What is the correlation between the percentage of industrialisation and economic output in 15 Southern African countries, and how can we model this relationship to predict future economic trends?HL
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4
Official IB Result
4/20
General feedback
4/20
0
10
20
No overall summary is available for this report.
6.1·Weakness
Page 13
The TI-84 screenshots and menus add visual clutter. Summarize settings and results in concise text rather than full screenshots to maintain relevance.
Criteria A: Presentation
1/4
0
2
4
Criteria Strands
A.1Coherence and logical development
Poor
A.2Organization and structure
Moderate
A.3Conciseness and relevance
Poor
Criteria Feedback
- Research question is clearly stated, providing a focus for the exploration.
- A table of contents and recognisable sections give a basic organizational framework.
- Frequent broken tables, redundant screenshots and layout issues disrupt coherence and logical flow.
- Inconsistent citation style and misaligned table entries detract from professional presentation.
1.1·Suggestion
Page 1
The split of the research question across two paragraphs disrupts fluency. Combine into one continuous statement to improve organization and reader engagement.
1.2·Strength
Page 1
The research question is clearly stated, focusing on the correlation and modelling future trends. This precise framing enhances coherence and logical development in the introduction.
1.3·Weakness
Page 2
Table of contents is present but cell alignment is inconsistent. Ensure uniform column widths and correct merged headers for better presentation.
1.4·Weakness
Page 4
Table 1 contains many empty cells and truncated country names. Reformat or split into separate tables to maintain clarity and consistency.
1.5·Weakness
Page 9
The transition between data description and choice of statistical tests lacks justification. Explain why Pearson’s correlation is preferable to other measures.
1.6·Suggestion
Page 17
Incomplete data for the DRC and Seychelles limits regional analysis. Seek alternative sources or document reasons for data gaps to strengthen validity.
1.7·Weakness
Page 27
Citations are inconsistent: mix of MLA and web. Adopt one citation style consistently (e.g., APA) for clarity and professional presentation.
Criteria B: Mathematical Communication
1/4
0
2
4
Criteria Strands
B.1Mathematical language and notation
Moderate
B.2Multiple representations
Moderate
B.3Clarity and consistency
Poor
Criteria Feedback
- Basic statistical notation (e.g., r, r²) appears in places and a scatterplot plus tables show attempt at representation.
- At least two distinct representations (tables, scatter diagram, regression equations) are provided.
- Indices and subscripts are often garbled (e.g., R^{-2} instead of R²), and symbols appear without definition.
- Figures and tables are poorly formatted or unreadable; axes lack labels and units, weakening clarity.
2.1·Suggestion
Page 3
The introduction overview of industrialisation is generic. To deepen mathematical communication, link theory to specific variables and define terms quantitatively.
2.2·Suggestion
Page 9
Multiple representations of data (tables, scatter plot, regression equations) are used. To improve, integrate clearer captions and cross-references.
2.3·Suggestion
Page 10
Axes on the scatter plot are unlabeled with units. Add axis titles and units (e.g. % for industrialisation, GDP in billions) to improve mathematical clarity.
2.4·Weakness
Page 19
The formula for R^{-2} is incorrectly typeset and misuses inverse notation. Use and standard summation notation. Ensure consistency with definition.
2.5·Weakness
Page 19
Throughout the report, appears instead of the correct coefficient of determination notation . Correct this to avoid confusion.
2.6·Suggestion
Page 20
In the cubic regression, the model yields unrealistic negative GDP at higher industrialisation. Discuss the domain of applicability and model limitations graphically.
Criteria C: Personal Engagement
0/3
0
2
3
Criteria Strands
C.1Independent thinking
Poor
C.2Personal approach
Poor
C.3Creativity and initiative
Poor
Criteria Feedback
- Independent thinking is minimal, following textbook procedures with little originality.
- Personal approach is only hinted at through a brief autobiographical note and does not meaningfully drive the exploration.
- Creativity and initiative are limited to running standard regression types without deeper justification.
3.1·Strength
Page 3
Your personal context as an SADC essay participant adds authenticity. This demonstrates genuine engagement and personal motivation driving the investigation.
3.2·Strength
Page 19
Choice of regression types (linear, quadratic, cubic, power, exponential) shows initiative. However, justify selection criteria quantitatively rather than by eye.
3.3·Suggestion
Page 26
Reflection on policy implications shows personal engagement. To deepen, propose specific policy recommendations based on mathematical findings.
Criteria D: Reflection
0/3
0
2
3
Criteria Strands
D.1Depth of reflection
Poor
D.2Critical analysis
Poor
D.3Connection to understanding
Poor
Criteria Feedback
- Reflection consists of superficial observations and lists of limitations without critical depth.
- Analysis of model assumptions and data quality is basic and descriptive, lacking probing critique.
- Connections between reflections and the student’s own mathematical understanding are not developed.
4.1·Suggestion
Page 25
The discussion of the long-term trend acknowledges model faults but remains superficial. Critically analyse the implications of extrapolating beyond the data range.
4.2·Suggestion
Page 26
Conclusion notes correlation does not imply causation—a vital reflection. For deeper critique, discuss potential confounding factors and alternative models.
4.3·Strength
Page 26
The weaknesses section effectively identifies data limitations. Consider adding a strategy for multivariate analysis to account for other economic drivers.
Criteria E: Use of Mathematics
2/6
0
3
6
Criteria Strands
E.1Relevance and level
Moderate
E.2Accuracy and correctness
Poor
E.3Knowledge and understanding
Poor
Criteria Feedback
- Uses relevant HL mathematics—Pearson correlation, multiple regression models and residual analysis—appropriately at course level.
- Demonstrates some understanding of statistical interpretation and model fitting procedures.
- Several formulas are misstated or misapplied (e.g., IQR outlier test, R² notation errors).
- Interpretations of correlation coefficients and domain limitations are flawed or missing, and data‐handling mistakes undermine accuracy.
5.1·Weakness
Page 11
The IQR outlier test formula is misapplied: upper fence should be Q3 + 1.5·IQR, not (1.5·IQR)·Q3. Correct the formula to Q3 + 1.5 × IQR.
5.2·Weakness
Page 12
Interpreting r = –0.101 as a negative trend is misleading: values in (–0.3, 0.3) indicate no linear correlation. Acknowledge the correlation is effectively zero.
5.3·Suggestion
Page 20
The cubically modelled function uses high-precision coefficients but lacks error margins. Discuss uncertainty in parameter estimates to show mathematical rigor.
5.4·Suggestion
Page 21
Residual sum of squares is calculated but not contrasted across models clearly. Present a summary table ranking models by SS_res for effective comparison.