It doesn't matter whether you're debating from a podium or you're just fighting with your mom at home: a few simple rules apply to arguing like a pro. When you use effective communication, a well placed argument, and really pay attention to what your opponent is saying, you can make just about any opinion sound like the right one.

专车是时代车轮，谁也挡不住，因为没有一个乘客愿意回到脏乱差贵的出租车时代。有关部门不管怎么限制，都不会挡住车轮往前，因为这不是一个你让我用什么我就得用什么的时代，乘客都不担心专车并且那么爱用专车，有关部门在为谁执法呢？装逼。（作业本）

@刘春 ：最近碰到的企业家，都成了资本家，只关心资本运作和资本回报，不太关心企业自身的发展创新。最近碰到的老百姓，都是股评家，不是生活家，似乎生活的喜怒哀落、未来的生老病死，都寄托在股市上了。最近遇到的文化人，都忙着交流个股信息，忙着当股神，没空讨论文化和作品，也不像文人了。这就是中国牛？

　　每天，女孩都会来他店里吃饭，坐靠窗位置，点上两份套餐。

　　每次他问起几位用餐，她总是羞涩说两个人，可后却是一个人默默吃完。

　　他想也许曾经有个人陪在女孩身边，但那人已经不在了。

　　终于有天，他想过去问聆听女孩的故事，却听到那女孩自言自语：“两份的量也那么少，不够吃啊！”

“我们宿舍六个人，今天下午四个去打球了，只剩下我和一个老乡，老乡感冒了在被窝躺着，手里拿了好多纸巾，我正在换裤子，就在这时候，那四个打球的回来了，谁知他们开门后看见我在提裤子，老乡在床上，还有地上的纸团，说了句不好意思，回来早了，然后门一关又走了，就剩我和老乡两个人在那凌乱

　某顾客抱怨说：“脚太大真不是件好事，最难买到合适的鞋，只好将旧鞋补了凑合着穿。”“兄弟你言重了，俗话说得好：男人嘴大吃四方，女人嘴大情嚣张；男人脚大江山稳，女人脚大爱逞能……哎哟！”鞋匠的话还没说完，就被人从背后狠狠地踹了一脚。鞋匠压低声音：“肯定是我老婆来了！”

长沙股民170万四倍融资全仓中国中车 两天赔光跳楼

浅谈中国古代科技发展 自封建社会中期长期停滞并被西方超越的原因

一个有着“黄金手臂”的老爷爷..... 因为他的血里含有一种特殊的抗体.... 过去的60年里他一直坚持着献了1000多袋血...... 拯救了超过200万婴儿的生命.....

开始于一九○一年的清末新政开启了大清帝国全面深化改革的大幕，太后和整个朝廷有改革的诚意；也可否认，庆王奕劻作为政治经验丰富的资深官僚有着所谓“老成持重”、“老成谋国”的政治能力和政治素质，是个接近于合格的新政领袖。但是，庆王奕劻缺少了政治家、治国理政的最为重要的素质，那就是政治道德和底线伦理。在一个道德上存在极大负面评价人、一个官德屡屡被否定的人，居然成为大清帝国新一轮改革的旗手，这不能不说是一种可悲。贪官奕劻能否引领大清帝国完成新政改革的任务，战胜革命的步伐，实现民族独立和振兴的中国梦？

哈哈我们的Lab都用这个
BTW,edx的确很不错
IMF都在上面开课了...

You can make a design matrix X for a two group comparison either using model.matrix or simply with:

X = cbind(rep(1,nx + ny),rep(c(0,1),c(nx, ny)))

For a comparison of two groups, where the first group has nx=5 samples, and the second group has ny=7 samples, what is the element in the 1st row and 1st column of X^T X?

Suppose we have an experiment with two species A and B, and two conditions: control and treated.

species <- factor(c("A","A","B","B"))
condition <- factor(c("control","treated","control","treated"))

And we will use a formula of '~ species + condition'.

The model matrix is then:

model.matrix(~ species + condition)

Suppose we want to build a contrast of coefficients for the above experimental design.

You can either figure this question out through logic, by looking at the design matrix, or using the contrast() function from the contrast library. The contrast vector is returned as contrast(...)$X.

What should the contrast vector be, for the contrast of (species=B and condition=control) vs (species=A and condition=treatment)? Assume that the beta vector from the model fit by R is: Intercept, speciesB, conditiontreated.

Robust linear models
In calculating the solution and its estimated error in the standard linear model, we minimize the squared errors. This involves a sum of squares from all the data points, which means that a few outlier data points can have a large influence on the solution. In addition, the errors are assumed to be have constant variance (called homoskedasticity), which might not always hold true (when this is not true, it is called heteroskedasticity). Methods have been developed therefore to generate more robust solutions, which behave well in the presence of outliers, or when the distributional assumptions are not met. A number of these are mentioned on the robust statistics page on the CRAN website. For more background, there is also a Wikipedia article with references.

最近朋友圈动态：随便走了几步就在运动排行榜排了第一。买我面膜茶叶洗衣粉的太多了，累死了。点一下抢打车的红包，一分钱也是爱啊。哈佛耶鲁微软题目看你能做对几道，做对有个卵用？能帮我孩子投一票吗？能帮我朋友投一票吗？能帮我们企业投一票吗？投你个鬼啊，你反手能摸到你自己的肚脐眼吗？

一个同学的状态：前两天吃东西吃坏肚纸了，菊部地区有雷阵雨。

Generalized linear models
In the standard linear model, we did not make any assumptions about the distribution of Y, though in some cases we can gain better estimates if we know that Y is, for example restricted to non-negative integers 0,1,2,…, or restricted to the interval [0,1]. A framework for analyzing such cases is referred to as generalized linear models, commonly abbreviated as GLMs. The two key components of the GLM are the link function and a probability distribution. The link function g connects our familiar matrix product Xβ to the Y values through:

E(Y)=g−1(Xβ)
There is a function in base R for fitting GLMs, which is glm and uses a familiar form as lm, with additional arguments including family which specifies the distributional assumption of Y. Some examples of the use of GLMs are shown at the Quick R website. There are a number of references for GLMs on the Wikipedia page.

Mixed effects linear models
In the standard linear model, we assumed that the matrix X was fixed and not random. For example, we measured the frictional coefficients for each leg pair, and in the push and pull direction. The fact that an observation had a 1 for a given column in X was not random, but dictated by the experimental design. However, in the father and son heights example, we did not fix the values of the father’s heights, but observed these (and likely these were measured with some error). A framework for studying the effect of the randomness for various columns in X is referred to as mixed effects models, which implies that some effects are fixed and some effects are random. One of the most popular packages in R for fitting linear mixed effects models is lme4 which has an accompanying paper on Fitting Linear Mixed-Effects Models using lme4. There is also a Wikipedia page with more references.

Bayesian linear models
The approach presented here focused the estimation of β which minimized the squared error and then calculating its standard error. An alternative approach to the statistical inference about β is using Bayesian methods. Bayesian methods first involve the formulation of prior distributional beliefs about model parameters, and then calculating either directly or computing using computational methods the posterior distribution of β, from which we can examine the posterior mode (the most likely value), and credible intervals. In addition, many models can be connected together in what is referred to as a hierarchical model. A good reference for Bayesian hierarchical models is Bayesian Data Analysis, and some software for computing Bayesian linear models can be found on the Bayes page on CRAN. Some well known software for computing Bayesian models are stan and BUGS.

Penalized linear models
Penalized linear models introduce an penalty term to the minimization in addition to the squares of the residuals. These are typically of the form, λ∑i|βi|k, for different numbers k. The motivation for this extra term is to introduce some stability of the β, and requires picking a parameter λ which tunes how much the least squares part and how much the penalty affect the solution. When k=2, this is referred to as ridge regression, Tikhonov regularization, or L2 regularization. When k=1, this is referred to as LASSO or L1 regularization. A good reference for these penalized linear models is the Elements of Statistical Learning textbook, which is available as a free pdf. Some R packages which implement penalized linear models are the lm.ridge function in the MASS package, the lars package, and the glmnet package.

Many simultaneous linear models
Finally, in the next courses we will encounter an approach to improving the inference in linear models when Y is not a single column vector M×1, but we have thousands of such models to fit, M×N. Typically such data is presented in a tall format, N×M, where there are M samples for each linear model (often in the range 6−100), and N linear models to fit, for example a linear model for each of thousands of genes. The improved statistical inference comes from modeling some of the terms, such as the variance, as a hierarchical model, essentially sharing information across the N linear models. This will be covered in more detail in PH525.3x. Two of the first references for this approach are Lönnstedt and Speed, Replicated microarray data (2002), and Smyth, Linear models and empirical bayes methods for assessing differential expression in microarray experiments (2004). The methods in the Smyth paper are employed in the limma package on Bioconductor. The User Guide of the limma package contains extensive documentation and examples for analysis of genomics data.

     马化腾：“现在面临uber的竞争，中国一直有外面的互联网公司进来，强龙和地头蛇谁赢呢？目前看无一例外都是地头蛇赢了，没有一家打得过地头蛇。本地创业者所有身家性命都在这里，一天可以做决策几次，跨国公司还要向老板汇报，老板还有时差。这里又有资金，PE很活跃，对中国本土的创业者还是很有信心的，很接地气，而且主动思辨，一条路走不通试几条路。”
     
     这一点，BLEUS不敢苟同，Uber不是简单的外来和尚，Uber团队确实有不少国外过来的精英，但同样，Uber的本土化策略，也让这个团队吸纳了最懂这个城市的人，每个城市精干的三、五个人，或许就有一个是地头蛇，他们不再是层层上报，而是扁平管理决策，并且运营策略共享，快速复制，这一点，已经远远不是当年的MSN可以比拟。
     
     这个观点，如果朋友圈有PONY的朋友，可以转达，让PONY多了解Uber，或许他会改变一些看法。
     
     明天，周六下午两点，BLUES将在深圳华强群星广场北苏宁电器，对话手机测评的“大咖”王自如，现场交流、现场评测手机，聊聊手机生态圈的O2O线上线下融合的模式。
     
     （BLUES【公众号ID：bluemidou】，迅雷产品总监，原YY语音、腾讯高级产品经理，WeMedia自媒体联盟年度自媒体）

与半年前的预测相比，我们将2015年实际GDP增速的基准预测值由7.1%下调至7.0%。对2015年固定资产投资增速的最新预测为12.6%，对社会消费品零售总额增速的最新预测为10.7%，均比原预测略有下降。出口增速预测从原先的6.9%下调至2.5%；进口增速预测则由5.1%大幅下调至-4.2%。贸易顺差与GDP的比例的预测上调1个百分点，达到4.8%；对经常项目顺差与GDP的比例的预测则上调0.5个百分点，达到2.9%

　美银美林(Bank of America Merrill Lynch)周二(6月9日)发布报告称，中国宽松货币政策、为加入SDR放开资本项目、资本流出、以及经济增长迟缓，将给人民币带来下跌压力;基本预期未来12个月将下跌5%-10%。

世卫组织今天指出韩国医疗体制和文化是导致疫病扩散的原因。

美国超越沙特成世界第一大石油生产国

英国内政部一名高级官员表示，中国和俄罗斯的情报部门获取将近一百万份斯诺登泄露的机密文件，里面包含英国和美国间谍的身份信息，“可以确定，俄罗斯和中国获得这些信息”。在此之后，英国军情6处不得不将这些间谍从敌对国家撤回，“不止是俄罗斯和中国，可能还有其它国家看到了”。该官员指责斯诺登带来了不可估量的危害，“手中沾满鲜血”。

BBC报道，昨天欧洲杯预选赛克罗地亚主场1-1战平意大利，赛场草坪的一处纳粹标识超越比赛本身成为了关注焦点。克罗地亚足协已就此道歉，称已向欧足协报告，标识是赛前24-48小时弄上去的，责任人还不清楚。中场休息时，工作人员尝试掩盖标识，但是没有成功。

维基媒体基金会宣布，基金会管理的所有网站将默认启用HTTPS，这一决定即时生效。维基媒体基金会称，此举是为保护访问者的隐私和安全，让用户能安全和不被审查的自由获取知识。维基媒体旗下网站如维基百科，从2011年起就支持HTTPS，但没有默认启用而需要手动在地址栏输入HTTPS，从2013年开始HTTPS对登录用户默认启用，现在则对所有用户默认启用。