Hi everyone! I'm conducting a short survey for my Statistics class and would really appreciate your participation. If you're currently a college student, please consider taking a moment to complete it. Thank you so much for your help!

📌 TOPIC OF STUDY: Sleep habits among college students

👉 TARGET AUDIENCE: Currently enrolled college or university students

⏳ DURATION: Approximately 1 minute

🔗 ORIGINAL LINK: https://forms.gle/1aQSTTRnEx585cF8A

Thank you! :)

I'm a software engineering researcher, and I've developed a free, open-source educational web application called CliffInsight.

Cliff's Delta is a non-parametric effect size measure, but understanding how it is calculated and interpreted can sometimes be challenging, particularly for students and researchers who are new to statistical analysis.

CliffInsight provides a visual, step-by-step explanation of the calculation, allowing users to better understand the underlying comparisons between observations.

The tool is completely free and available here:

https://profmelattar.github.io/CliffInsight/CliffsDelta.html

Source code: https://github.com/profmelattar/CliffInsight

I'd be interested in hearing whether researchers find this kind of visualization useful for teaching or learning effect sizes, and what improvements would make it more helpful.

Can people fill out this form please. I am trying to gauge what to do with my business.

https://form.jotform.com/kinetrateam/kinetra for computer

https://app.jotform.com/kinetrateam/kinetra for app

I have a statistical dilemma I would like to clarify and start working on—and perhaps even turn into an exam topic. My question is complex; I’ll try to provide the relevant information. I want to determine the probability that GRAB—currently trading at around $4—will reach a specific price point within 2 to 5 years, based on variables like the P/E ratio, revenue, and any other parameters you might suggest. 2021 Valuation: $40 billion Revenue: $675 million Transactions: $16 billion IPO Price: $13 (calculated based on EV/Sales) 2025/2026 Valuation: $15–16 billion Revenue: $24–25 billion Profit: $200 million P/E Ratio: 41–44x I’ve calculated the same figures for Microsoft, Facebook, and Uber. I’m not sure what other companies to compare them with or how to run a statistical test (e.g., which test to use). Microsoft and Facebook went public while already profitable, whereas Uber is the closest comparable case, despite the pandemic. MICROSOFT 1986 Revenue: $140 million Profit: $24.1 million P/E Ratio: 14–15x Share Price: $21 1991 Revenue: $1.84 billion Profit: $463 million Share Price: $409 FACEBOOK 2012 Revenue: $3.71 billion Profit: $1 billion P/E Ratio: 100x Share Price: $38 2017 Revenue: $40.65 billion Profit: $15.93 billion P/E Ratio: 32x Share Price: $152 UBER 2019 Valuation: $75.5 billion (loss-making) Profit: -$3.03 billion (excluding share divestments) Share Price: $45 ($15 during the pandemic) 2025 Revenue: $52.05 billion Profit +$10 billion Share price $70–$80 P/E ratio 17–19x Can you name specific companies or explain the starting point for a concrete statistical calculation regarding GRAB returning to $13—or reaching a specific price—based on the performance of other companies?

I made a visual explanation connecting the sample mean to ordinary least squares regression.

The main idea is that the sample mean can be understood as the best constant prediction for a dataset. If you treat the data as a vector, projecting it onto the span of the ones vector gives the average times the ones vector.

Then the same idea extends to OLS regression: y gets projected onto the column space of X, the fitted values are the projection, and the residual is perpendicular to that space.

I made it for people who have seen the normal equations before but want a more intuitive picture of what least squares is actually doing.

I was doing a practice exam for my Statistics university exam and I got stuck on this question, does anyone want to reply with answers / solutions in the comments?

Basically, I’m trying to see if there’s a correlation between the last names of people and their closest friends. Thank you!

I’m collecting participants for data in my fun stats project, the more responses the better

Hey guys, I appreciate it if anyone can fill out this form: https://forms.gle/fr6264QGnAmR2K7t7 for my statistics class. Your data wont be shared and will be private. Please take a moment to fill this out 🙏

Hey guys trying to run an experiment so if anyone could respond that'd be great (bigger sample size the better, obv.) I'm trying to report on the effective of vitamins/supplements, preferably ones alike asgwagandha.

My question: have you ever taken ashwagandha/known anyone who’s taken it? If so, did it work or you/them? Yes or no.

We kept running into the same problem with time-series data during our analysis: forecasts get updated, but old values get overwritten. It was hard to answer to “What did we actually know at a given point in time?”

So we built TimeDB, it lets you store overlapping forecast revisions, keep full history, and run proper as-of backtests.

Quick 5-min Colab demo:
https://colab.research.google.com/github/rebase-energy/timedb/blob/main/examples/quickstart.ipynb

Would love feedback from anyone dealing with forecasting or versioned time-series data.

Hi everyone! I’m a high school student conducting an independent research project related to coffee shop prices & demand. My 2-3 minute survey consists of a few simple questions about your coffee buying habits & your responses will be anonymous. Note: this survey is for people in the US who buy coffee by the cup from coffee shops (at least occasionally), not people who drink exclusively from home. I’d really appreciate anyone taking the time to respond. Thanks!