I am performing an ordinal regression analysis reduced to 10 predictors from an original set of 50. Sample size: N = 380. One of these predictors is the primary predictor of interest (Safety Hazards [SH], a continuous variable). The rest are potential confounders of the primary predictor or contributors to the model.

My primary outcome is Job Satisfaction (JS).

I distilled the 10 candidates from the oriwith 10 predictors, reducedginal 50 by running ordinal bivariate regressions on each candidate.

a) A crude model [JS <-- (beta)*SH]

b) 49 adjusted models: [JS <-- (beta)*SH + any of the 49 predictors].

1. If any of the bivariate regressions produced a CIE of 10% or more for the primary predictor, SH, it made the list of 10, regardless of its p- value.

2. If any of the 50 candidates had a p- value of < = 0.1, regardless of its CIE value, it made it to the list of 10 as well.

I sort the list of 10 by CIE value and use that order to determine which variable to test next in the model.

I then begin the final fitting process to extract the most plausible confounders of SH and true model contributors from the 10 predictors. I use 2 different regression methods: Forward Stepwise Regression (FSR) and Backward Stepwise Regression (BSR).

The conditions to stay in the model are: If the p-value > 0.1 AND CIE <10%, then reject from the model; otherwise, retain.

In FSR, I start with a sparse (crude) model and sequentially add a predictor, then test whether it meets the retention criteria. If so, I retain it, add the next predictor to the new model, and apply the same retention/rejection criteria until all predictors have been tested.

In BSR, I start with a full model and sequentially remove predictors, one at a time, and perform similar rejection/retention tests as in FSR until all predictors have been tested.

However, I find that the FSR and BSR algorithms retain and reject different variables. To be clear, about 60% of variables are common to both algorithms.

My question is: why do FSR and BSR algorithms produce different results?

What is different about starting with a full model (BSR) versus a sparse model (FSR)?

What is different about starting with a sparse model (FSR) versus a full model (BSR)?

Please try to give an explanation that does not involve the term 'variance', as I tried to explain to my PI, only to be told that is the wrong way to explain it.

Thanks

David

I have been working on causal inference and experimentation frameworks within the e-commerce tech industry lately, specifically dealing with platform behavioral and transaction telemetry data to determine how customers behaves on those e-commerce platforms.

Lately i have been facing problems where standard textbook statistical theory fails when dealing with messy, real-world data distributions as human behaviour is unpredictable and many unexpected things, which i didnt even thought could happen, can happen in the real world. Such as product delivery delay, causing the customers to be angry and hence put them off from buying the product, each of these variables have to be accounted for.

To model and have these unexpected psychological state among customers accounted for, we use variables such as sudden spikes in customer support chat volume, rapid page refreshes, or immediate session drops.

Also, Marketing headers and automated CRM systems love to push blanket promotional campaigns, but as statisticians, we know that measuring simple unadjusted A/B lift is a massive selection-bias trap.

I been trying to come up with a Statistical model that is trying to model complex human behavior—specifically isolating true customer revenue incrementality from dead-weight margin cannibalization. Essentially, trying to answer: Did the coupon cause the revenue spark, or did we just hand a discount to a power-buyer who was already determined to buy anyway even without the vouchers?

Are people in the tech industry genuinely valuing such skills nowadays? Using statistics to model and predict human behavior and intent, answering questions like does our vouchers and ads really work?

I would love to hear your experiences how do u guys deals with the unpredictable messiness of human intent.

I did a personal side project on why some years bring much worse smoke haze to Singapore. I'd like people who know stats to tell me where the design is weak before I show it to anyone in the field.

The chain I test, one observation per year:

1. Pacific El Niño index (June to August) vs Kalimantan rain (August to October), 1979 to 2025, n = 47. Pearson r = −0.79.
2. Sumatra rain regressed on the El Niño index and the Indian Ocean Dipole index, n = 47. I test the Dipole coefficient.
3. log10(fire detections) = a + b·rain + c·post2016 + e, one model per island, 2001 to 2025, n = 25. post2016 is a dummy for a fire-prevention policy shift after 2015. R² is 0.82 and 0.89.
4. Fire detections vs Singapore's seasonal mean PM2.5, 2016 to 2025, n = 10. Spearman rho = 0.88, bootstrap 95% CI 0.47 to 1.00.

I applied a Holm correction across these five tests, used 10,000 paired bootstrap resamples, ran leave-one-year-out, and swapped data sources as robustness checks. All five tests stay significant.

What I'm unsure about:

1. I wrote the analysis plan after I had already explored the same data. I say this openly, but is there anything more I should do besides waiting for future seasons as a true holdout?
2. The Sumatra fire model has a Durbin-Watson of 1.44. Should I switch to Newey-West errors or an AR(1) model, or is the bootstrap enough here?
3. Is OLS on log10 counts acceptable, or should this be a negative binomial GLM?
4. A dummy for "after 2015" vs a linear trend: both give the same sign. Is there a better way to handle a policy break with only 25 years?
5. With n = 10, is rho = 0.88 worth reporting at all, or should I present it as descriptive only?
6. The five tests form one causal chain, so they aren't independent. Is Holm the right correction, or is the correction itself the wrong framing?

Code, data and a 12-page write-up: https://github.com/Shelikeit69/haze-drivers-research

I built this with an AI coding assistant, which is part of why I want people to check the stats. Blunt answers welcome.

From what I understand, correlation only looks for a connection between two variables, while regression explicitly looks to see if one variable's increase/decrease positively or negatively impacts another variable's increase/decrease.

Hi! This is my second semester studying Statistics at a top university. However, I came from a poor high school education, and I wasn't used to studying regularly or keeping up with the university workload. As a result, I failed Calculus I.

I realized that I urgently need to change my study habits. I also want advice on how to do well in my degree and, most importantly, truly learn the subjects, especially considering the use of AI and other tools.

I would love to hear your thoughts on how I can become a better student. What study methods, habits, and strategies do you recommend?

Thank you!

I’m considering taking a statistics course, and I’ve been thinking about the next steps—whether it’s significantly more beneficial for my career to pursue a technical specialization, a master’s, or even a PhD