Jan, what do you make of Stevie leaving Florian Wirtz on the bench saying he has no place for him in his XI if he were Arne Slott?

一月，你怎么看史蒂夫把弗洛里安·维尔茨放在替补席上的？说如果阿尔内·斯洛特，的首发阵容里就没有维尔茨的位置。

Edit, Undo, minus 2 times mu times the sum from i is equal to 1 to n of xi.

编辑，撤销，2倍均值乘以从i等于1到n的xi之和。

What's the sampling distribution for all of the xi's?

所有xi的抽样分布什么?

So you take each of the data points in the population-- xi.

因此，您获取总体中的每个数据点 - xi。

The songs can also be translated, but they communicate emotionally whether xi or not the words are understood.

歌曲也可以翻译，但无论歌词否被理解，它们都会传达情感。

The sum of the xi's is sufficient.

xi 的总和就足够了。

Each of these, just so you can relate to the formula, each of that is xi minus the mean squared.

每个数据点，为了让你能与公式对应起来，都xi均值的平方。

This numerator is the sum of each of the xi minus the mean squared from i equals 1 to N. And that ended up to be 38.80.

这个分子从i等于1到N的每个xi均值后的平方和。最终结果38.80。

For p, given all of my data, this is going to be proportional to n choose some of the xi's.

对于 p，给我的所有数据，这将与 n 选择一些 习 成正比。

You can just say, OK, for each xi I just perform this operation.

你可以直接说，好吧，对于每个xi我只需要执行这个操作。

And all of the xi's is n-dimensional.

而所有的xi都n维的。

I goes from 1 to n. 1 over, I'll just write down the sampling distribution, square root 2 pi sigma e to the minus 1 half xi minus x bar squared over sigma squared.

我从 1 到 n. 1， 我只写下采样分布，平方根 2 pi sigma e 到负 1 半 习 x bar 的平方超过 sigma 的平方。

And so if I knew things about beta distributions coming in, my updated inferences about beta distributions, and I can tell you all the properties about this inference after seeing some of the xi and n. So that's our inference.

因此，如果我了解了有关 beta 分布的信息，我更新了有关 beta 分布的推论，在看到一些 xi 和 n 之后， 我可以告诉您有关此推论的所有属性。 这就我们的推论。

1 Minus p to the n minus sum of the xi's plus beta minus 1.

1p到nxi的和加上β1。

With parameter p, then we would have f, sum of the xi's, i goes from 1 to n, given p. This is going to be n choose sum of the xi's.

使用参数 p， 那么我们将得到 f，习 之和，i 从 1 到 n，给 p。 这将 习 的 n 选择和。

If the xi's were normally distributed, this just happens.

如果 xi 正态分布的, 这种情况就会发生。

Some of the XIs is sufficient, and so I could just model some of the XIs.

一些 XI 就足够了，因此我可以只对一些 XI 进行建模。

But basically all I need to know is X bar, some of the XIs over N. And I'll put an N in here to make you remember that's a function of N. It has something to do with N. N is important here.

但基本上我需要知道的 X 条，一些 XI 超过 N。 我在这里放一个 N， 让你记住这 N 的函数。 它与 N 有关。

We see some xi's from a Bernoulli distribution.

我们从伯努利分布中看到了一些xi。

Exactly, if all these xi's are independent.

确切地说, 如果所有这些xi都独立的。