@ said:

@ said:

@ said:

Eh @ I think you're overreacting...

To all you weary readers, read this post and find out for yourself if you think @ was being rude: https://classic.7sage.com/discussion/#/discussion/13046/law-school-predictor

( @ I don't think you were...)

Cool man, how about you read that little thesis he posted and tell me you don't consider (15 times) to be very insulting?

You're both being ridiculously combative.

What can I say, baseless personal attacks are not something I tolerate in any way shape or form.

@ said:

Eh @ I think you're overreacting...

To all you weary readers, read this post and find out for yourself if you think @ was being rude: https://classic.7sage.com/discussion/#/discussion/13046/law-school-predictor

( @ I don't think you were...)

Cool man, how about you read that little thesis he posted and tell me you don't consider (15 times) to be very insulting?

@ Cool bro, I stand corrected on your score, you proved you weren't lying about one thing. You could easily redact your name on your works to prove those? Seems like you talk a lot about stats without back anything up, and I refuted every point you made which you failed to acknowledge at all. The data, the methods, the accuracy, everything.

Let's just get this story straight though, deliberately insulting people's intelligence and education, and demenaning valuable work you couldn't hope to achieve is what led to this. Not some personal vendetta. Stop trying to attribute claims to me, concocting your story or diliberatly misrepresenting you. Get real.

@

YOU POSTED A BLANK SCREEN WITH NO LINK GENIUS.

EXPOSED YOU LOSER, KEEP RUNNING THAT MOUTH OF YOURS. No one will ever obsess over your corny delusional self, I just wanted to expose and embarrass you off this forum. Hard to do with someone who is quite clearly insane like yourself.

Get an education

Stop Lying

Get a job

get a life

AND STOP LIKING YOUR OWN COMMENTS YOU ARE SOOOO PATHETIC ITS UNREAL

@ said:

Dear @,

I will not post links to published works because they would expose my identity to you and you seem to be a somewhat obsessive individual as evidenced by the fact that you made this post and dragged my LSAT score into a discussion of a statistical regressions inadequacies because I offended you by suggesting you were incorrect about something.

I am attaching screenshots of my LSAT score with my name redacted for the same reason I won't post links to published papers. Hopefully, this is enough to soothe your anxieties.

I'm not actually in the practice of prolifically posting online so I don't know how to post a picture on a forum. This link to imgur, the first source I looked up capable of letting me, will have to work to soothe your paranoid anxieties.

https://imgur.com/a/e6CGJ

If you want to tell me how, I'd be glad to post them in another format.

However, while you are at it you might want to reconsider your theory of the way the world works. Because right now your version of reality features me coming onto 7sage for help with logic games with a fictionalized back story about being unsatisfied with my LSAT score because I didn't finish the games section, but nonetheless pretending that my score was different than it was, also concocting a fictional academic history, and a fictional decision to choose law over economics all for the purpose of my grand scheme to make your endorsement of a law school prediction regressor look bad.

Have a nice life. Good luck with the LSAT. Take a couple of stats or econometrics classes sometime since it seems to be your passion. And I highly recommend the free meditation videos available through 7sage. Just a couple minutes a day might help put the world in perspective. It certainly helped calm me as I have tried to improve on logic games.

Goodbye,

Seeking Perfection

DUDE YOU ARE INSANE. At what point did I concoct your story? You posted these lies online yourself, I said you lied about your score and your papers and you don't have a degree and never heard a job. At this point you refuted 0 of those things. Keep talking, I want to see where your limit of embarrassment lies.

And stop liking your own commnets it's pathetic.

And I literally proved everything you delusion brain came up with wrong, how about instead of meditation videos, you take a second to look into the page on that tool that I suggested in the beginning instead of constantly writing useless novels on the subject. EXPOSED

@

"One of the most important ones is that there is zero covariance between independent variables and the error term. Due to extreme self-response bias that is definitely not true in the LSN data. There are ways to account for that, but they invariably increase the variance of your regression and in this case would have expanded the variance to the point where it would have been useless. Since they have a relatively narrow range to range to their predictions it follows that the law school predictor did not use a more robust estimator. This means that the predictions have a more narrow range, but may be wrong in some systematic fashion.

"The reason I allowed that the two were nearly equivalent is that if you used the same range of data the predictions closely matched what you would get just from the LSN data. It therefore gives a ballpark for your chances of getting in which thankfully is all we need for application purposes."

MyLSN does not provide any "predictions". If the regression and search tool are "nearly equivalent", and you seem to think the regression is wildly inaccurate, how is it that you concluded that myLSN search is useful? Do you mind reviewing the post I made indicating accuracy rates and then explaining how it indicated the toll is "wrong in a systematic fashion"?

"Finally, the reasons that I said the regression model isn't significantly better"

"However, I think it is actually such a small improvement over the data searching tool already available on LSN that aquiring would be almost totally useless."

So you are simultaneously indicating that the data is biased because it is user reported, therefore a regression would be incorrect. However the same self-reported data which is bias by your own account is providing value through the myLSN search? How's that work? If that data is "bias" and that makes it inappropriate for modeling, wouldn't that also mean it would be inappropriate for determining anything at all? Or does is it not self-response bias in myLSN and then when its used for the regression, then it becomes self-response bias?

"But, when I say that I don't think it would be hard, that's on a relative scale. It would certainly take me either a significant amount of time or favors from friends."

So, would it be hard or not? You position is kind of unclear on that point.

"If in your next seven posts, you manage to convince yourself that you were right"

Or literally expose everything you just stated as incorrect? I just addressed every statement you made, every single one, please continue to hammer home your points. (Not actually sure what they are, you seem to be backpedaling and changing your responses)

@ said:

@ said:

@ Look man, point being here is that this dude is making all kinds of statements that I am addressing point by point to be factually incorrect. I'm not about to sit here and let this dude post incorrect garbage all over my post. And I'm certainly not about to let this clown demean the tremendous amount of work that went into this model or talk trash about its accuracy when it has PROVEN rates. Talk trash about the data, without acknowledging its got MULTIPLE data sets. And lastly, talk trash about the value of a tool that has been corroborated by NUMEROUS law prep sites mentioned above and beyond those as well.

I hear you, dude. Listen, calling @ a clown or whatever isn't cool. He's a good dude and I've known him to be quite helpful on the forum. I aso know you are a knowledgable and cool dude! So just chill with the name calling is all. I hear you... I really do. It's just not a big deal in the scheme of things. It's the internet and everyone is entitled to their opinion. You both make good points but the acrimony and ad hominems aren't getting us anywhere with this discussion; so let's just end it here.

If you want to chat more about maybe collaborating in the future on making our own predictor feel free to PM. We can talk about how the Cavs are doing so far this season as well :)

@ Can you highlight a single valid thing that he said in the 900 page long comedic insults he posted? Like literally, is there a single thing that he managed to prove here that wasn't directly refuted? Just wanted to see where your high degree of confidence is coming from with this "specialist" on this matter? This dude has never had a job, he doesn't even have a degree I'm pretty sure. Someone who insists that their own personal preferences about data views is statical law, who doesn't understand the relevance of a degree in statistics to discussing statistical data modeling? Who cannot tell the difference between a historical percentage and a probability? I called him a "clown" because every single "fact" that he posted makes me laugh, and he's so sure of himself too.

"One of the most important ones is that there is zero covariance between independent variables and the error term. Due to extreme self-response bias that is definitely not true in the LSN data."

Who seems to think that "self-response bias" makes this data unfit for regression modeling, but at the same time think this VERY SAME data is completely fine using the search tool on myLSN? Doesn't "self-response bias" mean people are lying? So then, how is the data on myLSN useful but not useful when modeled? And so by implication the data on lawschoolnumbers is incorrect, top-law-schools.com are idiots for owning the exclusive license to this tool. I literally posted the accuracy rates of this tool, and somehow he failed to notice or acknowledge how that accuracy could have accrued with such a flawed model?

Does any part of that seem logical or coherent to you? IDK maybe you could explain something that he's trying to say that I'm missing?

@ Bet money he doesn't have any published works.. liar.

@ No man it is a big deal when you directly insult people, this dude made a post that insulted me 15 separate times by my count. Directly demean the work of numerous people who are just trying to offer free resources without understanding the basics of different data displays and persisting on assertions that have literally been proven wrong multiple times. I'm sure he's a good dude, he need to learn to keep his mouth shut when warranted and acknowledge when every single one his points has been directly disproved.

@ Look man, point being here is that this dude is making all kinds of statements that I am addressing point by point to be factually incorrect. I'm not about to sit here and let this dude post incorrect garbage all over my post. And I'm certainly not about to let this clown demean the tremendous amount of work that went into this model or talk trash about its accuracy when it has PROVEN rates. Talk trash about the data, without acknowledging its got MULTIPLE data sets. And lastly, talk trash about the value of a tool that has been corroborated by NUMEROUS law prep sites mentioned above and beyond those as well. Not to mention at this point this dude has insulted top 20 business programs, tier 1 consulting firms, THE FORTUNE 100 though?

@ Bro read the OP no one is trying to argue, I have this clown on my post talking all kinds of baseless stuff and trying hard to insult me? I'm literally just going point by point smacking him down but he persists lol. You see this book he just wrote thats like 96% him coming up with different ways to try and insult my education while avoiding aknowledging that I disproved his every statement? I

@ Also feel free to answer ANY, even a single one of the questions aside from your rudimentary display of intro data modeling assumptions that you learned in stats 101 or whatever. You're clearly delusional, please don't pollute my posts with any further insults, that seems to be 90% of your material, and avoiding any questions or aknowledgment of point by point destruction of your entire uneducated post, from beginning to end. Any other questions professor? Why do you not understand how me doing this for a living for 6 years, and having a degree in stats is relevant to discussing proper interpretation of statistical data models? That's like saying "just because you're degree is in math and you're a math teacher, doesn't mean you know math."

Bottom line, I posted the accuracy rate and the data sets used. How do you respond. Also, I think the fact that myLSN links to this predictor, in addition to a whole host of other law school prep sites, and the fact that it's licensed by top-law-schools.com speaks a bit to its relevance. Your clearly educated opinion notwithstanding. That enough posts for you? Want to offer an actual response now?

@ Wow man, so you just stated you have never worked with data in the real world. Okay you understand how moderately sized data in a academic paper is not actually modeling data for real application. You talk a big game man, what are your papers? list them please? I don't believe they exist.. prove me wrong. Calling it now, you haven't even graduated have you?

Seems like your post is a bunch of different ways of trying to insult my education. I'm not sure if you don't understand why I mentioned that I model large data sets for a living for 6 years and have a degree in business stats not one that "dabbles in statistics".

You don't have to write a doctoral thesis every time you reply. Now your response on the other had had one actual fact regarding rudimentary assumptions of data sets for use in modeling, those assumptions have been accounted for in the data, if you actually took a second to look at the tool before rushing to insulting my credentials?

As to your thesis here:

P1- Insult/ admission you have no real work experience modeling data, and no experience working with large data sets.

P2- Insult/ assertions about proper modeling tools based off your 0 actual expereience

P3 insult

P4- Insult- my response indicated the variety of data, not just LSN data that was used in the regression model, also the accuracy rate I posted above completely disproves your assertion.

P5- WRONG- they use additional data sets and a index formula combo for.a regression. Again all this talk of stats and you can't seem to understand that? IDK why

P6- no one cares

P7-"The reason that the tool deals with URM status any better than other predictors is because the underlying data includes more finely detailed information on it, not some imaginary sophistication."- This is your own quote, no one is misrepresenting your ignorance. You did a good job of displaying that yourself. My response was to post the methodology employed in accounting for this factor which you deemed "imaginary".

P8- See responses indicating URM methodology.

P9- Insult in form of conclusion?

Line 11/12- Irrelevant

P13-Yes, I actually made several responses. Not to "bait" you but to systematically expose your ignorant assertions point by point. You just seemed to read the credentials bit?

P14 insult

P15- insult

So, how about you just go back and read my posts, each of which disputes and disproves every single statement you have made on my post in full. Now you can continue to sling insults or you can come with some actual facts? And maybe try and get a job while your at it? Seems like you can use a bunch or big words and take up 10 pages without offering a single actual basis for any of your assertions. As to mine, check the posts above, that's called proof.

@ Do you have any more baseless statements you would like me to disprove? Also, and again, would you please explain your qualifications/source of knowledge for all of your incorrect assertions and assumptions? Do you have a degree based in stats or numbers? Do you have any real experience working with large data sets? Have you ever modeled data in real world situations? I think I may already know the answer to those questions.. again that's my assumption ;)

"LSN also has a tool for sorting through the data by LSAT score, GPA range, URM status, early decision, and which cycles of data you would like to include. It simply tells you how many people applied with your selected number range in that year, what percntage got in, what percentage of those that got in reported recieving scholarships, and what the average scholarship ammount was. I don't think that the law school predictor was doing anything all that much more impressive."

Or maybe you can explain how a regression model doesn't do anything different than that which you've described above? Is the percentage acceptance rate for variables the same thing as a probability of acceptance based on those variables?

Might be time to revisit the wikipedia page you got all your brilliant deductions from.

PS. I couldn't explain how irrelevant what you care and don't care about is to me. Please stop, I think you've had enough.

@ "The reason that the tool deals with URM status any better than other predictors is because the underlying data includes more finely detailed information on it, not some imaginary sophistication."

From the site:

URMs tend to have an improved chance of admission when compared to non-URM candidates with the same LSAT and GPA. URM races are generally considered to be African-American, Mexican-American, and Native American. There are arguments both for and against this practice, but the fact remains that it does exist. For the purposes of this prediction model, URM candidates receive a raw point boost to their index formula score; this boost is based off of a percentage of the roughly median applicant’s index score at a given school. This model gives the same percentage boost at all schools.

"I don't think I in anyway represented the search tool as not based on historical data or the law school predictor as based on something other than a fairly simplistic regression of that same data."

That ^^ doesn't sound that "imaginary" to me.

From the site:

Q: How did you create formulas for schools that don’t publish formulas?

A: A combination of regression analysis using 25%/75% LSAT and GPA data along with submitted user data and modifications to the resulting formula using submitted user data and law school applicant data available online. They’re not perfect at predicting, but then again, neither are the published schools’ formulas.

Q: Where do you find the data for all of this?

A: A number of information sources were used in compiling the data included in this model. These sources are: Internet Legal Research Group, Law School Admission Council, Law School Numbers, and US News & World Report. A number of Top-Law-Schools.com forum members have also kindly contributed information and feedback that has led to the ongoing improvement of this model. TLS forum member OperaSoprano has also provided valuable support and feedback; without OperaSoprano’s suggestion, I might have never created the part-time program prediction portion of the model. TLS forum member CyLaw has kindly offered a number of suggestions and support for Law School Predictor.

-So if they are using the same data, doesn't that contradict them using "more finally detailed information"- I think that would qualify as additional data? Also, can you explain what "finely detailed data" you were referring to?

From the site:

While the URM boost was not devised by using a particularly statistically rigorous technique (the URM feature debuted in Version 1.5, and has been tweaked a couple times since), it turns out that LSP is just about as good (or arguably better) at making predictions for URM applicants on LSN when compared to the average applicant on LSN. For those who sometimes suggest that decisions for URM applicants are largely unpredictable based on an applicant’s numbers, these results would shed some doubt on that assertion. This is not to say that URM applicant cycles are entirely predictable, but if a URM applicant plugs his/her numbers into LSP, s/he should get a decent of his/her chances.

The prediction categories used in testing accuracy are the same as LSP’s prediction engine with the exception of early prediction (which was not tested); predictions are adjusted to account for splitter-ness, weak GPAs, and URM status.

@ "I cannot justify those expenditures for creating a regression with data which doesn't conform to basic expectations of covariance between values and the error term and will inherently be either biased or extremely wide in its prediction ranges."

Can you explain what you meant to convey by that statement? I have a degree in stats and have no idea what "basic expectations of covariance between values and the error term" is referring to.

How would the data be"inherently biased"? can you explain what you mean by that?

As for the tools accuracy, below is taken directly from the site. Do you have any accuracy measures for your predictions to share with us?

"P.S.

I also care even less about the rank of the place where you went to undergrad(and got a business degree) than the miniscule to non-existent degree which a properly conducted regression would reveal that the law schools care about undergraduate institutions."- Would you elaborate on how the below accuracy measures are considered "minuscule to non-existent" or "extremely wide in its prediction ranges"?

Target admit rates, based on how LSP renders predictions:

When LSP said Admit: >= 87%

When LSP said Strong Consider: ~ 69%

When LSP said Consider: ~ 50%

When LSP said Weak Consider: ~ 31%

When LSP said Deny: (= 13%

Based on the 33,500+ LSN decisions from the 2008-09 admission cycle:

When LSP said Admit, 8402 instances: 87.0%, +0.0%

When LSP said Strong Consider, 4842 instances: 78.0%, +9.0%

When LSP said Consider, 10066 instances: 50.6%, +0.6%

When LSP said Weak Consider, 3646 instances: 22.8%, -8.2%

When LSP said Deny, 6754 instances: 10.0%, -3.0%

Difference between actual admit rate and predicted admit rate: (prediction categories weighted evenly)

Average rate: -0.3%

Median rate: -3.0%

Summary:

Prediction categories that were right on the money: Admit, Consider

Prediction categories that were reasonably close: Deny

Prediction categories that were somewhat off: Strong Consider and Weak Consider

A closer look…

A possible explanation for the Strong/Weak Consider results is that the chance of being admitted (based on admission index scores and LSP adjustments) should look like a parabolic curve (since it should be a normal distribution), and, based on these accuracy results, a curve with a high and narrow peak in the center (leptokurtic distribution).

Underrepresented Minorities (URMs)

Target admit rates for URMs, based on how LSP renders predictions: (same method as non-URM applicants after URM boost is applied)

When LSP said Admit: )= 87%

When LSP said Strong Consider: ~ 69%

When LSP said Consider: ~ 50%

When LSP said Weak Consider: ~ 31%

When LSP said Deny: (= 13%

Based on the 4,300+ LSN 2008-09 decisions for self-identified URMs and with LSP URM feature enabled:

When LSP said Admit, 963 instances: 87.1%, +0.1%

When LSP said Strong Consider, 418 instances: 70.8%, +1.8%

When LSP said Consider, 1034 instances: 55.3%, +5.3%

When LSP said Weak Consider, 436 instances: 33.2%, +2.3%

When LSP said Deny, 1468 instances: 14.9%, +1.9%

Difference between actual URM admit rate and predicted URM admit rate: (prediction categories weighted evenly)

Average rate: +2.3%

Median rate: +1.9%

Summary:

Prediction categories that were right on the money: Admit

Prediction categories that were reasonably close: Strong Consider, Weak Consider, Deny

Prediction categories that were somewhat off: Consider(/p)

@"I don't know why you are talking about having worked with excel in the course of obtaining a degree in business. It's rather superfluous to the problem."

This was meant to provide my qualifications for discussing statistical data interpretations. What are your qualifications?

@ "I don't think the advantage provided by the regression is all that great in part because most of the assumptions which you make as part of such a regression are not close to being satisfied here."

Can you be more specific about those assumptions that you are referring to? Can you explain what assumptions those would be?

@ "However, I think it is actually such a small improvement over the data searching tool already available on LSN that aquiring would be almost totally useless."

"I don't think that the law school predictor was doing anything all that much more impressive."

Above is an indication that you don't understand the difference between myLSN data view and a regression data model.

Below is a contradiction of your own statements. So, if you understand the differences between a regression and historical data view, how is it that you don't "think they are doing anything more impressive"?

"I don't think I in anyway represented the search tool as not based on historical data or the law school predictor as based on something other than a fairly simplistic regression of that same data."

@ Word of advice, trying to argue with someone who's degree was in stats while simultaneusly acknowledging you don't understand stats is a poorly chosen method of argument. And I'm sure in your imaginary world it would be easy to create all sorts of things, but why don't you try entering reality and actually trying to back of some of the clearly outrageous claims you made. Does is matter what tool you use to run regression? No it does not. And given that a top 20 business programs taught that method using .. you guessed it: excel, I would warrant a bit of forethought before attacking people for doing something you don't even understand how to do or use.

"However, I think it is actually such a small improvement over the data searching tool already available on LSN that aquiring would be almost totally useless." Um, small improvement or something totally different? I think the latter is correct. Do you understand that mylsn and this a regression are two different ways of viewing data, each with their own respective qualities? That's something I learned in my studies, as "superfluous" as that may seem to you.

And one more quick thing, I think that the use of excel for 95% of business applications speaks a fair bit to it's appropriateness in modeling data. Have you ever actually had a job where you worked with data? I've done so for the last 6 years. So I understand how you may feel top business programs, fortune 100 companies, and the largest professional services firm in the world might all be wrong about using excel, but based additional off my own extensive expereince I think the use of the tool would be warranted? I'm sure you disagree based off your own tremendous knowledge of the subject. "Amateurish" I think you called it?

@ You need to fall back with that attitude. Whatever you're own personal opinions are on using a regression versus using historical data is irrelevant to me and every one else on this forum. Having a business degree in statistics means that I understand them, which clearly was missed on you. If you can create one so easily than do it.. and idk what "assumptions" your talking about here, that's the point of a regression is to fit a line to a bunch of data points to provide predictions based on variables. Anything else you'd like to get off your chest here? with that long drawn out completely valueless response you've provided other than demonstrating your proficiency in using a bunch of terms you don't understand? It called thinking before you speak (or attempt poorly constructed attacks).

@ Definitely. My intention in making this post was just to indicate that it is a useful tool that would serve a lot of people, and 7sage and it's users would benefit from updating it and incorporating it into the site. And that goes especially for URM's out there (like myself), given the ability of the tool to logically and coherently account for URM status in the admissions probability. @

I think that now that we may have cleared up any confusion about the difference between this tool and the myLSN search tool, more folks may be able to understand and appreciate the ability of this tool to provide an alternate means of deriving the statistical probability of admission into law schools!!

Also, I would suggest taking at least a brief look at the "about" page on that predictor before making assumptions about the difficulty in creating such a model, it's actually rather involved.

@ @ The search tool on my LSN is a display of historical data. This predictor is a logistical regression, I think that instead of arguing with you foks I would just refer you to google to understand the difference between those two things.

I also graduated from a top 20 undergrad business program majoring in business stats, and was still unable to create a regression model using the data given it's size and the constraints of cpu processing power processed by y laptop (it just freezes and crashes, its not that I don't know how to do it, my computer won't cooperate its not powerful enough I guess?) I am aware of excel sort functions, I also have worked at a big 4 accounting firm for 4 years now, this was after managing IT operations at a fortune 100 company for 2 years- this is just to give you some indication that I also have worked with excel and am familiar with it. If you don't "think I am correct" please feel free to create such a model and provide one if you are able to easily create I, I would appreciate that.

One of the things about this predictor is that it accounts for URM status in a sophisticated manner, something that makes this tool stand out among any other resource available on the internet.

@ Yeah initially those were my thoughts until I actually sat down to create it. Needless to say working with spreadsheets that have an excess of 1 million cells of data (Mylsn data) isn't something that is easily handled on most peoples at home computers. Were you able to create it easily? Mind sending me your model?

Also, I think that the lack of any other similar logistical regression model available on the entire internet speaks to the nature of the difficulty involved in creating one.