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Module 11 · Project 6 · Due the date on Canvas · Time-Series Visualization · adaptive competency DV19

Follow the signal. Protect the truth.

Reveal change over time without smoothing away the event, inventing a trend, or comparing series on a dishonest scale.

What to do for Canvas Project 6

Work through Module 11 and save its required evidence and files.
Complete the associated workspaces: Module 10, Module 11, Module 12. Previously earned completion remains valid; additional work is optional review.
Open Project 6, check the packet status, and download Project_6_Canvas_Packet.zip. For Canvas, upload at least one artifact from your work or a screenshot of your grade. The full ZIP is optional. A download is not a Canvas submission. Submit by the date on Canvas, only if you have not already submitted.

QuantegyAI feedback does not submit to Canvas. Module numbers, evidence identifiers, and Canvas weekly resource numbers are distinct.

9module steps

4interactive investigations

1time-series defense

60+minutes

Mission progress0%

Begin with the five-minute change-over-time refresh.

Begin time-series mission

By the end of this module, you will be able to

Decompose a series into trend, seasonality, and remainder, and say what each part means.
Choose a smoothing window knowing what it reveals and what it erases.
Diagnose dishonest time-series charts: truncated axes, invented trends, and misleading comparisons.
Verify a time-series claim in browser R and defend the reading.
Audit AI time-series output for invented patterns.
Apply all of this to Project 6: your DV19 Time-Series Evidence Studio, due the date on Canvas.

Your evidence map

DV19

0 XP

DV19Calculate change over timeReady to begin

DV19Separate signal componentsLocked

DV19Defend a smoothing windowLocked

DV19Distinguish shocks and shiftsLocked

DV19Verify transformations in RLocked

DV19Bound the visual claimLocked

Student learning outcomes

By the end of Module 11, students can…

Compute and interpret levels, first differences, percent changes, and indexed series — and choose the one that answers the stated question.
Separate trend, seasonality, and irregular variation in a decomposition.
Choose and justify a smoothing window, naming its alignment, peak-suppression, and edge costs.
Distinguish transient shocks from persistent level shifts, and bound temporal claims short of causality.
Verify every time-series transformation in R while keeping the raw observations accessible.

Module 11 step by step

Each step below is its own short page, so nothing runs on for screens at a time. Work through them in order — your progress is saved as you go, and the numbered bar at the top of every page jumps straight to any step.