Placing adjectives in front of nouns
Bachelor thesis or Bachelor's thesis
Understanding Python super() with __init__() methods I'm trying to understand the use of super(). From the looks of it, both child classes can be created, just fine.   I'm curious to know about the actual difference between the following 2 child classes.  class Base(object):     def __init__(self):         print "Base created"  class ChildA(Base):     def __init__(self):         Base.__init__(self)  class ChildB(Base):     def __init__(self):         super(ChildB, self).__init__()  ChildA()  ChildB() Accessing the iPhone's Call log with the iPhone SDK Modifying list while iterating Under which assumptions a regression can be interpreted causally? First, don't panic. Yes, there are many similar question on this site. But I believe none gives a conclusive answer to the question below. Please bear with me.  Consider a data generation process $\text{D}_X(x_1, ... , x_n|\theta)$, where $\text{D}_X(\cdot)$ is a joint density function, with $n$ variables and parameter set $\theta$. It is well known that a regression of the form $x_n = f(x_1, ... , x_{n-1}|\theta)$ is estimating a conditional mean of the joint distribution, namely, $\text{E}(x_n|x_1,...,x_{n-1})$. In the specific case of a linear regression, we have something like $$ x_n = \theta_0 + \theta_1 x_1 + ... + \theta_{n-1}x_{n-1} + \epsilon $$ The question is: under which assumptions of the DGP $\text{D}_X(\cdot)$ can we infer the regression (linear or not) represents a causal relationship? It is well known that experimental data does allow for such interpretation. For what I can read elsewhere, it seems the condition required on the DGP is exogeneity: $$ \text{E}(x_1, ... x_{n-1}|\epsilon) = 0$$ The nature of randomisation involved in experimental data ensures the above is the case. The story goes then to argue why observational data normally fails in achieving such condition, reasons including omitted variable bias, reverse causality, self-selection, measurement errors, and so on. I am however uncertain about this condition. It seems too weak to encompass all potential arguments against regression implying causality. Hence my question above. UPDATE: I am not assuming any causal structure within my DGP. I am assuming that the DGP is complete, in the sense that there must be some causality (an ontological position it could well be debated), and all relevant variables are included. The key is to identify the set of assumptions which ensure me causality goes from certain variables to the other, without assuming from the outset such direction of causality.  Many similar posts on the site spend time mentioning why correlation does not imply causation, without providing hard arguments about when it does. That is the case, for instance, of  very popular post. Additionally, in the  on the site about the topic, the accepted answer gives the very vague answer:  Expose all hidden variables and you have causation.  I do not know how to translate that to my question above. Neither is the second most upvoted answer. And so on. That is why I believe this post does not have an answer elsewhere. Simplify $\sum (x_i- \mu)^2$ How can I simplify this relation? $$\sum_{i = 1}^m(X_i-\mu)^2=\sum_{i = 1}^m(X_i-\bar{X}_m)^2+m(\bar{X}_m-\mu)^2$$ I know how to set up: $$\sum_{i = 1}^m(X_i-\mu)^2=\sum_{i = 1}^m[(X_i-\bar{X}_m)+(\bar{X}_m-\mu)]^2$$ but when I open the bracket, how do I handle the mixed term? $$2\sum_{i = 1}^m(X_i-\bar{X}_m)(\bar{X}_m-\mu)$$  Thank you! Why does C++ not let baseclasses implement a derived class' inherited interface? Words to describe people with good/poor vocabularies Words to describe people with good/poor vocabularies. I am looking for words which I can use to describe characters and I would also like to describe their vocabularies, and explain how their vocabularies stem from their upbringing Twitter Bootstrap uses these break points for responsive design:  Large Display: 1200px Default: 980px Tablet: 768px Mobile: 480px  A client said they are using 1280px for their Large Display breakpoint.  Is there an industry standard for these or are they just made up as we go right now? Do Stack Overflow and other Stack Exchange sites have an app I can use? Why won't my follower follow me after the "A Night to Remember" quest line? At the end of the "A Night to Remember" quest line, my follower came with me when I was teleported to the Misty Grove. Once there, I told my follower to wait.  Now, when I was teleported back to the real world, my follower's not with me: presumably because he's still waiting at the Misty Grove. I've waited for some time and still he hasn't come back.  How can I get my follower back? how to prove that this positive sequence converges to 1 I have a positive infinite sequence $a_n$ and I know that $a_n$$a_{n+1}$ converges to 1 and also $a_n$$a_{n+2}$ converges to 1.  Why does it mean that $a_n$ also converges to 1? How can I prove it?  Thanks. Why is the notation $\frac{\partial f}{\partial x}$ used? Interpretation of simple predictions to odds ratios in logistic regression I'm somewhat new to using logistic regression, and a bit confused by a discrepancy between  my interpretations of the following values which I thought would be the same:    exponentiated beta values  predicted probability of the outcome using beta values.    Here is a simplified version of the model I am using, where undernutrition and insurance are both binary, and wealth is continuous:  Under.Nutrition ~ insurance + wealth   My (actual) model returns an exponentiated beta value of .8 for insurance, which I would interpret as:     "The probability of being undernourished for an insured individual is .8 times the probability of being undernourished for an uninsured individual."   However, when I calculate the difference in probabilities for individuals by putting in values of 0 and 1 into the insurance variable and the mean value for wealth, the difference in undernutrition is only .04. That is calculated as follows:   Probability Undernourished = exp(β0 + β1*Insurance + β2*Wealth) /                              (1+exp(β0 + β1*Insurance + β2*wealth))   I would really appreciate it if someone could explain why these values are different, and what a better interpretation (particularly for the second value) might be.     Further Clarification Edits As I understand it, the probability of being under-nourished for an uninsured person (where B1 corresponds to insurance) is:  Prob(Unins) = exp(β0 + β1*0 + β2*Wealth) /               (1+exp(β0 + β1*0+ β2*wealth))   While the Probability of being under-nourished for an insured person is:  Prob(Ins)= exp(β0 + β1*1 + β2*Wealth) /            (1+exp(β0 + β1*1+ β2*wealth))   The odds of being undernourished for an uninsured person compared to an insured person is:  exp(B1)   Is there a way to translate between these values (mathematically)? I'm still a bit confused by this equation (where I should probably be a different value on the RHS):  Prob(Ins) - Prob(Unins) != exp(B)   In layman's terms, the question is why doesn't insuring an individual change their probability of being under-nourished as much as the odds ratio indicates it does?  In my data, Prob(Ins) - Prob(Unins) = .04, where the exponentiated beta value is .8 (so why is the difference not .2?) Find $\sum\limits_{k\, \text{ odd}} \frac{2(k^2-1)}{k^4+k^2+1}$ Conditional clause (then) In this first instance the main clause only stands alone if you  omit (then).  But in the other example it clearly has a main clause. So would this be classed as an embedded main clause as it only works as a independent clause if it's adjusted and or if it's joined with a dependent clause.  If I have enough money, then I will buy a new car.  If it rains, the picnic will be cancelled.  The picnic will be cancelled if it rains. Question on determinants of matrices changing between integer matrices The following problem came up from a though I had while reading:  Let's say we have $M=\mathbb{Z}^n$ and we have another free $\mathbb{Z}$-module, $N$, inside of $M$ also with rank $n$.  We know we can make a matrix $A$ that changes $N$ to its representation in $M$ (ie has as columns the basis $N$ expressed in coordinates coming from the basis of $M$). If the index of $N$ in $M$ is $m$, I am pretty sure just from tinkering that the determinant of $A$ should also be $m$, however, I have not been able to show this.  It is unclear to me how to proceed. I though maybe I could do something related to the points in $\mathbb{Z}^n$ that are within the unit box of $N$, but I cannot really make sense of this.  Thank you for any help or direction. mysql select distinct rows into a comma delimited list column Forgotten iOS Restrictions Password/Passcode What's the difference between mobile and desktop processors? I just read about the new Samsung Galaxy Note Edge having a 2.7&nbsp;GHz quad-core processor and 3GB of RAM.  The laptop I bought last year by HP is 4&nbsp;GB of RAM and 2.3&nbsp;GHz quad-core and my iMac is even older and is 2.5&nbsp;GHz i5.  Does this mean that the new Samsung gadget is more powerful than my desktop?  Is the 2.7&nbsp;GHz the same kind of GHz as non-mobile devices (is it scaled up, or compared, etc.)?  Why, in terms of power, do modern computers not have two of those Samsung quad-core processors running in parallel pushing out 5.4&nbsp;GHz processing power for the amount of electrical energy as two Galaxy Note batteries? How can I install apt packages non-interactively? If I run:  sudo apt-get --yes install postfix    or  sudo bash -c 'yes | apt-get --yes install postfix'   an interactive prompt appears to configure postfix. I need to automate the installation of postfix (I can modify the configuration files after the install).  Is there some magic that lets me install postfix (and other package) without human intervention?