christopheraden

> Have fun explaining to your spouse why your household's TV is showing more dating site ads than that of their friends.

The targeting is sometimes only _so_ good. While it works in aggregate well, sometimes there are laughable targetings, and it also depends on what audiences the advertiser wants to hit.

I get some pretty strange targeted ads through Facebook that don't seem at all relevant, and the "Why Am I Seeing This Ad" dropdown has very nebulous explanations ("Targeting Men between 25-35 in San Francisco"). I would imagine this problem to be similar with targeted ads on Live TV unless they keep the ads pretty generic or have vastly better targeting than FB or Google.

While it is a policy issue at its core, changing law and policy moves at a glacial pace, and it's not even a certainty that it'll get changed at all (the "nothing to hide" defense gets brought up a lot on these matters, and it's a pretty persuasive argument to those that can't recognize the fallacy). Technical solutions have the benefit of being much quicker to enact, albeit in a flawed way that is, if highly successful, a band-aid on a bullet wound.

Sure, using hypothesis tests could pick out some of the structured examples in the Datasaurus, but in practice, things are often more subtle. Goodness of Fit tests to check for normality, in particular, are a little bit thorny, lacking power in small sample sizes, and rejecting normality for slight departures in higher sample sizes. My experience has been with assumption checking that by the time a hypothesis test has sufficient evidence to reject an assumption, you'd usually be able to see it visually.

Until you get into high dimensions, it probably doesn't hurt too much to visualize the data. Additionally, it can be helpful to understand what signal has been left in the residuals (ex: you fit a linear model, but failed to include a quadratic term), which is something hypothesis tests aren't as good at telling you.

A couple questions.

> OLS works fine in classification problems. And it has advantages.

Do you have more explanation of these advantages? I read through the link you sent, and a bit more about linear probability models. Such things were never discussed in my statistics curriculum (BS, MS, PhD), except for motivating why logistic regression was necessary. I'm not sure I understand the economist's arguments in favor of LPM. Both the interpretation and the distribution of the test statistics will be totally different with OLS versus Logistic Regression, and the overall probability of a defunct project is pretty small ( \hat{P(y=0)} = .07 )--enough where there would be pretty big differences. To be clear, my reservation is with the p-values in the OLS model, not the predictions it generates. While the models agree on the direction of the covariates, the magnitudes are quite different, even when you convert logit/probit to be on the same scale as LPM.

> Multicollinearity refers to perfect multicollinearity.

Perfect multicollinearity will definitely mess up the estimation, but even if Score and Comments are not perfectly collinear, it's difficult to talk about each one's effect on the probability individually, as is the interpretation of coefficients in a (logistic) regression. What does the VIFs look like for Score and Comments, in particular?

> I mention R² as one measure of predictive power.

But the outcome is binary, so you'll have a similar issue as Minimaxir's first point about OLS. If you wanted to talk about prediction accuracy, what about a confusion matrix, misclassification rate, or specificity/sensitivity/F1? Granted, you'll not want to predict on the same tagged examples that you trained the model on, but maybe you could split it 80-20? Or tag another 20-50? There are also R²-like measures you can use when the dependent variable is binary (a whole class of pseudo-R² measures).

I would be curious to see the relationship between these predictors and the response. It's usually been my experience that linearity is a strong assumption to make, and that I'd expect for something like comments or score that once it reached a certain threshold, there was no extra value added by getting more comments/score. Are the log-score and log-comments linear over their entire support?

What about something ala Neovim? I've only ever looked at Tex from the perspective of a user (I don't program my own macros too often), so I don't know how hard it'd be, but why not a language overhaul?

I use SAS professionally at my job, and R in all my academic/hobby work. R has a couple packages that give similar functionality as PROC SQL (about 95% of my SAS workflow, since it's far nicer than data steps for a lot of things). There's an ODBC package (RODBC), as well as SQLDF, which allows you to use SQL queries to manipulate data frames in R.

While there is (almost?) always a way to do a SQL query using idiomatic R, I have to admit that sometimes my brain thinks up a solution in SQL faster (a product of upbringing).

>What kind of negative effect might result from a bunch of unqualified high school teachers teaching CS poorly? Is some exposure better than none regardless of teaching quality?

Is this problem similar enough to math that we can draw on data about mathematics education in schools? You can make a lot more money in industry with a math degree than you can teaching math to grade schoolers.

Certainly, we hear tons of stories about unqualified math and science teachers, and all the harm they do to a desire to learn math, but I'm sure some students get exposure to math that wouldn't normally have much desire to immerse themselves in it.

Excellent! I guess now is the time for me to finally make the move over to El Capitan, since your usage seems pretty similar to mine. Thanks!

Can anyone comment if the GM fixes the battery problems Beta 1 had? I was on the first beta awhile back, and my 2012 rMBP got about an hour battery life (I usually get closer to ~3-4) and was always hot to the touch.

The same thing happened to me with Mavericks, so I chalked it up to Apple not optimizing the early betas for battery life.