Skip to content
Aabha AI Academy
Skip to reading

Statistics

Primary reference register

Verified 14 October 2026. The teaching narrative, examples, data, exercises and diagrams are original. References support definitions, method assumptions and tool behavior; their examples and illustrations are not redistributed. Links are for further reading rather than prerequisites for using the offline guide. Online documentation can change; companion package versions are fixed separately.

Statistical concepts and methods

  1. NIST/SEMATECH Exploratory Data Analysis: exploratory purpose and checking evidence; Lessons 1–3.
  2. NIST measures of location: mean and median definitions; Lesson 2.
  3. NIST measures of scale: sample SD, range and IQR; Lesson 2.
  4. NIST detection of outliers: investigating unusual points and model dependence; Lesson 2.
  5. NIST histogram: binning, counts and distribution shape; Lesson 3.
  6. NIST box plot: quartiles, fences and alternative whisker conventions; Lessons 2–3. The course explicitly chooses 1.5-IQR whiskers rather than implying every boxplot uses them.
  7. NIST underlying assumptions: process assumptions and nonrandomness; Lessons 4 and 6.
  8. NIST completely randomized designs: assignment design; Lessons 4 and 8.
  9. NIST binomial distribution: introductory binary-trial model; Lesson 5.
  10. NIST normal distribution: normal model and sampling-distribution context; Lesson 6. The course explicitly states independence/finite-variance qualifications and avoids a universal n=30 rule.
  11. NIST confidence limits for the mean: Student-t interval and repeated-sampling meaning; Lesson 7.
  12. NIST confidence interval approach: mean-interval construction and assumptions; Lessons 6–7.
  13. NIST two-sample t-test: unequal-variance standard error and Welch–Satterthwaite degrees of freedom; Lesson 8. The course does not interpret a non-rejection as equivalence.
  14. American Statistical Association p-value statement: p-value limitations, effect size and context; Lessons 8–9.
  15. NIST scatter plot: association and visual patterns; Lesson 10.
  16. NIST model validation: residual checks and limits of R-squared; Lesson 10.

Exact-version computational references

  1. NumPy 2.3 quantile: named linear-interpolation convention.
  2. SciPy 1.17.0 independent t-test: equal_var=False, alternative, confidence_interval and return values.
  3. SciPy 1.17.0 t distribution: quantiles and tail probabilities.
  4. SciPy 1.17.0 linear regression: slope, intercept and R-squared relationship.
  5. SciPy 1.17.0 Pearson correlation: definition and interpretation boundaries; the course uses descriptive r rather than endorsing its default test on clustered shipments.
  6. Matplotlib 3.10.8 color guidance: plotting implementation considerations.
  7. W3C WAI complex images: short and detailed text equivalents for statistical graphics. Source-level accessibility measures do not constitute an assistive-technology audit.

Prior-course alignment and authorship

The existing Aabha Python for AI and Data package and SQL for Data Analysis and AI package were inspected locally, including their READMEs and syllabi. Their units cover CSV handling, table grain, grouped summaries, validation and basic chart construction. This course builds statistical reasoning on top without requiring those programming courses for the calculator route. No claim is made about a live course page, enrollment state or publication approval.

The data generator, source dictionary and exact seeds document the invented mechanism. Every chart was generated from the course's own data or explicitly labelled original miniature examples. No chart or dataset was copied from the reference websites, and no real personal information was used.

Download this source file