Reproducibility

Run it twice. Same number.
Run it next year. Same number.

Every report ships the exact R that produced it — fixed seeds, pinned packages, isolated container. Anyone can re-run the analysis and land on your figures, including reviewer two.

See a code appendix → Why R →

The appendix, verbatimin every report

# reproducible by construction
set.seed(4271)
glm(repeat_purchase ~ ., family = binomial)
# OR discount_depth = 1.42 — every run

seed + code + container = the same answerverifiable

Why this matters

AI tools that generate analysis code on the fly produce different code — and different results — on every run. That makes the answer impossible to cite, audit, or trust for any decision that has consequences.

🔁

Deterministic

The R is written by specialist agents and independently verified — a second agent recomputes the numbers from your raw data with its own code. Same inputs produce identical outputs — including the random seed used for any sampling, splits, or simulations. No drift, no surprises.

📜

The code is in your report

Deck and Brief reports include the full code appendix; every tier ships its source on request with the actual R script. Not a high-level summary — the exact script that produced the numbers above it. Copy it, run it locally, verify it.

📌

Citable methodology

Every report has a one-click citation in APA, MLA, Chicago, and BibTeX. Link the live report URL or attach the PDF — both reference the same methodology, the same data, the same result.

Why "AI does the analysis" isn't enough

When an AI assistant writes code on every prompt, you get a different program every time — and often a different answer. That's fine for exploration. It's a problem for anything someone else has to trust.

LLM code generation Inconsistent

✗ New Python script written each run — same question can produce different code, different methods, different numbers.
✗ No version control. The "analysis" only exists in the chat session that produced it.
✗ Hallucinated outputs documented in independent reviews. Plausible-looking statistics that don't match the data.
✗ Cannot be cited — the source isn't a stable reference, it's a one-time conversation.
✗ No audit trail when a stakeholder asks "how did you get this number?"

MCP Analytics modules Reproducible

✓ Each module is a reviewed R script. Same data + same parameters always produces the same result.
✓ The R source code ships in every report. Anyone can read it, copy it, run it.
✓ Reports are persistent and searchable. Re-open any analysis from the library, any time.
✓ Citable in APA/MLA/Chicago/BibTeX — with the R source and methodology in every report, ready for a paper, a board deck, or a review.
✓ AI handles interpretation and discovery. The numbers come from R. Best of both.

This is what's in your report

Below is an excerpt from a real telecom churn analysis. The same code runs every time, with the same edge-case handling, the same model specification, the same metrics. You don't have to take our word for it — the source ships in the report appendix.

#' ## Core Analysis Pipeline
#' All statistical computations happen once in `compute_shared()` and are
#' then distributed to individual report cards.

compute_shared <- function(df, params) {
  #' ### Step 1: Parameter Setup and Data Cleaning
  #' Two parameters govern the analysis: `confidence_level` controls prediction
  #' interval width, and `top_n_features` limits how many predictors appear in
  #' the ranking chart.
  confidence_level <- params$confidence_level %||% 0.95
  top_n <- as.integer(params$top_n_features %||% 10L)

  # Coerce total_charges — may arrive as character with blank strings
  df$total_charges <- suppressWarnings(as.numeric(df$total_charges))

  # Drop rows missing the two most critical fields
  df <- df[!is.na(df$monthly_charges) & !is.na(df$tenure), ]

  # Binary churn outcome: 1 = churned, 0 = retained
  df$churn_binary <- as.integer(trimws(as.character(df$churn)) == "Yes")

  #' ### Step 3: Logistic Regression Model
  #' We fit a binary logistic regression with 15 predictors covering contract,
  #' billing, service add-ons, and demographics. The model is wrapped in
  #' `tryCatch()` to handle edge cases gracefully.
  model <- tryCatch(
    glm(churn ~ ., data = model_df, family = binomial(link = "logit")),
    error = function(e) { message("Logistic regression failed: ", e$message); NULL }
  )
  ...
}

Excerpt from analytics__telecom__churn__customer_retention — the actual R source that runs in production. Full file: 584 lines, included in every report.

Citable, reproducible, persistent

A report that exists only in a chat session can't be cited. Our reports are stable URLs and PDF documents with structured methodology blocks — built for the moment when someone asks "where did this number come from?"

APA Citation (auto-generated in every report) MCP Analytics. (2026). Telecom Customer Churn & Retention Analysis [Statistical analysis report]. Retrieved from https://mcpanalytics.ai/reports/...

BibTeX (also one-click) @misc{mcpanalytics2026churn,
  title = {Telecom Customer Churn & Retention Analysis},
  author = {{MCP Analytics}},
  year = {2026},
  url = {https://mcpanalytics.ai/reports/...}
}

Try a reproducible analysis

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WHERE NEXT

cite itCitations in every report →Four citation formats, ready to paste into your paper or deck. see itWorked examples →Open a report and re-run it — reproducibility in practice. depthChoose your depth →A fast read, a one-page answer, or the full commissioned study.

Where to start

One email with your data and your question. We take it from there.

Cymple

Data Scientist

Send me your data and question, I’ll send you the analytics.

ds@mcpanalytics.aimcpanalytics.ai

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Run it twice. Same number.Run it next year. Same number.