Direct answer
To assess linear regression, you need (1) the data inputs that define the model, (2) the provenance of those inputs, (3) timeliness and alignment of records, and (4) quality and assumption checks that determine whether the fitted relationship is interpretable.
What data is needed, and what “assess” means
Linear regression is a statistical method that fits an equation of the form y = b0 + b1x + error, where x is a predictor (independent variable) and y is the outcome (dependent variable). “Assessing” usually means confirming that the fitted relationship is statistically meaningful under the model’s assumptions, and understanding how sensitive the results are to data and choices.
Data inputs (the core required fields)
You need paired observations: each row must include the predictor value(s) and the target value.
- Predictor values (x): numeric features you believe may explain variation in y.
- Target values (y): the numeric outcome you are trying to model.
- Units and definitions: what each variable means (for example, whether values are levels or changes), so comparisons are valid.
- The mapping of rows: each x must correspond to the correct y for the same time period or event.
If you use multiple predictors (multiple linear regression), you need a consistent set of feature columns for every row.
Provenance: where the data comes from matters
Data provenance is what lets you verify the calculation. For linear regression, provenance includes:
- Source of each variable (for example, a provider dataset, an internal calculation, or an external feed).
- How variables were computed (exact formulas for transformations like returns, spreads, spreads-per-time, or normalization).
- How missing or non-tradable periods were handled, because regression cannot “see” what you removed—only what remains.
- Consistency rules: the same definition and calculation method should apply across the entire sample.
This matters because two datasets that look similar can encode different transformations, resulting in different regression behavior.
Timeliness and alignment checks
Even with correct formulas, regression quality depends on how time is handled.
- Timestamp alignment: ensure each predictor uses information available at the time you consider it, and that the target corresponds to the intended future or same-period horizon.
- Sampling frequency: mixing daily and intraday points without a clear resampling strategy can distort relationships.
- Overlap and leakage risks: if predictors are computed using data that includes the target period, the model may fit patterns that will not reproduce.
A practical assessment includes documenting the time window for each observation and verifying there is no unintended overlap.
Quality checks that affect coefficients and interpretation
Linear regression results are sensitive to data issues. Before interpreting any fitted equation, check:
- Missing values: how they were removed or imputed. Different strategies can change fitted parameters.
- Outliers: extreme x or y values can pull the best-fit line toward them.
- Scale and transformation: if variables are poorly scaled or mixed (for example, raw levels plus small standardized features), numerical stability and interpretation can suffer.
- Variable stability: if relationships change dramatically across time, a single global linear model may not represent a stable mechanism.
Assumptions and limitations (material failure modes)
Assessing linear regression is also about checking failure modes. Key assumptions often discussed include:
- Linearity: the expected value of y changes approximately linearly with x.
- Independence of errors: the unexplained part should not be systematically correlated across observations.
- Constant variance (homoscedasticity): the spread of errors should be roughly stable across the range of x.
If these assumptions fail, common symptoms include unstable coefficients across samples, misleading fit statistics, or residuals that show structure instead of random noise.
Historical relationship ≠ future performance
A fitted regression can look strong on historical data while being unreliable elsewhere. Changes in conditions, regime shifts, and differences between training and evaluation windows can break the relationship. Therefore, assessment should include validation using a method appropriate for your sampling scheme (for example, separating periods rather than randomly shuffling time-ordered observations).
Evidence or example: what you would verify in your own dataset
A self-checklist approach:
- Confirm each row has exactly one x value (or vector of predictors) aligned with one y target, with documented definitions. 2. Recompute at least a small sample of your x and y from the raw inputs to ensure provenance and formulas are implemented correctly. 3. Plot y vs.