Budget Variance Analysis Case: Contrasting Excel with Tableau<sup>*</sup>
Bibliographic record
Abstract
ABSTRACT This case not only familiarizes students with data analytics but also demonstrates its practical application. It presents a scenario of an electronic components company, equips students with budgetary and financial performance information, and challenges them to prepare a visual budgetary variance analysis for a board meeting. This hands‐on exercise, conducted first in Excel and then in Tableau, enhances students' analytics skills. The learning tasks include creating formulas that depend on multiple worksheets in Excel, interpreting data results, and importing data into Tableau. Students will establish correct relations between imported tables; create formulas and new calculated fields; filter and format data; construct graphs; create workbooks, dashboards, and stories; publish graphical analyses on the Tableau cloud server; and distribute the presentation via a hyperlink. The case was tested with Master of Accountancy students in an Advanced Information Systems class and received high student evaluation scores. It is recommended for use in an undergraduate or graduate accounting or business program after students conceptualize the budgetary variance analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".