Question: Can you match these 7 cultures to their inventions (without looking it up)?


[I] Measuring scales, chess, tractor, shampoo, buttons, ruler, sanitary napkin (maxipads), speakers, the zero, cataract surgery, wireless communication, ink, plastic surgery

[J] Odometer, lighthouse, vending machine, central heating, pizza, urn, crane, alarm clock, democracy, frappe coffee

[K] Telescope, Thermometer, Microscope, Corporate Finance, Investment banking, yacht, stock market & stock exchange

[L] Flight recorder, Google maps, ultrasound scanner, permaculture, cochlear implant, electric drill, WIFI

[M] Helicopter, Jet pack, space suit, synthetic rubber, satellite, electric powered railway stations, radiator

[N] MP3 player, bulletproof vest, fishing net, watchphone, touch screen, underfloor heating, smart prosthetic skin, juche

[O] Virtual reality, TV remote control, walkie talkie, the blimp, Google, lipstick, cafeterias, the weekend, pacemaker, defibrillator, woodstock, color television, monotheism, pawn shops, discount stores, vaccines: polio, cholero, bubonic plague, flashlight, capitalism, communism, krav maga, valium, prozac

In the tradition of Jehovah’s Witnesses, does Romans 6:23 mean that all people who are resurrected have their…

Since the Bible itself says there is to be a resurrection of the good and bad (Acts 24:15) how do Jehovah’s Witnesses explain their position that death blots out the sins of all?
If death blots out sin would not all those resurrected be without sin?

How to use IMPORTJSON function in google script to request a list of top gainers and their float [on hold]

I’m trying to write a google script that use the IMPORTJSON function to request a list of the top 10 stocks that gained value during the current day. I separated the code into two files. One file has the IMPORTJSON function, and the other has the function that request the data that I need. After requesting the data, the latter function sends it to me by email, but the email that I receive says “Error getting data”.This is the IMPORTJSON script:

    function IMPORTJSON(url,xpath){
    // /rates/EUR
    var res = UrlFetchApp.fetch(url);
    var content = res.getContentText();
    var json = JSON.parse(content);

    var patharray = xpath.split("/");

    for(var i=0;i

This is the function is supposed to request the data and send it by email:

    function requestAndSendData() {

    // Get the email address of the active user - that's you.
    var email = Session.getActiveUser().getEmail();

    // Get the name of the document to use as an email subject line.
    var subject = 'runner ';

    // Append a new string to the "url" variable to use as an email body.
    var quote  =IMPORTJSON("");
    // Send yourself an email with a link to the document.
    GmailApp.sendEmail(email, subject, quote);

I wrote the request function using the IEX API dcoumentation from this website IEX API Documentation

I am not really sure why I am getting this error. I would appreciate any help. Thanks.

Is a person likely to realize most of their ultimate potential in a three-day tennis clinic?

I was reading a magazine article about a thirty-something actress who never played tennis before, took a tennis clinic, and in the course of a three day weekend, rose to “high intermediate” level (a 4.0 or 4.5).

My understanding is that tennis players are rated from 1.0 (rank beginner) to 7.0 (world class). Your (local) tennis pro or instructor is likely to be about 6.0 on this scale.

Seeing the woman’s strong showing, are the tennis instructors likely to think, we have a “natural” here who’s likely to be “pro” caliber (6.0 or better)? Or is there more likely to be a natural ceiling of, say, 5.0 with her having reached “most” (80-90 percent) of her potential at the clinic itself?

Put another way, are the tennis ratings “exponential” (like the Richter scale for earthquakes)? That is, could it be ten times harder to reach 6.0 than 5.0, and 100 times harder to reach 7.0 than 5.0? Meaning that if some who exited a tennis clinic with a 4.5 rating could (with practice) bump it up to 5.0, that would be three times better, and 5.5 would be a tenfold improvement?

Where can I read about the theory behind GLMs in their most general form?

I was following along with the MIT Opencourseware “Statistics for Applications” and their analysis of GLMs only covers discussion of GLMs whose dependent variable $y$ is distributed according to a distribution in the canonical exponential family (i.e. of the form below):
P(y | theta) = h(y)exp(theta^Ty – A(theta))

If this is the case, then one can easily determine that $mu = mathbb{E}[Y] = A'(theta)$, and since $mu$ is related to the linear predictor via the link function $g$, we have
X^Tbeta = g(A'(theta)),qquad theta = (gcirc A’)^{-1}(X^Tbeta)

However, I’m curious about the more general case where the distribution belongs to the overdispersed exponential family:
P(mathbf y | theta,tau) = h(y,tau)expleft(frac{mathbf b(mathbf theta)^Tmathbf{T}(mathbf y) – A(theta)}{d(tau)}right)

Using the identity $mathbb{E}[nabla_thetaell] = 0$ where $ell$ is the log-likelihood function I was able to arrive at
[D_theta mathbf b]^Tmathbb{E}[mathbf T(mathbf y)] = nabla_theta A

but there are two problems with this. For one, the dimension of $mathbf b$ might not equal the dimension of $theta$, and even if it did this would not guarantee the matrix $D_thetamathbf b$ (the Jacobian of $mathbf b$) is invertible. Second, even if it were invertible, we would only arrive at
mathbb{E}[mathbf T(mathbf y)] = ([D_theta mathbf b]^T)^{-1}nabla_theta A

which doesn’t give us any direct correspondence between the parameter $theta$ and the linear predictor $mathbf X^Tbeta$, since the link function links $mathbb{E}[mathbf y]$ to $mathbf X^Tbeta$, not $mathbb{E}[mathbf T(mathbf y)]$.

So, how are GLMs of this form implemented? Where can I go to read more about how this works?