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Mathematics Applications & Interpretation (AI) IA Exemplar: US GDP… | RevisionDojo
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IB Mathematics Applications & Interpretation (AI) HL Internal Assessment Example
How Can The Monthly GDP Of The United States Be Used To Model The
Monthly Values Of The S&P 500 And NASDAQ For The Last 10 Years, And
Which Stock Index Has A Better Fitting Model To Allow For Accurate
Predictions?HL
4
Official IB Result
11/20
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11/20
0
10
20
6.1·Suggestion
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The rationale provides context but would be strengthened by explicitly identifying a research gap or novel contribution of this study.
Criteria A: Presentation
3/4
0
2
4
Criteria Strands
A.1Coherence and logical development
Good
A.2Organization and structure
Good
A.3Conciseness and relevance
Moderate
Criteria Feedback
Clear and logical structure with a defined introduction, body and conclusion
Coherent progression of sections (introduction → data collection → model testing → evaluation)
Effective use of headings, table of contents and appendices to guide the reader
Occasional digressions and verbosity (repeated definitions, raw tables in body) disrupt conciseness
Minor errors in captions and labels (e.g., “NASDAO” typo) affect professionalism
Inconsistent bibliography formatting detracts from overall polish
1.1·Weakness
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Table 1 caption contains a typo (“NASDAO”); ensure accuracy in all headings and labels for professional presentation.
1.2·Suggestion
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Bibliography entries vary in format (italicization, punctuation). Adopt a single citation style (e.g., APA) for presentation coherence.
Criteria B: Mathematical Communication
2/4
0
2
4
Criteria Strands
B.1Mathematical language and notation
Good
B.2Multiple representations
Good
B.3Clarity and consistency
Good
Criteria Feedback
Use of multiple representations (tables, scatterplots, overlaid regression curves) aids understanding
Units of measurement (e.g., months since June 2014) are not always specified
Lack of residual or diagnostic plots limits depth of representation
2.1·Weakness
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The hypothesis section uses notation correctly but occasionally mixes variable notation. Ensure consistent use of LaTeX notation throughout.
2.2·Strength
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The formula for is correctly presented, reflecting sound mathematical communication.
2.3·Weakness
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The definitions of and introduce a different symbol than . Unify the dependent‐variable notation to avoid confusion.
2.4·Suggestion
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Figure 1 effectively visualizes GDP trend over time, but adding a residual plot would provide deeper insight into model fit.
2.5·Suggestion
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When introducing the quadratic model, specify that is measured in months since June 2014 so units are clear in the formula.
2.6·Weakness
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Consistent notation for the dependent variable would enhance clarity (some analyses use , others ).
2.7·Strength
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The student effectively uses tables and regression graphs to compare multiple models, demonstrating clear mathematical communication.
Criteria C: Personal Engagement
2/3
0
2
3
Criteria Strands
C.1Independent thinking
Good
C.2Personal approach
Good
C.3Creativity and initiative
Good
Criteria Feedback
Clear articulation of personal motivation in economics and financial markets
Student’s choice of data range and model testing reflects individual initiative
Development of a percentage-error adjustment shows creative thinking
Reliance on standard Desmos outputs limits depth of independent methodology
Creative elements are modest and confined to basic model selection
Personal approach drives the topic but lacks deeper methodological innovation
3.1·Strength
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The student’s personal interest in economics and financial markets is clearly articulated, demonstrating authentic personal engagement in the investigation.
Criteria D: Reflection
2/3
0
2
3
Criteria Strands
D.1Depth of reflection
Good
D.2Critical analysis
Good
D.3Connection to understanding
Good
Criteria Feedback
Meaningful reflection on model limitations (overfitting, logistic plateau)
Basic critical analysis comparing R² and percentage error
Clear link between findings and understanding of market behavior
Equations and numerical computations are mostly correct
Justification of model choice shows sound understanding
Error-adjusted forecasting method is simplistic and lacks statistical rigor
Chronological ordering errors in data plotting may distort results
Major statistical tools (confidence intervals, cross-validation) are absent
5.1·Strength
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The aim and approach justify data selection period well, showing thoughtful design in accounting for pandemic and recession effects.
5.2·Weakness
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Ordering GDP values numerically instead of chronologically can distort temporal relationships; maintain time sequence when plotting for regression.
5.3·Strength
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Excluding linear regression is appropriate given data complexity; this model selection shows good mathematical understanding.
5.4·Strength
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The student justifies model choice and acknowledges overfitting risk in quartic regression, reflecting understanding of model complexity.
5.5·Suggestion
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Demonstrating a single‐point percentage error is informative, but include error distribution (e.g., standard deviation) for a complete assessment.
5.6·Weakness
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Adjusting predictions by adding average percentage error is not statistically rigorous. Consider constructing confidence intervals or using cross‐validation instead.
5.7·Weakness
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Use of average-error adjustment shows initiative but lacks statistical rigor; consider more robust forecasting methods.
