MARKET DATA ANALYTICS OF GLOBAL EV OUTLOOK 2024 PPT.pptx

This is piece delved into the analysis of the trajectory Global EV Outlook 2024
Moving towards increased affordability using insights from Naked Statistics by Charles Wheelan to defend or support arguments made.

1. MARKET DATA ANALYTICS YAKUBU AKUYINGA SHAIBU MARKET RESEARCH TOPIC Global EV Outlook 2024 Moving towards increased affordability VISUALIZATION INSIGHTS SUPPORTED BY NAKED STATISTICS: Stripping the Dread from the Data by Charles Wheelan

2. Was the data analysis done well? Yes, overall the analysis was done well. The Global EV Outlook 2024 provides a clear, structured combination of historical data, forecasts, scenario modeling (STEPS, APS, NZE), and cross-regional comparisons. It adheres to several best practices from Naked Statistics (pp 97-98) Good use of descriptive statistics : The report summarizes complex global EV trends into digestible figures (e.g., projected EV sales, electricity demand by mode and region) and graphical comparisons over time .( pp 8-9) Inference from data : It draws logical forecasts about oil displacement, electricity use, and policy effects using simulation-based scenarios, which aligns with Wheelan’s view that good statistics are about inference from sampled or modeled data .(16-17)

3. What is clear? Scenario modeling clarity : The three scenario types (STEPS, APS, NZE) are well-defined, offering distinct policy-driven forecasts.(pp10-11) Visual representation : Data visualizations are strong—clear bar and line charts help communicate forecasts effectively, something Wheelan would support as an aid to comprehension.(pp10-13) Breakdown by region and mode : The report disaggregates data by country and vehicle type (e.g., 2/3W, LDV, trucks), enabling comparative analysis and aiding in identifying trends(pp40-41)

4. What is missing? According to Wheelan’s emphasis on data context and limitations: Uncertainty bounds or confidence intervals : The projections are presented as point estimates without confidence bands or sensitivity ranges. Wheelan cautions against “false precision” and would likely point out this as a weakness (see his treatment of polling error margins and overconfident predictions)- pp 3, 91-93 Explanation of methodology : While the document references IEA models, it doesn't deeply explain the underlying methods or assumptions in scenario construction, making it harder to judge robustness—something Wheelan insists is vital for transparency .( pp.13, 77-78, 115)

5. What could be misleading? Descriptive statistics masking nuance : For example, reporting that 40% of LDV sales in 2030 will be electric in STEPS might obscure the fact that this change is unevenly distributed and highly dependent on regional policies.( pp6-7 ) Aggregated projections : Wheelan warns against combining variables into single indices or summaries that may overlook variance. The report’s global-level projections (e.g., oil displacement of 12 mb /d by 2035) are useful but could obscure regional disparities and uncertainties .( pp 13-14 ) Scenario confusion risk : Readers unfamiliar with STEPS, APS , and NZE may misinterpret which data corresponds to which scenario. Wheelan stresses the importance of clarity when presenting layered data( pp 95-96 )

6. Three key tips or giveaways for better data analysis and visualization : 1. Always Provide Context and Uncertainty Why it matters : Data without context can mislead. Wheelan emphasizes that numbers need interpretation, including their limitations. Tip : Add confidence intervals, error margins, or sensitivity ranges to forecasts.( pp 92-93 ) Giveaway : If a chart shows a precise projection (e.g., "12 million barrels/day oil displacement by 2035") but doesn’t mention possible variation, it's a red flag for false precision.( pp11-13 )

7. 2. Make Complex Data Visually Intuitive Why it matters : Good visuals help people understand trends and relationships quickly. Tip : Use simple, clean charts with clear legends, time axes, and color codes to compare categories or trends.( pp10-11 ) Giveaway : If you can tell the story (e.g., EV growth by country or fuel savings) just by glancing at the graphic, that’s good visualization—as Wheelan supports through intuitive storytelling.( pp. 40-41 )

8. 3. Clarify Assumptions and Methodologies Why it matters : Statistics can be bent to tell different stories depending on the assumptions used. Tip : Disclose the underlying model, scenario definitions, or sample source.( pp 79-81 ) Giveaway : If results are shown under "scenarios" like STEPS or APS, but without explanation of how those are derived, it weakens credibility—violating Wheelan’s rule that good statistics are transparent and reproducible.( pp 91-92)

9. CONCLUSION Global EV Outlook 2024 is a strong statistical document by Wheelan's standards: descriptive, comparative, and inferential. However, it would benefit from greater transparency around its modeling assumptions and inclusion of uncertainty ranges to avoid false precision—two principles emphasized in Naked Statistics .