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We could just collect a bunch of data. For a material .What's the volume it occupies at some pressure and temperature?
对一种物质我们可以得到一系列测量数据,在给定的温度和气压下,它的体积是什么？
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Apple has been talking about... my manager and I were discussing online data storage.
苹果公司一直在讨论……我们经理和我也讨论过网络数据仓库。
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But it was still interesting to watch and to see unfold So, now I had this picture of real-live data of a real-life city operating in front of me.
但是看着这些事物一点点展开非常有趣,现在我得到了现实的城市里,现实生活的数据就在我面前运转。
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He introduced the concept of electronegativity which was a measure, therefore, it is quantitative, of the atom's ability to attract electrons within a covalent bond and developed a scale of electronegativity.
他引入了电负性这一概念,一个反映着原子在成共价键时吸引电子的能力的数据标度，因此这是数量上的，在共价电子中,电子原子能吸引电子,并发展为一定规模的电负性。
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Now, as an aside, there's another couple of layers at the very top above the stack and above the heap, but below the tech segment and those are called initialized data and uninitialized data.
题外话，在堆和栈的顶端,还有好几层，但是在技术段之下,他们被叫做初始化数据和未初始化数据。
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So the Priestly materials are found as a block in Leviticus, a large part of Numbers, and then they're scattered throughout Genesis and Exodus.
在《利未记》中，与祭司相关的内容占了很大一块，有很多的数据，它们都将贯穿在《创世纪》和《出埃及记》中。
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The good news is the elements in any one spot in a hash table are likely to be a small number, three, four, five.
好的一点是在哈希表中,任何一个存贮点上的元素数据是很小的,三个，四个，五个。
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Here's another graph of data on the metabolic syndrome showing how it differs by race and ethnicity.
这是另一幅关于代谢综合征的数据图,它表明代谢综合征在种族与民族间的差异
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There's a problem with that and the problem is that countries that are less successful don't keep data--that's a problem.
但这个研究方法也有问题,在那些不太成功的国家里,数据并不充分,这是个问题
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The figure you are quoting in New York times which came out of a report published by a French economist who lives in India John Kenneth. And the article was by Sangota, the New York Times editor.
你从纽约时报,某篇报道上摘下来的数据,出自一名住在印度的法国经济学家,之手，他叫约翰·克南斯,该文章由纽约时报的圣郭特所编辑。
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We want to have that definition clear because in fact we're going to, we might want tabulate heats of reaction, right, and of course want to know what the conditions are for the tabulated values apply. And we're going to want to calculate them from other quantities and again, we're going to need to know each case what are the relevant conditions?
我们想要明确这个定义,因为实际上我们会想把,反应热制成表格，当然我们会,想知道表中的数据在什么样的,条件下是有效的，我们会,想要从其他的量中算出它们,再一次，我们需要知道,每种情形下相关的条件是什么？
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It's visualizing data, visualizing information, moving around in real time.
这些数据和信息变得看得见，摸得着，在不断实时运转着。
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So I wrote a little program called "chart" that, based on some data I happened to hardcode into the program, shows us their numbers for the past several years.
我写了一个叫做“图表“的小程序，基于我在程序中硬编码的一些数据，向我们显示在过去几年中我们课程的人数。
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Conceptually, it's put at the top of my chunk of RAM, -- below it, goes initialized and uninitialized data -- - this is a fancy way of saying global variables come next -- and below that, comes what's called the heap.
概念上，它放置在内存块的顶端，在下面，已初始化的和未初始化的数据-,这是一种指明紧随其后的全局变量的设想方式-,在后来，引入了一个叫做堆的东西。
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If you look up in the corner of your Periodic Table on one of the sides, they have actually tabulated this for you.
你认真看一下角落上,在一个角落，应该已经为你们把数据列成表了。
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In particular, a very common thing people do with data is they extrapolate.
这种错误，在数据预测时,很常见。
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I've added the ability to have more complex data structures here. But I dropped a hint in the first lecture about what you could computer with things. In fact if you think for a second about that list, you could ask what can I compute with just that set of constructs?
我添加了使用更复杂的数据结构的能力,但是我在第一节课给大家了了一个,关于你可以用什么来做计算的暗示,实际上如果你思考,这个列表一会儿的话,你会问我可以用？
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Now, dereferencing works on y and because the pointers are sharing 13 that one pointee, they both see the 13.
现在，解引用在y上起作用了,因为指针共享,同一个指针数据，它们两个都看到了。
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You're not searching a million things.
你不是在一百万个数据中进行查找。
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Well, a cross-sectional study would mean that you'd get a random group of the population and measure them in 1970 and then take another random group five years later and measure them, but it wouldn't be the same people necessarily, it would be different groups of people.
那么,横断面研究就是,1970年你在人群中随意选择一组人,并记录下相关数据,然后,五年后再随意选择一组人记录数据,但这不需要是同一组人,通常是不同的人
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So we're to assume we can get to any piece of data, any instruction in constant time, and the second assumption we're going to make is that the basic primitive steps take constant time, same amount of time to compute. Again, not completely true, but it's a good model, so arithmetic operations, comparisons, things of that sort, we're all going to assume are basically in that in that particular model.
因此如果我们假设在恒定的时间内,我们可以取得任何一块数据,任何一种数据结构的话,我们要做的第二个假设就是,基本的原始操作计算花费的时间是恒定的,这个假设也不是完全正确的，但这个模型其实挺不错的，因此算法操作，比较，这一类的事情，我们在这个特定的模型中都假设是基本的,操作，花费的时间是恒定相同的。
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It's in a file called swap.C. Everything should be alphabetical so it should be toward the end of this week's packet and notice that it's almost identical except for a couple of syntax changes.
在一个叫做swap。c的文件中，对于这周末的数据包来说,所有的东西都是字母,请注意,除了几个语法改变,它几乎是一样的。
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In terms of primitive data, what we saw were two kinds: Right?
在基础数据方面，我们上节课学了两种类型对不对？
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A cohort study means you take the same group of people and follow them every five years for that period of time.
定群研究意味着你只有一组研究对象,然后在研究期间每五年进行一次记录数据
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This is useful especially when we get to details like forensics and looking at data on a hard drive, - 'cause if you don't know how much store-- how many bits are composing your file, you're never gonna recover that data or be able to access what you're looking for.
这是很有用的，尤其是在法庭上,查看硬盘驱动器里的数据时，如果你连-,文件有多少比特都不知道，你如何去恢复数据,获取你想要的信息呢。
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So you have these at your disposal.
所以说你在处理数据的时候可以用这些常数。
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It used to be a big, broad sheet and they made it smaller in 2005 and they started--they keep cutting out data.
曾经是很宽大的报纸,在2005年被缩小了,并且开始不断减少数据
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So in fact, it is the case, if you look at data, and by the way, that's the way I ended up setting a lot of these parameters and playing with it, was comparing what my simulation said to historical stock data.
所以实际上，在这个例子中，如果你看到数据，顺便说一下，这就是我设置很多变量,然后编程的方式，也就是把我的仿真程序得到的结果,和历史股票数据进行比较。
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In our--in this particular study we had some people estimate things low and some people estimate things high, but overall, this--these would represent overestimates.
在这个实验中,有些人低估了,而有些人高估了数据,但总体来说,结果显示人们高估了数据
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So, they started to do collecting of data on mortality and they developed something called actuarial science, which is estimating the probability of people living.
他们开始收集死亡率的数据,并且发展出精算学,用来估算人在各年龄段死亡的概率
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