#linearmodels — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #linearmodels, aggregated by home.social.
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SAT Linear Model Word Problem: Population Growth | Find x in 2015
SAT Algebra Question of the Day:
A linear model estimates the population of a city from 1991 to 2015. The population was 57 thousand in 1991 and 224 thousand in 2011. What is x thousand in 2015 (to the nearest whole number)?
Want more SAT hacks? Comment “1600” 👇
#SAT #satmaths #SATprep #linearmodels #algebra #wordproblems #testprep #SAThacks #harvard
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SAT Linear Model Word Problem: Population Growth | Find x in 2015
SAT Algebra Question of the Day:
A linear model estimates the population of a city from 1991 to 2015. The population was 57 thousand in 1991 and 224 thousand in 2011. What is x thousand in 2015 (to the nearest whole number)?
Want more SAT hacks? Comment “1600” 👇
#SAT #satmaths #SATprep #linearmodels #algebra #wordproblems #testprep #SAThacks #harvard
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Look at what I found at thriftbooks... Probably from a library , original 1971 edition (published two years before I was born!) and one of the very good (?best) and clear books written on the topic of #linearmodels @[email protected] @[email protected] @[email protected]
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‼ Announcement: Online Unfold.jl workshop ‼
📅 09.05.2025
💶 Free!
👉🏼 https://github.com/s-ccs/workshop_unfold_2025
❓ rERPs, mass univariate models & deconvolution!If you are interested in combined #EEG / #EyeTracking, in natural experiments, sequential sampling models + EEG (e.g. DriftDiffusion), #VR+EEG, - this could be a useful workshop for you!
#EEG #linearmodels #statistics
#julialangOrganized with Romy Frömer (CHBH)
and the S-CCS lab (@uni_stuttgart) -
‼ Announcement: Online Unfold.jl workshop ‼
📅 09.05.2025
💶 Free!
👉🏼 https://github.com/s-ccs/workshop_unfold_2025
❓ rERPs, mass univariate models & deconvolution!If you are interested in combined #EEG / #EyeTracking, in natural experiments, sequential sampling models + EEG (e.g. DriftDiffusion), #VR+EEG, - this could be a useful workshop for you!
#EEG #linearmodels #statistics
#julialangOrganized with Romy Frömer (CHBH)
and the S-CCS lab (@uni_stuttgart) -
📈 Models simplify complex observations by filtering out details that might not generalize to new instances, but… simplification requires assumptions.
Take #LinearModels: they assume data is fundamentally linear, dismissing deviations as mere noise.
The art lies in knowing what to keep and what to discard.
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"The Robust Beauty of Improper Linear Models in Decision Making" lives rent free in my mind. I think about this paper from 1979 ALL. THE. TIME!
TL;DR: experts can make robust linear models by just picking a few salient features from their experience. See https://www.cmu.edu/dietrich/sds/docs/dawes/the-robust-beauty-of-improper-linear-models-in-decision-making.pdf
In today's parlance the TL;DR would read "feature selection is really important."
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"The Robust Beauty of Improper Linear Models in Decision Making" lives rent free in my mind. I think about this paper from 1979 ALL. THE. TIME!
TL;DR: experts can make robust linear models by just picking a few salient features from their experience. See https://www.cmu.edu/dietrich/sds/docs/dawes/the-robust-beauty-of-improper-linear-models-in-decision-making.pdf
In today's parlance the TL;DR would read "feature selection is really important."
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In today's lecture on #StatisticalModeling, I explained how to define meaningful non-orthogonal hypotheses/contrasts in (generalized) #LinearModels.
I only learned about the difference between specifying a contrast matrix vs. a hypothesis matrix in this paper:
How to capitalize on a priori contrasts in linear (mixed) models
(by Daniel Schad et al., 2020)
https://doi.org/10.1016/j.jml.2019.104038Preprint: https://arxiv.org/abs/1807.10451