Interpreting software output without paid software

Learning objectives

• Read a generic statistical output table without brand-specific software.
• Translate estimate, SE, statistic, df, p, CI, R² and residual summaries into factory language.
• Flag when a result is unsafe to act on.

Core idea

Correct interpretation depends on question, design, data and assumptions, not on the vendor of the output. Any generic table with estimates, standard errors, statistics, degrees of freedom, p-values and CIs can be read.

Reading checklist

• Verify the model matches the question.
• Check n, missing values and how categories were coded (reference level).
• Interpret effect with units and against a reference.
• Check CI width and assumption diagnostics before deciding.
• State limitations and the next action, not just the p-value.

Garment-factory example

A generic regression table shows slope 3.1 N/SPI (SE 0.6, t = 5.2, df = 38, p < 0.001, 95% CI 1.9 to 4.3, R² = 0.38). The engineer reports: 'Each extra stitch/inch adds about 3 N of strength (95% CI 1.9–4.3), explaining 38% of variation; useful but not sufficient — fabric weight must also be studied.'

Method

1. Confirm the design matches the question.
2. Read effect + CI first, p second.
3. Check assumption diagnostics before acting.

Common mistakes

• Equating a green p-value with a proven root cause.
• Treating high R² as proof the model is valid.
• Confusing precision of software output with quality of the data.