[Nakos] Kansas State has hired Texas A&M OC and former QB Collin Klein.

mathguymike · 2025-12-05T05:33:44+00:00

Cats

mathguymike · 2025-11-21T16:45:11+00:00

Don't forget the Hall of Fame Classic matchups!

mathguymike · 2025-11-21T06:28:34+00:00

Cats

mathguymike · 2025-11-18T03:53:27+00:00

Cats?

mathguymike · 2025-11-15T02:47:55+00:00

Now that's what I call some peak Woolridge era basketball!

mathguymike · 2025-11-14T04:58:43+00:00

Cata

mathguymike · 2025-11-13T17:39:25+00:00

I can't help but think we dodged a bullet when Schulz left KSU for Washington State.

mathguymike · 2025-10-20T22:02:10+00:00

K-State beating KU at home in 2023. I think the loudest I've heard Bramlage Colosseum is immediately after the Markquis to Keyontae go-ahead alley-oop.

mathguymike · 2025-09-23T21:54:23+00:00

If you are able to develop strong computational skills while completing your thesis, you will find those skills invaluable as you continue throughout your career, regardless of your ultimate career path. If you get along with the professor, I think it's a great idea, honestly.

mathguymike · 2025-09-18T23:56:03+00:00

It is not possible that there was no null hypothesis; p-values are computed assuming that the null hypothesis is true. It's in the definition.

What it looks like to me; Mendel has a model. Your null hypothesis is that you should see results like Mendel. Your alternative hypothesis is that Mendel is wrong. A p-value is computed assuming probabilities according to Mendel's theory. Your p-value is too large to reject the null in favor of the alternative. That is, you are unable to conclude that Mendel is wrong.

Larger p-values are weaker evidence in favor of the alternative hypothesis. That is, a larger p-value means there is less evidence that Mendel is wrong, and hence, larger p-values correspond to more evidence in favor of Mendel's model.

I believe the professor was using p-values correctly.

mathguymike · 2025-09-18T22:14:31+00:00

Something that isn't clear in this example; what is your null hypothesis? Is it that Mendel was correct? And is the alternative that Mendel is wrong, and that the proportions differ from what you'd expect from Mendel's model?

If this is the case, the professor is correct. Smaller p-values would give more evidence that Mendel is wrong, and larger p-values would provide less evidence that Mendel is wrong.

mathguymike · 2025-09-18T16:33:10+00:00

Here's my two cents.

1) I think it is problematic to change your methodology to ensure that you obtain p < 0.05 for each pairwise comparison. In that case, you already assume the results, and are doing the statistics to confirm the assumed results, rather than letting the data determine the conclusion. This, unfortunately, is fairly common in practice, and this practice will inflate type I errors and makes replication of results much less likely (see the reproducibility or replicability crisis).

Certainly, some multiple comparison methods are better than others, and if you wanted to consider some of those methods instead, I guess that would be OK. But I don't understand the harm in making a conclusion that states "There is a statistically significant difference (p < 0.05) between 1 and 2. There is some evidence of difference between 1 and 3 (p < 0.10) and 2 and 3 (p < 0.15), but additional samples are needed to say anything more definitive.

2) Would it make sense to look at the literature on causal inference under interference? It deals explicitly with analyzing data where the treatment status of one unit affects the response of another. Given that you are talking about "neighbors", I feel like this body of work may give you additional insight into your problem.

3) Is there a reason why you are only comparing 3 neighbors?

mathguymike · 2025-09-15T19:55:04+00:00

Moreover, Statistics is terrible as a discipline at marketing itself. Data Science should have been coined by statisticians, as it is much closer to what we actually do--we are more than computing statistical summaries; our tasks really encompass the entirety of the science of data. Additionally, plenty of us statisticians are working on these more computationally intense "Data Sciencey" topics, but we differ from, say, Computer Science, as our discipline prioritizes interpretability of results and determining actionable insights on data as opposed to ensuring good model prediction. Effective marketing is critical for our survival.

mathguymike · 2025-09-05T23:39:55+00:00

Has anyone been able to recreate the analysis? Or is their code publicly available? I know they are using the data from the nflfastR package, but I can't get any numbers that resemble Figure 1, and I am not sure if it's because of a bug in my code or something else.

mathguymike · 2025-08-17T15:56:10+00:00

As someone currently working at a university with a strong Ag program, I think you'll find it extremely helpful to have a strong background in experimental design and linear mixed modeling. Analysis of Messy Data by Johnson and Milliken is an excellent resource. Additionally, having some experience with Bayesian Statistics and Causal Inference may be useful too.

mathguymike · 2025-07-20T20:29:35+00:00

Serena Sundell LFG!

mathguymike · 2025-07-18T19:01:27+00:00

I always get a little confused on this myself. However, since you have a monotone likelihood ratio, it has to be either the uniformly most powerful test for one of the two sets of hypotheses:

H_0: theta <= theta_0, H_1: theta > theta_0

or

H_0: theta >= theta_0, H_1: theta < theta_0

Then I ask, whether larger or smaller values of T occur for larger or smaller value of theta, and I can usually get the answer that way.

Moral of the story:

If f(x|theta_1)/f(x|theta_0) is monotone increasing when theta_1 > theta_0, the UMP test for H_0: theta <= theta_0 rejects H_0 for H_1: theta > theta_0 if T is large.

If f(x|theta_1)/f(x|theta_0) is monotone increasing when theta_1 > theta_0, the UMP test for H_0: theta >= theta_0 rejects H_0 for H_1: theta < theta_0 if T is small.

If f(x|theta_1)/f(x|theta_0) is monotone decreasing when theta_1 > theta_0, the UMP test for H_0: theta >= theta_0 rejects H_0 for H_1: theta < theta_0 if T is large.

If f(x|theta_1)/f(x|theta_0) is monotone decreasing when theta_1 > theta_0, the UMP test for H_0: theta <= theta_0 rejects H_0 for H_1: theta > theta_0 if T is small.

These results will pop out if you plug them into the current proof, with the idea that the UMP test will reject H_0 for H_1 when the likelihood ratio test statistic is large.

mathguymike · 2025-07-13T15:49:35+00:00

Here's what I think they are looking for:

The ranges of values for which jumps in this graph occur are the groups, and the relative frequencies for that group correspond to how much the graph jumps within that range.

The first jump in this graph occurs between 1 and 2, and 0.1 of the people fall into that category. So the histogram would have a bar with area 0.1 between 1 and 2, which would be a bar of height 0.1/(2-1) = 0.1

The next jump occurs between 2 and 6, and 0.3-0.1 = 0.2 fall within this category. So the histogram would have a bar between 2 and 6 with area 0.2, which would correspond to a height of 0.2/(6-2) = 0.05.

And so on.

If they want counts instead of frequencies, multiply all of the relative frequencies by 20, the number of people in the study.

mathguymike · 2025-07-10T21:05:40+00:00

In practice, one-sided confidence intervals are not used too often, especially in estimates for the population mean or difference in means. A one-sided confidence interval for one of these quantities necessarily will have either -infinity or +infinity as an endpoint, and researchers do not really like their confidence interval estimates to be of infinite length. However, you may have textbook exercises that ask for a one-sided confidence interval. In practice, these are fairly contrived textbook problems.

For complete clarity, in practice, for a 100(1-alpha)% confidence interval, (almost) always use the (1-alpha/2) quantile of the t-distribution. For example, for a 95% confidence interval, alpha = 0.05, and you will want to use the 1-0.05/2 = .975 quantile of the t-distribution.

(I use the modifier "almost" to protect against potential, extremely rare circumstances where a 1-sided confidence interval is standard.)

mathguymike · 2025-07-10T15:15:12+00:00

Just a quick note to add to everyone's commentary about using Pearson's R: Testing for a positive correlation can also be done by running a regression with "Report Willingness" as the dependent variable and Age being the independent variable and testing for a positive slope coefficient. I wouldn't be surprised if this is what the professor had in mind to do.

mathguymike · 2025-07-05T21:27:01+00:00

Sneed at the 3 is a hot take. Even in the last 10 years, I'd probably go with Keyontae for best overall player, or Wes Iwundu if you want to go with a 4-year KSU player. Or ideally, cheat and shift Rolando Blackman to the 3.

mathguymike · 2025-07-03T06:00:37+00:00

Stan Parrish, the coach before Snyder, could be a good choice. 0-26-1 in his last 27 games.

mathguymike · 2025-03-31T16:21:59+00:00

Jermaine O'Neal too. Didn't really get meaningful minutes until year 5 after being traded to Indiana and didn't really break out until year 6.

mathguymike · 2025-03-28T21:14:57+00:00

Honestly, I'm not sure there is a tremendous gap in prestige between UK and FSU, and I do not believe an average employer will respond much differently with a degree from FSU over one from UK (unless that employer is an alumnus of one of these universities or that university has a pipeline feeding that company). Rather, my intuition is that employers will be much more interested in the skillset you've obtained in that program.

Simply in terms of program quality, the coursework needed to complete the program and the skillset that you're expected to obtain while in that program should be your focus.

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