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Module 6 — Multivariate & Time-Series Visualization

Weeks 11–12 · graduate statistics · adaptive competencies DV11–DV12

Represent higher-dimensional data and change over time without hiding structure, uncertainty, or aggregation choices.

Statistics focusfaceting, heatmaps, pair plots, PCA intuition, trend, smoothing, seasonality, rate of change

R/tool focusInline browser R labs; optional desktop RStudio/Positron for capstone; facets, heatmaps, correlation matrices, date parsing, smoothing, indexed time series

Interactive assignmentFacet/heatmap comparison, smoothing-window exploration, time-series claim audit

Interactive reading

Predict before reveal

Why can smoothing a time series be helpful and dangerous at the same time?

Smoothing can reveal trend by reducing noise, but it can hide abrupt changes, seasonality, and meaningful local variation. The window choice must be justified.

Standardization lab

The standardization lab needs JavaScript.

Multivariate representation lab

The representation lab needs JavaScript.

AI-output audit

This AI audit needs JavaScript.

Hands-on visual lab

A weekly series carrying trend, an annual cycle, and noise. Smoothing is a filter, not a cleaning step: widen the window far enough and the seasonality you were asked to report disappears by construction. Then run the browser-R lab below without leaving QuantegyAI.

This visual lab needs JavaScript.

R lab

R / Quarto / Shiny workflow

Create small multiples, heatmaps, and smoothed time-series views; explain what each preserves and hides.

# Suggested R workflow scaffold
library(tidyverse)
# 1. Import or construct the dataset
# 2. Verify summaries before visualizing
# 3. Build the ggplot2 visualization
# 4. Write the statistical interpretation and limitation note
# 5. Log any AI assistance and corrections

Adaptive reflection

What I know / where I go next

I can say when variables must be standardized before they are combined. I can choose between facets, heatmaps, pair plots, and reduced dimensions for a given question. I can name what each multivariate view preserves and what it discards. I can justify a smoothing window rather than accepting a default. I can state which dimensions my capstone visual omits.

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Capstone connection Add this module’s evidence to your capstone readiness tracker. If your explanation depends on AI, include the prompt, output, verification method, and final correction.