Regression analysis
In statistical modeling, regression analysis is a statistical method for estimating the relationship between a dependent variable and one or more independent variables.
At a glance
Key signals
0% cross fields · reaches 6 more
- Mathematics
- Statistics
- Biostatistics
- Explanation
- Examples
- Misconception
- Sourced relations 2
- Attribution
Dependencies
What this concept builds on and what it makes possible — derived from the atlas’s dependency, causal and structural relations, not from every related edge.
Foundations · builds on
Maximum Likelihood EstimationenablesRegression analysisEstablished
Open Maximum Likelihood Estimation →
Maximum Likelihood Estimation enables Regression Analysis.
Enables · leads to
Causal Inferencedepends onRegression analysisEstablished
Causal Inference depends on Regression Analysis.
Econometric modeldepends onRegression analysisEstablished
Econometrics runs on regression.
Mechanism: Econometric models estimate their coefficients by fitting regressions to economic data.
System context
Regression analysisis aMathematical modelEstablished
regression analysis is a kind of Mathematical model.
Mechanism: Regression analysis is a mathematical model that fits a formula to data, capturing how one quantity depends on others.
- Wikipedia (English & German editions) verifiedmoderate evidence
Logistic regressionis aRegression analysisEstablished
Logistic regression is a kind of Regression Analysis.
Ordinary Least Squaresis part ofRegression analysisEstablished
Ordinary Least Squares is a part of Regression Analysis.
Time series analysisis aRegression analysisEstablished
Time Series Analysis is a kind of Regression Analysis.
Structural role & consequence
Interpreted from the current atlas graph — what the connections mean, not just how many there are.
Currently dark in the atlas: no key date stored · 11 of 11 of its relations lack claim-level evidence.
atlas representation · Describes the current Thinking OS representation, not the state of the world.
Builds on 1 foundation (requires / depends-on / derived-from / emerges-from).
structural · Structural graph analysis — not a claim of importance, causation or history.
Exercises 2 annotated mental models — a concept that connects several thinking patterns.
curated · Curated annotations, not a derived measure.
Structural neighbourhood: 11 → 38 → 104 concepts reachable within 3 hops.
structural · Structural reach — being reachable is not the same as being understood.
cross-field
11 within-field, 0 cross-field
Strengths & constraints
Constraints
- Evidence coverage currently thin in the atlas — few of its relationships carry claim-level evidence. atlas representation
- No dated history stored — the atlas records no key date for this concept. atlas representation
Conditions
- Its dependency reading rests on 1 foundation relation. structural
- Read structurally — most of its relationships carry no external evidence yet, so claims here are graph-derived. structural
Dependency radial
What this concept builds on (left) and what it makes possible (right) — derived from dependency and causal relations.
What builds on this
2 concepts build on this directly, 2 in total, across 2 disciplines.
Structural downstream reach along dependency edges — not a claim of historical necessity.
Seen through each discipline
How this concept sits in each of its fields — derived from its real connections in the graph, not asserted.
Through this lens it connects to Mathematical model.
Through this lens it connects to Correlation.
Through this lens it connects to Hypothesis testing, Confounding, Logistic regression and Survival analysis.
Through this lens it connects to Hypothesis testing, Econometric model, Ordinary Least Squares and Time series analysis.
Related ideas to explore
Concepts that look related but are not yet connected here — candidates for a connection to reason about, not established links.
This idea also appears in…
The same structure shows up in other disciplines. These are real recurrences drawn from the graph — a starting point for asking “what carries over, and what changes?”
Cause and effect31 disciplines · 31 concepts
Signal vs noise14 disciplines · 9 concepts
Concepts
shares a mental model · shares a mental model
shares a mental model
shares a mental model
shares a mental model
shares a mental model
shares a mental model
Mental models at work here
Cause and effect
One thing genuinely bringing about another — as opposed to two things merely moving together. Establishing it needs a mechanism and controls, not just a pattern.
Ask: if I changed the cause, would the effect change? And what else might explain the link?
Signal vs noise
Real data mixes a meaningful pattern (signal) with random variation (noise); the skill is telling them apart before you act on either.
Ask how much of what you see could be chance. Small samples are mostly noise; averaging and repetition pull the signal out.
- Connects 11 other ideas across 4 disciplines.
- A cross-disciplinary bridge — its connections reach into 6 other fields.
- Most of its connections are of the “Kind & structure” kind.
- It exercises 2 reusable thinking patterns.
Derived from the graph’s real structure — observations, not a score.
Sources
- Wikipedia (English & German editions) verifiedmoderate evidence
- Wikidata verifiedmoderate evidence
- Polynomial Regression verified
- Regression Diagnostics verified