Regression model design and implementation checklist
Clarifying prediction versus effect estimation at the outset prevents variable selection and interpretation errors. Identify candidate confounders from theory first.
Regression model design and implementation checklist
Basic information
Record the analytical purpose and design.
Completed example for this section (fictional)
This fictional example is for explanation. Adapt it to your own program. Your entries will not change.
- Program and research question
- Reception waiting-time prediction
- Analysis purpose: prediction / effect estimation
- Prediction and description of associations
- Dependent variable and scale: continuous / binary, etc.
- Waiting time: minutes, continuous
- Valid sample size
- Illustrative equation only; no actual sample obtained
- Statistical software
- R is a candidate.
- Analyst and date
- Staff member A / July 2026 (fictional)
Reason for this entry: Do not imply actual data were analyzed.
Writing tips and explanation for this section
About this section
Record the analytical purpose and design.
Do not imply actual data were analyzed.
View the method explanationWriting tips
Follow each field’s heading to record the program name, focus, period, and other details. Leave undecided matters clearly marked as unresolved.
Read the full fictional exampleVariable design
Organize main independent variables, controls and candidate confounders.
Completed example for this section (fictional)
This fictional example is for explanation. Adapt it to your own program. Your entries will not change.
Scroll horizontally to view the table
| Variable | Role: main independent variable / control | Scale | Theoretical selection rationale | Dummy coding needed? |
|---|---|---|---|---|
| Preceding consultation count | Main explanatory variable | Nonnegative integer | Examine queue length and waiting-time relationships. | Not needed |
Reason for this entry: This model does not estimate causal effects.
Scroll horizontally to view the table
| Variable | Role: main independent variable / control | Scale | Theoretical selection rationale | Dummy coding needed? |
|---|---|---|---|---|
Writing tips and explanation for this section
About this section
Organize main independent variables, controls and candidate confounders.
This model does not estimate causal effects.
View the method explanationWriting tips
Keep the information in each row aligned. Scroll wide tables horizontally to see every column.
Read the full fictional exampleMulticollinearity check
Check correlations for pairs, and VIF by regressing each explanatory variable on all other explanatory variables in the model. Distinguish the focal variable from its correlation partner.
Completed example for this section (fictional)
This fictional example is for explanation. Adapt it to your own program. Your entries will not change.
Scroll horizontally to view the table
| Focal explanatory variable / correlation partner | Correlation coefficient r | Focal variable VIF | Action: exclusion / transformation, etc. |
|---|---|---|---|
| Only preceding count is an explanatory variable. | Between-variable correlation not applicable | Not applicable with only 1 explanatory variable excluding the intercept | Recheck when adding variables. |
Reason for this entry: Do not invent values for inapplicable diagnostics.
Scroll horizontally to view the table
| Focal explanatory variable / correlation partner | Correlation coefficient r | Focal variable VIF | Action: exclusion / transformation, etc. |
|---|---|---|---|
Writing tips and explanation for this section
About this section
Check correlations for pairs, and VIF by regressing each explanatory variable on all other explanatory variables in the model. Distinguish the focal variable from its correlation partner.
Do not invent values for inapplicable diagnostics.
View the method explanationWriting tips
Keep the information in each row aligned. Scroll wide tables horizontally to see every column.
Read the full fictional exampleModel fit and diagnostic findings
Record model fit and diagnostics.
Completed example for this section (fictional)
This fictional example is for explanation. Adapt it to your own program. Your entries will not change.
Scroll horizontally to view the table
| Indicator | Value | Problem present? | Action |
|---|---|---|---|
| Residual distribution and autocorrelation | Not calculated | Undetermined | Check time-ordered residuals after obtaining actual data. |
Reason for this entry: Without observed diagnostic values, leave judgments undetermined.
Scroll horizontally to view the table
| Indicator | Value | Problem present? | Action |
|---|---|---|---|
Writing tips and explanation for this section
About this section
Record model fit and diagnostics.
Without observed diagnostic values, leave judgments undetermined.
View the method explanationWriting tips
Keep the information in each row aligned. Scroll wide tables horizontally to see every column.
Read the full fictional exampleCoefficient interpretation notes
Describe the substantive meaning of main coefficients, how much change is represented.
Completed example for this section (fictional)
This fictional example is for explanation. Adapt it to your own program. Your entries will not change.
Reason for this entry: State coefficient meaning alongside unchecked issues.
Writing tips and explanation for this section
About this section
Describe the substantive meaning of main coefficients, how much change is represented.
State coefficient meaning alongside unchecked issues.
View the method explanationWriting tips
Use the example as a reference and record what you have established about your own program. Distinguish assumptions and unchecked matters from confirmed facts.
Read the full fictional exampleCompletion check
Completed example for this section (fictional)
This fictional example is for explanation. Adapt it to your own program. Your entries will not change.
- ☑ Checked: Documented theoretical variable selection before analysis
- ☐ Not checked: Diagnosed collinearity and recorded coefficient instability and actions; use VIF or other measures as appropriate to the model
- ☐ Not checked: Diagnosed residual, variance and dependence assumptions appropriate to the selected model
- ☐ Not checked: Reported effect sizes and confidence intervals alongside p-values
- ☑ Checked: Stated causal interpretation limits, including observational confounding
- ☐ Not checked: Checked sample adequacy relative to variable counts
Reason for this entry: The proposed model and causal interpretation restrictions are documented. Sample size and estimation diagnostics remain unchecked.
Writing tips and explanation for this section
About this section
The proposed model and causal interpretation restrictions are documented. Sample size and estimation diagnostics remain unchecked.
View the method explanationWriting tips
Read each item and check those you have confirmed. Do not use the fictional example’s checkbox states as findings about your own program.
Read the full fictional exampleBefore writing, check when to use the worksheet and what completion involves
Regression model design and implementation checklist is a worksheet for applying Regression Analysis in meetings or individual work. For the overall duration of the method, use “Planning example, not a universal standard duration: 1–2 weeks, including data preparation, modeling and interpretation” as a reference, adjusting for scale and resources. Fill in what you know provisionally and leave unresolved matters as questions to check next.
When this worksheet is useful
- When estimating how a program variable affects outcomes while controlling several background factors
- When identifying factors influencing outcomes to decide where intervention would have greater effects
- When exploring predictors of binary outcomes such as service reach, dropout or target achievement through logistic regression
Participants and preparation
1–2 evaluators with statistical and data analysis knowledge and skills in R, Python, SPSS or other statistical software
Bring the materials used for decisions, existing data, and differing stakeholder views to help complete the worksheet.
Suggested order
- 1. Basic informationRecord the analytical purpose and design.
- 2. Variable designOrganize main independent variables, controls and candidate confounders.
- 3. Multicollinearity checkCheck correlations for pairs, and VIF by regressing each explanatory variable on all other explanatory variables in the model. Distinguish the focal variable from its correlation partner.
- 4. Model fit and diagnostic findingsRecord model fit and diagnostics.
- 5. Coefficient interpretation notesDescribe the substantive meaning of main coefficients, how much change is represented.
- 6. Completion check
Signs the worksheet is ready for review
- Regression coefficient table: unstandardized and standardized coefficients, p-values and 95% confidence intervals
- Diagnostic plots: residual distributions and Q–Q plots
- Interpretation report: effect sizes, substantive meaning and limitations
Cautions for review
- Inferring program causation from statistical significance. In observational regression, describe associations and always state design limitations for causal interpretation.
- Overfitting: too many variables relative to sample size fit the observed data excessively and reduce generalization.
- Ignoring collinearity destabilizes coefficients. Consider diagnostics, domain knowledge and the estimation question together when responding.
Read a completed fictional example (your entries will not change)
A fictional illustrative linear model: predicted waiting time = 5 + 2 × preceding consultation count.
Basic information
- Program and research question
- Reception waiting-time prediction
- Analysis purpose: prediction / effect estimation
- Prediction and description of associations
- Dependent variable and scale: continuous / binary, etc.
- Waiting time: minutes, continuous
- Valid sample size
- Illustrative equation only; no actual sample obtained
- Statistical software
- R is a candidate.
- Analyst and date
- Staff member A / July 2026 (fictional)
Reason for this entry: Do not imply actual data were analyzed.
Variable design
Scroll horizontally to view the table
| Variable | Role: main independent variable / control | Scale | Theoretical selection rationale | Dummy coding needed? |
|---|---|---|---|---|
| Preceding consultation count | Main explanatory variable | Nonnegative integer | Examine queue length and waiting-time relationships. | Not needed |
Reason for this entry: This model does not estimate causal effects.
Multicollinearity check
Scroll horizontally to view the table
| Focal explanatory variable / correlation partner | Correlation coefficient r | Focal variable VIF | Action: exclusion / transformation, etc. |
|---|---|---|---|
| Only preceding count is an explanatory variable. | Between-variable correlation not applicable | Not applicable with only 1 explanatory variable excluding the intercept | Recheck when adding variables. |
Reason for this entry: Do not invent values for inapplicable diagnostics.
Model fit and diagnostic findings
Scroll horizontally to view the table
| Indicator | Value | Problem present? | Action |
|---|---|---|---|
| Residual distribution and autocorrelation | Not calculated | Undetermined | Check time-ordered residuals after obtaining actual data. |
Reason for this entry: Without observed diagnostic values, leave judgments undetermined.
Coefficient interpretation notes
Reason for this entry: State coefficient meaning alongside unchecked issues.
Completion check
- ☑ Checked: Documented theoretical variable selection before analysis
- ☐ Not checked: Diagnosed collinearity and recorded coefficient instability and actions; use VIF or other measures as appropriate to the model
- ☐ Not checked: Diagnosed residual, variance and dependence assumptions appropriate to the selected model
- ☐ Not checked: Reported effect sizes and confidence intervals alongside p-values
- ☑ Checked: Stated causal interpretation limits, including observational confounding
- ☐ Not checked: Checked sample adequacy relative to variable counts
Reason for this entry: The proposed model and causal interpretation restrictions are documented. Sample size and estimation diagnostics remain unchecked.
Common errors in judgment
Treating a significant coefficient as a cause
How to revise: Examine identification design and unobserved confounding.
Reason: Statistical associations differ from causal effects.
Assuming high R² guarantees prediction at a new site
How to revise: Check prediction errors in other data or periods.
Reason: Training-data fit does not guarantee generalization.
References and checked sections
- NIST/SEMATECH e-Handbook: What are the typical underlying assumptions in process modeling?NIST
- Section consulted
- Underlying assumptions
- How it relates to the text
- Check assumptions about errors and functional form, and distinguish predictive fit from causal evidence.
- NIST/SEMATECH e-Handbook: Linear Least Squares RegressionNIST／SEMATECH
- Section consulted
- Definition of a Linear Least Squares Model / Disadvantages of Linear Least Squares
- How it relates to the text
- Regression models and linear least squares; limitations of outliers and extrapolation
How to cite this page
Change history
- — Checked sources for definitions and applicability conditions, and added fictional examples, reasons for judgments and examples for every worksheet field. Revised unverified claims about history and adoption. Also reviewed existing procedures and cautions, and adjusted fixed participant counts and thresholds and generalizations about institutions. Preserved field positions, types and counts while revising fixed conditions and thresholds in the forms.
- — Correction based on independent content review (content-check-causal). Revised table headings and explanations to prevent reading VIF as a variable-pair measure. Preserved field positions and counts.