The person in question is my sugar mother and she doesn't want any of my information, she's offering me her bank account # and she sent me a picture of her ID to prove that she's who she claims to be(and I've video chatted her before as well.) Any chance I'll get in trouble for this?
On thanksgiving day a woman posted in a facebook group that her father had just passed away (left her money) and in good deed because it was thanksgiving she was offering to pay people’s debts. Well I inboxed her and she pretty much wants me to open up a bank account with First Tech Credit Union and give her my log in and password info. Ofcourse its sketchy!   So I questioned her and asked why she wanted that instead of her sending me the money directly from her bank to mine. She then responded because she wants to keep records of my debts. (She asked for proof and I sent her screenshots ONLY of my outstanding balances) I heard about the depositing checks scam so I questioned her about how she would deposit the funds and she said cash.  Her facebook page is 100% legit, I screenshotted her pics and family profiles etc. We came to an agreement that I would only accept her help if she sends me a picture of her ID before me sending this info.   She literally just wants to deposit the money to me, She isn’t asking me to send nobody money from that account or anything. Of course the account will be empty when I provide it to her. When she does deposit the money I am planning to immediately change the password and obviously stay watching my account activity while she deposits the funds.   She actually also provided pics of other accounts she funded. My question is, is this a scam? And if so how could she possibly scam me? Specially if i have her information and A pic of her ID. She is a christian lady and looks like she has a good heart, We talked about my struggles and she actually looked into why I needed to be assisted with the money. By no means was she pushy or anything. I want to believe that she just wants to help me but I am also too skeptical.
How do I resize root partition? I add video files to the video editor,  Both audio and video channels appear but only the audio plays.  The video is just a white box.  Anyone know what is wrong.  Added video in WMV.  Also tried MP4 same thing. I've created a simple mesh consisting of a cube that has been modelled out into a corner by extrusion:    I'm now trying to sculpt detail onto it in sculpt mode using dynotopo, however I'm getting different results on different sides of the mesh.  The bottom of the mesh (which was the original cube that I extruded from) sculpts nice and smoothly as expected, however the other side is jagged and distorted which I can't sculpt as nicely. Why is this? The original faces appear to be the same. Is there a way to get the count of rows in all tables in a MySQL database without running a SELECT count() on each table? Whenever I try to install anything with the Ubuntu Software Center, the following error appears.     Failed to download repository information      Check your Internet connection.      E:Unable to parse package file /var/lib/apt/lists/partial/archive.ubuntu.com_ubuntu_dists_precise_multiverse_i18n_Index (1) What to conclude from this lasso plot (glmnet) Following is the plot of glmnet with default alpha(1, hence lasso) using mtcars data set in R with mpg as the DV and others as the predictor variables.  glmnet(as.matrix(mtcars[-1]), mtcars[,1])     What can we conclude from this plot regarding different variables, especially am, cyl and wt (red, black and light blue lines)? How would we phrase the output in a report to be published?  I thought of following:   wt is the most important predictor of mpg. It is negatively affecting the mpg. cyl is a weak negative predictor of mpg. am may be a positive predictor of mpg. Other variables are not robust predictors of mpg.    Thanks for your thoughts on this.  (Note: cyl is the black line which does not reach 0 till very close to it.)  Edit: Following is plot(mod, xvar='lambda') which shows x-axis in order reverse of above plot: What is Thanos's motivation for his action? If I had a gauntlet with essentially unlimited power, and my goal was to help make sure life in the universe was able to prosper, then my first thought toward this goal probably would not be      to extinguish half of all life.   This doesn't directly help anyone, and it is, at best, a temporary solution, since      populations will eventually grow back.   Assuming that I am not, in fact, secretly in love with an avatar of death itself, why would I choose to      commit universal genocide   instead of directly solving the limited resource problem?  So, to be specific, my question is: In-universe, what is Thanos's motivation for attempting to do this instead of more reasonable alternatives? When I click on 'Business Data Connectivity Services' link on 'Manage Service Application' page of Central Administration, I get the following error : "The load balancer is not provisioned. Verify the load balancer was provisioned successfully before retrieving endpoint addresses. " I got a solution from  link, but as per instruction I am unable to install the hotfix for SharePoint 2013. Is there any solution available for this problem? Thanks in advance Fantasy book with shape shifting dragon warriors Set in a fantasy world with shape shifting Dragon kings and their warriors. They terrorize the humans, and kill those with magic.   The protagonist is the great-grandson of a powerful warlock. He finds out that he has magic, and fights with it to save his new friends, the Griffin, Blackhorse (I think that's his name), some creepy necromancer who is immortal, and Gwen (fellow sorceress), along with the kingdom that the Griffin rules in.   They fight the protagonist's father, the leader of the Dragon kings, the king's Duke, and the creepy sorcerer, who has a curse (when he dies, he changes side from good to bad, or bad to good). I have no clue what this is called, but I want to find out, since I'm out of things to read. Local polynomial regression: Why does the variance increase monotonically in the degree? How can I show that the variance of local polynomial regression is increasing with the degree of the polynomial (Exercise 6.3 in Elements of Statistical Learning, second edition)?  This question has been asked  but the answer just states it follows easliy.  More precisely, we consider $y_{i}=f(x_{i})+\epsilon_{i}$ with $\epsilon_{i}$ being independent with standard deviation $\sigma.$  The estimator is given by  $$ \hat{f}(x_{0})=\left(\begin{array}{ccccc} 1 &amp; x_{0} &amp; x_{0}^{2} &amp; \dots &amp; x_{0}^{d}\end{array}\right)\left(\begin{array}{c} \alpha\\ \beta_{1}\\ \vdots\\ \beta_{d} \end{array}\right) $$ for $\alpha,\beta_{1},\dots,\beta_{d}$ solving the following weighted least squares problem $$ \min\left(y_{d}-\underbrace{\left(\begin{array}{ccccc} 1 &amp; x_{1} &amp; x_{1}^{2} &amp; \dots &amp; x_{1}^{d}\\ \vdots\\ 1 &amp;  &amp;  &amp;  &amp; x_{n}^{d} \end{array}\right)}_{X}\left(\begin{array}{c} \alpha\\ \beta_{1}\\ \vdots\\ \beta_{d} \end{array}\right)\right)^{t}W\left(y-\left(\begin{array}{ccccc} 1 &amp; x_{1} &amp; x_{1}^{2} &amp; \dots &amp; x_{1}^{d}\\ \vdots\\ 1 &amp;  &amp;  &amp;  &amp; x_{n}^{d} \end{array}\right)\left(\begin{array}{c} \alpha\\ \beta_{1}\\ \vdots\\ \beta_{d} \end{array}\right)\right) $$ for $W=\text{diag}\left(K(x_{0},x_{i})\right)_{i=1\dots n}$ with $K$ being the regression kernel. The solution to the weighted least squares problem can be written as $$ \left(\begin{array}{cccc} \alpha &amp; \beta_{1} &amp; \dots &amp; \beta_{d}\end{array}\right)=\left(X^{t}WX\right)^{-1}X^{t}WY. $$ Thus, for $l(x_{0})=\left(\begin{array}{ccccc} 1 &amp; x_{0} &amp; x_{0}^{2} &amp; \dots &amp; x_{0}^{d}\end{array}\right)\left(X^{t}WX\right)^{-1}X^{t}W$ we obtain $$ \hat{f}(x_{0})=l(x_{0})Y $$ implying that  $$ \text{Var }\hat{f}(x_{0})=\sigma^{2}\left\Vert l(x_{0})\right\Vert ^{2}=\left(\begin{array}{ccccc} 1 &amp; x_{0} &amp; x_{0}^{2} &amp; \dots &amp; x_{0}^{d}\end{array}\right)\left(X^{t}WX\right)^{-1}X^{t}W^{2}X\left(X^{t}WX\right)^{-1}\left(\begin{array}{ccccc} 1 &amp; x_{0} &amp; x_{0}^{2} &amp; \dots &amp; x_{0}^{d}\end{array}\right)^{t}. $$ My approach: An induction using the formula for the inverse of a block matrix but I did not succeed.  The paper  by D. Ruppert and M. P. Wand derives an asymptotic expression for the variance for $n\rightarrow\infty$ in Theorem 4.1 but it is not clear that is increasing in the degree. Proof that the coefficients in an OLS model follow a t-distribution with (n-k) degrees of freedom Background  Suppose we have an Ordinary Least Squares model where we have $k$ coefficients in our regression model, $$\mathbf{y}=\mathbf{X}\mathbf{\beta} + \mathbf{\epsilon}$$  where $\mathbf{\beta}$ is an $(k\times1)$ vector of coefficients, $\mathbf{X}$ is the design matrix defined by  $$\mathbf{X} = \begin{pmatrix} 1 &amp; x_{11} &amp; x_{12} &amp; \dots &amp; x_{1\;(k-1)} \\ 1 &amp; x_{21} &amp; \dots  &amp;       &amp;   \vdots \\ \vdots &amp;   &amp; \ddots &amp;       &amp;   \vdots \\ 1 &amp; x_{n1} &amp; \dots  &amp; \dots &amp;   x_{n\;(k-1)}     \end{pmatrix}$$ and the errors are IID normal,  $$\mathbf{\epsilon} \sim \mathcal{N}\left(\mathbf{0},\sigma^2 \mathbf{I}\right) \;.$$  We minimize the sum-of-squared-errors by setting our estimates for $\mathbf{\beta}$ to be $$\mathbf{\hat{\beta}}= (\mathbf{X^T X})^{-1}\mathbf{X}^T \mathbf{y}\;. $$   An unbiased estimator of $\sigma^2$ is  $$s^2 = \frac{\left\Vert \mathbf{y}-\mathbf{\hat{y}}\right\Vert ^2}{n-p}$$ where $\mathbf{\hat{y}} \equiv \mathbf{X} \mathbf{\hat{\beta}}$ ().   The covariance of $\mathbf{\hat{\beta}}$ is given by $$\operatorname{Cov}\left(\mathbf{\hat{\beta}}\right) = \sigma^2 \mathbf{C}$$ where $\mathbf{C}\equiv(\mathbf{X}^T\mathbf{X})^{-1}$ () .  Question  How can I prove that for $\hat\beta_i$, $$\frac{\hat{\beta}_i - \beta_i} {s_{\hat{\beta}_i}} \sim t_{n-k}$$ where $t_{n-k}$ is a t-distribution with $(n-k)$ degrees of freedom, and the standard error of $\hat{\beta}_i$ is estimated by $s_{\hat{\beta}_i} = s\sqrt{c_{ii}}$.    My attempts  I know that for $n$ random variables sampled from $x\sim\mathcal{N}\left(\mu, \sigma^2\right)$, you can show that  $$\frac{\bar{x}-\mu}{s/\sqrt{n}} \sim t_{n-1} $$ by rewriting the LHS as $$\frac{ \left(\frac{\bar x - \mu}{\sigma/\sqrt{n}}\right)  }   {\sqrt{s^2/\sigma^2}}$$ and realizing that the numertor is a standard normal distribution, and the denominator is square root of a Chi-square distribution with df=(n-1) and divided by (n-1) ().  And therefore it follows a t-distribution with df=(n-1) ().  I was unable to extend this proof to my question...  Any ideas?  I'm aware of , but they don't explicitly prove it, they just give a rule of thumb, saying "each predictor costs you a degree of freedom". I am relatively new to Blender so please excuse me if this is rather amateur!   The bottom of my torus has poor surface quality, I have scaled the vertices down for a smaller (variable) tubular radius all the way around the torus and at the bottom the surface quality is awful, with inaccurate shading. Any ideas as to what is wrong and/or how I could fix this?  Thanks, itsanate Will an iOS reinstall make a jailbroken iOS device "unjailbroken" and “supported by Apple” again? Will an    Work on any jailbroken-iPhone and Make a jailbroken-iPhone "unjailbroken"? Make a jailbroken-iPhone "supported by Apple"?   Similarly, does such an iOS reinstall reset the iPhone (jailbroken or not) back to a "clean, factory default" state?  My near-term purpose: I have an iPhone 4S running iOS 5.1 I'd like to jailbreak to at least install better Google Voice integration like . How to install Wireshark I am new to Linux and have a need to install Wireshark 2.0.0 on VirtualBox's Xubuntu 14.04. I have already downloaded .tar.gz package and extracted it. Then I opened terminal in the wireshark folder and type /.configure with intention to follow it by make and sudo make install executions but the attempt as unsuccessful as sudo apt-get install wireshark. Could somebody help me how to install Wireshark step by step, please? There is a line in the middle of my stroke. How do I fix that? Where does gravity get its energy from? Show that $|z_1 + z_2|^2 < (1+C)|z_1|^2 + \left(1 + \frac{1}{C}\right) |z_2|^2$ Let $z_1$ and $z_2$ be two complex numbers. Show that there exists $C &gt; 0$ with   $$  |z_1 + z_2|^2 &lt; (1+C)|z_1|^2 + \left(1 + \frac{1}{C}\right) |z_2|^2. $$   I tried to simplify the L.H.S and R.H.S, SNF I was finally left to compare between a real number and a complex number  I really couldn't think of anything else. Please help. Few days ago I got sample of toroidal core from my friend. I don't know any technical details of core just know OD-10cm, ID-6cm, height- 5cm and wet about 1.845 kg(M4 grade CRGO). I want to make toroidal transformer using this core but how to calculate core area and maximum VA capacity of core.I take some photos and attach. How to remove symbolic link I was trying to create this symbolic link:  sudo ln -s /usr/share/phpmyadmin /var/www/phpmyadmin   but I accidentally typed:  sudo ln -s /usr/share/php,yad,in /var/www/phpmyadmin   So now I want to correct it but it says symbolic link already exist. How can I refinance or get out of a car loan I am upside down on? I purchased a car a little over 1 year ago a 2012 Dodge Avenger and recently went to get it refinanced and found out that because I'm so upside down that I can't get it refinanced I still owe over 15,000 and the car is only worth about 8000. I'm so bothered by this a few days ago I received a pre-approval letter from capital one bank for 35,000 so how can I go about doing this if I trade the car in I will have to put down a substantial amount of money in order to get out of this car. Can I just get the other car and turn the other one in. What should I do