (yes, positive!). I tried to reproduce it with lm and lmer:  l1 = lm(stackY ~ stackX + as.factor(unit), data = paneldata) l2 = lmer(stackY ~ stackX + (1|as.factor(unit)), data = paneldata)   The first one yields -1.045 in perfect agreement with the within estimator above. Cool. But the second yields -1.026, which is miles away from the random effects estimator. Heh? What is going on? In fact, what is  plm even doing, when called with model = "random"?  Whatever it is doing, can one somehow understand it via the mixed models perspective?  And what is the intuition behind whatever it is doing? 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