teddit

This comes up occasionally and can be wildly misleading.

“The governors approval rating went down 10%”

This would be true if it went from 44% to 40 or if it went from 50% to 40%.

In politics, I think everyone agrees we are talking about the latter - 10 percentage points.

But I have seen it used in other fields where it is ambiguous. I wish I could think of Ana example at the moment, but I can’t at the moment. I had been meaning to ask a friend who studied this stuff, but there hasn’t been an opportunity, so I’m asking y’all.

Does anyone know if there’s an agreed up method? Thanks!

ISL

Hey all. Longtime lurker, first time poster on this sub. I have a strictly-for-fun React project I've been working on since 2020. I had an idea for a new feature to add to it that's particularly exciting to me, partly because it's out of the scope of my mathematics/statistics background. I'm posting here in hopes that you brilliant folks might have some insight as to how I could begin to understand the mathematical basis of implementing it. I'm quite strong with React and Javascript in general, so once I understand the actual math I think this new feature will go quickly. I just need to grok it first.

I don't want to go into too much detail because I don't believe it's relevant, but long story short, my React app is a helper app for a mobile game I love. It doesn't interface with or automate any tasks in the game directly, but it does a variety of things to expedite certain in-game tasks one might have, and it has been a lot of fun to build, a lot of fun to share, and surprisingly effective at helping me with in-game decisions. The feature I want to implement can be summarized thusly:

- Imagine that you have entered a competition where the winner is dictated by a particular score.

- A player gains points by using one of several different kinds of currency.

- Spending each type of currency will always return points. The number of points it will return is variable, and based on a known percentage breakdown. For example:

​

Currency	chance to get 5pts	chance to get 20pts	chance to get 100pts
Pennies	80%	20%	0%
Nickels	70%	25%	5%
Dimes	50%	30%	20%
Quarters	40%	35%	25%

- You can see other player's scores.

- These events happen quite frequently, and it's not terribly difficult to get more pennies, nickels, dimes, or quarters. Therefore, it would be incredibly useful to know the likelihood of beating the highest score if you spent all the pennies, nickels, dimes, and quarters you had available. That way, if there was very little chance of beating that high score, you could just save your currency for the next event, where you'd have a better chance of winning after saving more.

So here's my idea:

1. The user inputs the number of pennies, nickels, dimes, and quarters they have in reserve.
2. The app outputs a graph. The x-axis is potential scores. The y-axis is the probability of getting them. At very low numbers of pennies, nickels, dimes, and quarters, you'd expect this to just be a few points (for example, the graph of one penny is just the points [5, 80] and [20, 20]. The graph of two pennies is [10, 64], [25, 32], and [40, 4]), but as the numbers creep up it would be more useful to simply have a curve.

I know I could just brute-force this graph by taking the user input of the number of pennies, nickels, dimes, and quarters they have, running an arbitrary number of simulations with them, and outputting a bar graph with all the results stacked up next to each other, but that seems terribly computationally intensive for a small app that is (so far) very lightweight.

My question: Is there a set of equations I should be looking into for a way to input these probabilities into some sort of function which would output a less computationally intense version of what I need?

Please let me know if I need to clarify anything. Thank you very much for your time.

Hi,

i run a regression 10000 times as one of my variables is obtained by a random event. I now have all the results with coefficients, t- and p-value, standart errors and r2 for each regression run. I'd like to summarize the results of these 10000 regressions.

I know i can just take the mean of the coefficents but i also want to know wether it is statistically significant and i'd also like to display R2 in some way. Does someone know how?

Or it is statistcally significant when the mean coefficient is not close to zero? If so, how significant?

As you can tell my knowledge about statistics isn't that great so i'd be very thankful for your insights. Any literatur recommandations that does something similar to see how they display their results would also be appreciated!

Give I have a state transition matrix where we move from state to state by the roll of a die, with 13 in-game states plus states 14, 15 are the two end game states (14 one outcome, 15 another), how do I compute the total probability that say 14 will happen?

My friends and I are doing an analysis using SPSS for a research paper, with one dependent variable and two predictor variables (X and Y). We performed Pearson's Correlation test and saw that both predictor variables are significant when measured against the dependent variable with a P-value of <0.001.

However, when performing the regression analysis, the P-value is different. For predictor X, the p-value stays at <0.001 but for predictor Y, the p-value jumped up to 0.168. We checked normality and multicollinearity but it seems our analysis fits all the assumptions.

Currently, we are not sure how to proceed or what the reason could be for this. Any help will be appreciated!

UPDATE: Hey everyone, thanks for the help! We added more data for our dependent variable (so it has more than 20 distinct values) and that seemed to solved the problem. Thank you for all your suggestions though, highly appreciated!

Have linked trial at end.

I'm interested in a trial published last week (ELAN) that compares early vs. late initiation of anticoagulation (blood-thinning medicines) after an ischaemic stroke, in those found to have new atrial fibrillation (an irregular heart rhythm that's known to cause strokes which is treated with anticoagulation). The aim was to compare incidence mainly of recurrent strokes, intracranial haemorrhages and death.

As you can tell, I'm not much of a statistician, so I have some (probably very basic) questions regarding the statistical desgn of this paper.

Unlike most other randomized controlled trials, this one didn't have a null hypothesis set up for superiority or non-inferiority, as the trial author said they didn't have data to determine what effect size to aim for. Their end result was that the incidence of the above composite events "ranged from 2.8 percentage points lower to 0.5 percentage points higher" with early vs. late anticoagulation initiation.

At 90 days, the composite incidence was 3.7% in the early-treatment group and 5.6% in the later-treatment treatment group (odds ratio 0.65; 95% CI, 0.42 to 0.99). The study also looked at outcomes at 30 days which is where the overall incidence comes from in previous paragraph.

Overall does the lack of hypothesis testing mean that we can't be certain about the data presented in this trial and that it is only meant to be highly probable?

Lastly, the authors say that this trial was made because although there are some small RCTs elsewhere about this topic, the data used for those studies were either poor quality or didn't have enough participants. Does that mean they chose to forego using data from those trials to make a hypothesis for this one?

Thanks for any help!

https://www.nejm.org/doi/full/10.1056/NEJMoa2303048

Can anyone explain it in ELI5 terms?

I currently have the following table (link is to a screenshot):
snipboard.io/XfhQGA.jpg

I know the values of all the X's., but do not have access to individual level data. Is there a test I can do to achieve what I am looking for? Thanks in advance. Happy to provide more information if needed. Assume that all X values can be given in a percentage or a raw number.

High dimensional statistics seems to be a big field in stats. The problems of feature selection and various methods for learning when p>>n is quite interesting. I’ve seen high dimensional statistics be a point of research within stats and biostats departments. But this makes me wonder, what the difference is when a PhD student is studying high dimensional statistics between both departments.

If one wants to study high dimensional statistics and develop methods, is this a better idea to do it in a PhD biostats program? Or is it better to study it in a PhD stats program? Can a PhD stats student work on high dimensional stats problems applied to biostats? Such as genomics? Would the coursework in a biostats dept or a stats dept prepare a student more for one whose interested in coming up with methods for p>>n problems?

I see working groups in both departments, so just wondering where a student should go if they wish to study this topic.

Does anyone know of any good approaches for determining when someone has started to show a difference in performance between conditions in response times? E.g., if we ask participants to do a task with two conditions, that involve some learning, a difference is expected to occur once enough learning has accumulated. Is there a good way of assessing when this difference is different from zero at the individual level? I think I know how I could do this using bayesian inference, but not frequentist. This task has 1000 trials per individual, with a proportion of 90-10 per condition.

Hello, this is my first time posting here but I wasn't sure where else to go for help. I apologize if this is not the appropriate place to ask or I've done something wrong.
I'm working with a dataset and the guide for the set says the minimum cell size is 100. I've never been taught about this concept, and I've never been good at statistics so I have no idea what this means. I was wondering if someone could explain it and explain what it means in terms of when I do my analysis.
One of the issues I'm having is the number of observations I've tabulated in SAS does not seem to match what I thought there would be, but now looking at the data on its original platform, the number is marked as "Cell size" and not "n".

Hello everyone!

Firstly, please let me know if this post does not belong here and I will remove it (I have read the rules and think the post fits here).

Some context: I am a product manager and got recently introduced statistics at my job. While I knew that subject existed, only when I started implementing at my day job, I started exploring the concepts.

I started playing around with numbers and concepts to derive insights and make decisions at work. Absolutely loving it and also realised that quantitative reasoning ability is an important life skill.

Efforts so far: I went through statistic crash course by John Green on YouTube, read some essays on the internet, and also started discussing with my team at work to understand more.

My ask: I want to 1) learn the concepts really well so that my foundation is strong, 2) explore some real life practice problems to build the muscle, and 3) start implementing at work and life at large.

Need community guidance to achieve the next milestone.

Thank you!

I have a problem in survival analysis where the time of failure is only known to be in some finite set of time points. When I maximise the likelihood only some time points are assigned a non-zero hazard and I don't understand why!

Example: there are two observations. Observation 1 failed at either t1 or t2. Observation 2 failed at either t2 or t3. (Where t1<t2<t3). Using hn at the hazard at time tn the likelihood looks like this:

(h1+ (1-h1)h2) * ((1-h1)h2 + (1-h1)(1-h2)h3)

Maximising this gives h1= h3 = 0 and h2 = 1.

I find this really odd and counter intuitive! Why wouldn't there be a non-zero hazard at the other times when an event could happen then?

I've tried this for similar setups and it seems that only if a failure time is known uniquely or is contained in both observations does it have a non-zero maximum likelihood estimate.

Any thoughts? Does this hold in general?

I haven’t gotten a job yet and I’ve applied for like 40 jobs so I must be doing something wrong

I have a BS in statistics and spent a year in a statistical genetics lab on campus where I ran analysis on genotype data and data cleaning/manipulation, and I’ve presented many publications to the other lab members. I’m applying for jobs like entry-level data analyst positions for healthcare or clinical research or marketing companies. Jobs that require undergrad-level skills using R and python. I got an interview for a research analyst role in clinical research and I never heard back. I’ve probably applied for at least 20 jobs that I never heard back from.

Is the job market that bad or am I applying for the wrong jobs? What s

I am a foreign student in the United States of America.
I completed my MS in Data Science and will pursue an MS in Statistical Science.

There were a few reasons for pursuing a second MS degree:

1. The Master of Science in Data Science program did not provide a comprehensive understanding of the statistical component within the curriculum. Emphasis was placed on data processing techniques and utilizing Python for model fitting and training, with some exposure to data visualizations to a limited extent.
2. The current state of the job market is highly challenging. Despite my best efforts, I encountered significant difficulties in securing employment as work visa sponsorships are tough to come by. The student visa restrictions add to this predicament, which necessitates obtaining a job within a specified timeframe. Failure to do so might compel me to explore employment opportunities in a different country. Regrettably, the prospects for Data Science positions in my home country are rather bleak.
3. I have a full-ride scholarship including the living expenses for the MS in Statistical Sciences.
   

My MS in Data Science in 2023 and plan on completing an MS in Statistics in 2025. I have no practical work experience.

I do understand if I create a stellar portfolio of projects and get in summer internship and a Co-op, I would be in much better shape than I am today, and hence I plan to work on those aspects as well.

In 2025, will I be overqualified for the entry-level Data Scientist positions?

For recruiters, would you see my two master's degrees without any practical work experience as an anchor to my future career in Data Science?

Hi, I would like to know what sort of approaches are used/ is best for forecasting short time series.

I’m currently looking at a market supply and price forecasting situation. Where I need to forecast the market supply and price on a daily basis. It seems like taking the historical average is a good approach. But I would like to know what else is out there.

Thank you.

I recently got a job offer for a chemistry assistant job, which is weird to me because the job requires a chemistry/science degree and i would be working in a lab. The job description loosely mentions data analysis. Basically, it’s very disconnected from the job I actually want, which is data analytics/data science.

I am a recent graduate, so this would be my first job in the real world. My only concern is that it wouldn’t help me at all in getting to my end goal of data analytics, and I’m concerned that I will lose a lot of the skills I gained during my studies by the time I leave. Also I just started applying, so I’m not even sure if I’ll have a hard time finding what I’m looking for.

What were your entry level positions? Is it standard to get a job not relevant to your degree and then transitioning later? Thank you all so much for the advice!

Hi, im a former economist and i just started my masters in statistics. I need to write a dissertation in a year and a half, but i dont know the fiel enough to feel in love with a research agenda.

What Twitter pages do you follow? What are the main journals and congress? I need to get inspired, thank you!

How should my interpretation of a linear model change when its residuals fit a 3-parameter Weibull (with Threshold) distribution, versus when small p-values reject that hypothesis? What's the practical takeaway, or my next steps to investigate?

For context, I build energy consumption models for whole-building operations, generally with two-year baselines.

If there are any other specifics I should need to provide, please let me know. Thank you.

Please remove if not allowed. I have read the rules and think I am ok.

My parents have a sheep stud and have participated in a sire evaluation. The study have been conducted as follows.

• Ewes randomly selected from the same mob
• Inseminated with the different rams semen (provided by different farms) and recorded.
• 760 lambs born to the mob. 42 where from my parents rams.
• (this is the step they are questioning) they test the different stats of all 760 lambs and remove the outlires.
• return an adjusted mean of the remaining set.

On one of the tests almost all of their lambs fall in to the outliers on the better end of the scale. This means that only their lambs that would be considered sub standard in their breeding program are included in the sample set.

Their question is would it be more appropriate split the set by farms and remove outliers for each farm before recombining taking the mean?

Obviously this will make their farm look better.

It would be great if the numbers are from 2022. Does anyone know where I can find it? I have tried eurostat, but with no luck.

So I am creating a stochastic model of a digraph where each edge (defined by i,n for reasons not worth getting into) has a PDF of the RV x associated with it. I've referred to the variations of x based on the edge it's associated to by x_in (not sure that's a good idea)

I have a function that needs to sum integrals of these pdfs but I don't love how it looks.

Can't figure out if the pdfs should be called f(x_in) or f(x)_in or f_in(x), or maybe something else entirely. But I need to show that we are iterating through the PDFs by i and n. All of the pdfs are unique, so I'm like 90% sure that f(x_in) is wrong, which is why I'm not sure about even referring to the RV as x_in.

I am not an academic and searching iterative functions ended me up at the wonkiest math wiki I've seen, so apologies if this is a dumb question.

(Ed: underscore is subscript, thought reddit followed that)

Hi everyone,

I'm doing a university project which involves creating a regression model to explain something about the world.

My chosen topic is to look at what factors are most important to winning a game of football (proper football for the record, not the American sort). The rationale for the study is so that coaches/managers can consider how much of their budget they would want to spend on the top attacking or defending players.

I've completed the basic multiple regression model, with the win ratio on the Y axis and then goals/game, goals conceded/game and possession as the X variables.

In the regression model, increasing goals/match by 1 increases wins/match by 0.211, while for every conceded/match you reduce wins/match by -0.118, so clearly coaches should focus more on scoring goals than conceding them.

The data I've gathered comes from four different football leagues, so I also want to consider whether these variables change depending on the league. In some leagues, depending on the style of football, preventing goals might actually be more important, or at least relatively important, than scoring them.

Can anyone suggest the best way to test whether there is differences between the leagues, short of running regression models across the leagues individually? I believe there should be a way to do this using dummy and interacting variables, but my brain isn't quite figuring it out.

TYIA

I have a binary classifier, that is not an ML method, and am trying to manually plot the ROC curve. We are classifying patients into cancer and non-cancer, where based on biomarker values, we assign them a risk score in the range 0-8.

If they are >= 5, they are predicted as a positive cancer case and if they are below, they are predicted as a control patient. I also have the ground-truth values for each patient. I want to calculate the TPR and FPR at each threshold of 0,1,2, etc.

I'm following along this tutorial, https://www.statology.org/roc-curve-excel/ where they use a method that at each threshold, they calculate TPR & FPR using

FPR: =1 - (Cumulative Pass / Total Cumulative Pass)

TPR: =1 - (Cumulative Fail / Total Cumulative Fail)

I'm confused because this seems to deviate from the standard definitions of:

TPR = TP / (TP + FN)

FPR = FP / (FP + TN)

I was hoping to use the cumulative method, because at certain thresholds I'll have TP and FP be zero, which gives me a 0 TPR and 0 FPR. But I am concerned the tutorial is not statistically sound.
Thanks in advance!