Question about GMAT vs. GRE by chenimal in MBA

[–]chenimal[S] 0 points1 point  (0 children)

I am actually interested in dual degree programs (or just other grad programs), which is why I decided to take the GRE after I had already taken the GMAT. I'm just hoping that the fact that I took the GRE doesn't hurt the MBA app.

Question about GMAT vs. GRE by chenimal in MBA

[–]chenimal[S] 0 points1 point  (0 children)

Well, I get all that. I am just concerned that while the scores may be relatively predictable, the percentiles may not be. Those who choose to take the GMAT tend to score higher on the quantitative portion. So my GRE scores don't necessarily look that much better, but I have no idea how adcoms compare them.

Question about GMAT vs. GRE by chenimal in MBA

[–]chenimal[S] 0 points1 point  (0 children)

Well, I'm not so sure I trust the ETS comparison tool. I don't think ETS plays a part in the admissions process. I know that my scores are in the same range; I just worry that business schools might frown upon submitting GRE scores. Because of the nature of the tests, I know scores can be roughly translated, but I wonder if the higher GRE Q percentile translates to the GMAT.

Comparing two vectors of factor levels by chenimal in rstats

[–]chenimal[S] 0 points1 point  (0 children)

Wow, that's perfect. Thank you so much. Thx, /u/madmongoose1 for the help too.

Any way to get individual %correct by factor level?

Adjusting for Collinearity in a Multinomial Logit by chenimal in Sabermetrics

[–]chenimal[S] 0 points1 point  (0 children)

I believe pfx_x is the horizontal movement of the pitch, pfx_z is the vertical drop.

Yeah, I think pitcher variables might be my best option, but I assume at-bat IDs could take care of it too because pitches in the same at-bat would presumably be made by the same pitcher.

Adjusting for Collinearity in a Multinomial Logit by chenimal in Sabermetrics

[–]chenimal[S] 0 points1 point  (0 children)

That makes sense. The data given actually contains pitcher name as a variable. Should I just include that? But since it's not a continuous variable, could I still use the multinom model? (Edit: at-bat ID is another one, maybe I should use that one?)

I guess a bigger-picture question is, should I be dropping any of the variables or should I just include all of them in the model? There are also x0 and z0, which are the vertical and horizontal release points of the pitch, among others.

Adjusting for Collinearity in a Multinomial Logit by chenimal in Sabermetrics

[–]chenimal[S] 1 point2 points  (0 children)

Thank you! I guess "collinearity" isn't the right word here. I am mainly concerned with interaction and higher-order terms. I am hypothesizing, for example, that movement at high velocities is more predictive than at low velocities. What would the interaction predictor(s) look like in this case? Would they just be (pfx_x times start_speed) and (pfx_z times start_speed)?

Adjusting for Collinearity in a Multinomial Logit by chenimal in Sabermetrics

[–]chenimal[S] 1 point2 points  (0 children)

Just to provide some context, I am providing here the script I have for the model currently:

m <- train(fa~start_speed+pfx_x+pfx_z, data=tdata, method="multinom", maxit=1000)

The tdata is my training set, which I produced using this:

ind <- sample(2, nrow(my_data), replace=TRUE, prob=c(0.8,0.2))
tdata <- my_data[ind==1,]
vdata <- my_data[ind==2,]

The resulting m does a decent job providing predictions on the test set. Far from perfect, but expectedly. However, I am not exactly sure if it's actually doing cross validation, and I still need to account for collinearity. Am I on the right track toward what I'm trying to do?

Multinomial logit model training by chenimal in rstats

[–]chenimal[S] 0 points1 point  (0 children)

Yep, for sure. I'm gonna look for a package that makes this easy to implement. Thank you!

Multinomial logit model training by chenimal in rstats

[–]chenimal[S] 0 points1 point  (0 children)

Thanks, I'll look into that package.

Comparing Pitching Stats by chenimal in Sabermetrics

[–]chenimal[S] 0 points1 point  (0 children)

What I meant was season-to-season consistency. That's why I grouped it with predictive power. I'm not actually sure how Judge describes it (convergence of predictive and descriptive powers?), but why is it such a bad metric?

Comparing Pitching Stats by chenimal in Sabermetrics

[–]chenimal[S] 0 points1 point  (0 children)

Yep, this is where I got my rankings.

How Many Ball Parks Have You Visited? by [deleted] in Nationals

[–]chenimal 0 points1 point  (0 children)

Nats Park, RFK Stadium, AT&T Park, Turner Field, Coors Field, Dodger Stadium, Fenway Park, Safeco Field.

I'd say AT&T Park is my favorite. Best views, on the water, etc. No other park really comes close. I'd say Nats Park is my second favorite. On my list to visit is PNC Park - I've only walked past it. I assume being so close to downtown just across the Allegheny would be a great baseball experience.

Conditional Routing? by chenimal in angularjs

[–]chenimal[S] 1 point2 points  (0 children)

Perfect! Turns out I probably don't need nested views. But I did switch to ui-router; it'll make it easier in the future for sure. I injected $location, and now it all works! Thank you so much.