Numeracy – Access to Health
Statistical Investigation of Body Mass Index (BMI)
Purpose and Aim
It focuses on:
- accurate use of SI and non-SI units,
- application of mathematical formulae,
- statistical analysis of health data,
- interpretation of graphs and charts related to BMI
This investigation aims to develop essential numeracy skills required in a health profession.
Learning Outcomes Covered
This investigation covers Learning Outcomes 1–6, including:
SI and imperial unit conversions BMI calculations Use of formulae and standard form Data presentation and interpretation Statistical measures and graphs
SI and Non-SI Units
Task 1: Unit Conversions
Correct unit conversion is essential in healthcare calculations.
Heights must be converted to metres (m) Weights must be converted to kilograms (kg) Both metric and imperial units are commonly used in health data Conclusion: Accurate unit conversions ensure reliable and safe health calculations.
The scatter graph shows a downward
linear trend, indicating that as age
increases, BMI decreases. The highest
BMI, approximately 38, was observed in an
11 year old boy, while the lowest BMI of
approximately 21 was recorded in a 14
year old boy.
Task 2: Scatter Graphs (Findings)
The scatter graphs show:a downward linear trend, a negative correlation between age and BMI. As age increases, BMI tends to decrease slightly. Conclusion: BMI changes during adolescence due to growth and physical development
The scatter graph shows a downward linear
trend, indicating that as age increases, BMI
decreases. The highest BMI, approximately
27, was observed in a 10 year old girl, while
the lowest BMI of approximately 21 was
recorded in a 14 year old girl.
This trend is identical to that seen in the
boys’ data.
Task 3: Statistical Measures
Mean Median Mode and Range
- how averages describe data,
- why the mean can be affected by outliers,
- how range shows the spread of data.
Although the mean BMI for boys (24.90) is higher than that observed for girls (24.02), the median and mode are higher for the girls’dataset. This discrepancy suggests the boys’ mean BMI may be influenced by outlier high values, which is supported by the larger range in the boys’ data (16.71) compared to the girls’ range (5.84).
STANDARD DEVIATION
Standard deviation measures:
- what standard deviation measures,
- how to interpret variability,
- why it is useful in health data analysis.
how far data values are spread from the mean. Small standard deviation → data is consistent Large standard deviation → data is more variable
Standard Deviation Explained
Standard Deviation
Calculated values: Boys’ standard deviation: 4.41 Girls’ standard deviation: 1.62 Conclusion: Boys’ BMI data is more spread out and less consistent than girls’ BMI data.
Task 4: BMI Categories
Findings:Only 20% of boys and girls are a healthy weight Most children are overweight or obese No children are underweight Conclusion: A high proportion of children have an unhealthy BMI.
The pie chart illustrates the distribution of boys across the given
weight categories based on their BMI. It reveals that only 20% of
boys are considered a healthy weight. Most boys fall into either the
overweight or obese categories, with 40% in each.
The pie chart illustrates the distribution of girls across the given
weight categories based on their BMI. It reveals that only 20%
of girls are considered a healthy weight. Half of the girls are in
the overweight category, while 30% of them are considered
obese.
While 20% of both boys and girls fall into the healthy weight
category, there are fewer girls in the obese category compared
to boys. None of the children from these samples are
considered underweight.
Charts and Comparisons
Pie charts and bar charts were used to:
show percentages
compare boys and girls
compare children and adults
Task 6: Comparison (Conclusion)
Key comparison:
Adults have a higher percentage of healthy weight individuals
Children show higher obesity rates
Possible reasons:
lifestyle, diet, physical activity, growth patterns
Task 6: Standard Form
Standard form was used to:
- express very large or very small numbers,
- improve clarity in scientific data.
Examples:
9580 = 9.58 × 10³ 0.019 = 1.9 × 10⁻²
Conclusion: Standard form allows health data to be presented clearly, accurately, and professionally.
Vegetables such as carrots should be included in a diet to maintain a healthy BMI. Below is a list of nutrients found in 100 g of raw
carrots. These values should be converted into the same units (milligrams, mg), and then written in standard form. To convert from grams (g) to milligrams (mg), multiply by 1000.
To convert from micrograms (mcg) to milligrams (mg), divide by 1000.
Some limitations include:- lack of background information,
- natural growth changes in children.
Conclusion: Results are informative but cannot be generalised to the whole population.
Limitations of the Investigation
Final Summary
From this investigation, learners developed skills in:
- unit conversion,
- BMI calculation,
- statistical analysis,
- data interpretation,
- health-related decision making
Numeracy skills are essential in healthcare.
Accurate calculations and careful interpretation help support safe and effective practice
Reflection
Why is accuracy important in health data? Which statistical measure is most reliable? How can BMI data be used responsibly in healthcare?
Statistical Investigation of Body Mass Index (BMI)
Yana Matviienko
Created on January 6, 2026
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Transcript
Numeracy – Access to Health
Statistical Investigation of Body Mass Index (BMI)
Purpose and Aim
It focuses on:
This investigation aims to develop essential numeracy skills required in a health profession.
Learning Outcomes Covered
This investigation covers Learning Outcomes 1–6, including:
SI and imperial unit conversions BMI calculations Use of formulae and standard form Data presentation and interpretation Statistical measures and graphs
SI and Non-SI Units
Task 1: Unit Conversions
Correct unit conversion is essential in healthcare calculations. Heights must be converted to metres (m) Weights must be converted to kilograms (kg) Both metric and imperial units are commonly used in health data Conclusion: Accurate unit conversions ensure reliable and safe health calculations.
The scatter graph shows a downward linear trend, indicating that as age increases, BMI decreases. The highest BMI, approximately 38, was observed in an 11 year old boy, while the lowest BMI of approximately 21 was recorded in a 14 year old boy.
Task 2: Scatter Graphs (Findings)
The scatter graphs show:a downward linear trend, a negative correlation between age and BMI. As age increases, BMI tends to decrease slightly. Conclusion: BMI changes during adolescence due to growth and physical development
The scatter graph shows a downward linear trend, indicating that as age increases, BMI decreases. The highest BMI, approximately 27, was observed in a 10 year old girl, while the lowest BMI of approximately 21 was recorded in a 14 year old girl. This trend is identical to that seen in the boys’ data.
Task 3: Statistical Measures
Mean Median Mode and Range
Although the mean BMI for boys (24.90) is higher than that observed for girls (24.02), the median and mode are higher for the girls’dataset. This discrepancy suggests the boys’ mean BMI may be influenced by outlier high values, which is supported by the larger range in the boys’ data (16.71) compared to the girls’ range (5.84).
STANDARD DEVIATION
Standard deviation measures:
how far data values are spread from the mean. Small standard deviation → data is consistent Large standard deviation → data is more variable
Standard Deviation Explained
Standard Deviation
Calculated values: Boys’ standard deviation: 4.41 Girls’ standard deviation: 1.62 Conclusion: Boys’ BMI data is more spread out and less consistent than girls’ BMI data.
Task 4: BMI Categories
Findings:Only 20% of boys and girls are a healthy weight Most children are overweight or obese No children are underweight Conclusion: A high proportion of children have an unhealthy BMI.
The pie chart illustrates the distribution of boys across the given weight categories based on their BMI. It reveals that only 20% of boys are considered a healthy weight. Most boys fall into either the overweight or obese categories, with 40% in each.
The pie chart illustrates the distribution of girls across the given weight categories based on their BMI. It reveals that only 20% of girls are considered a healthy weight. Half of the girls are in the overweight category, while 30% of them are considered obese. While 20% of both boys and girls fall into the healthy weight category, there are fewer girls in the obese category compared to boys. None of the children from these samples are considered underweight.
Charts and Comparisons
Pie charts and bar charts were used to:
show percentages
compare boys and girls
compare children and adults
Task 6: Comparison (Conclusion)
Key comparison:
Adults have a higher percentage of healthy weight individuals Children show higher obesity rates
Possible reasons:
lifestyle, diet, physical activity, growth patterns
Task 6: Standard Form
Standard form was used to:
- express very large or very small numbers,
- improve clarity in scientific data.
Examples: 9580 = 9.58 × 10³ 0.019 = 1.9 × 10⁻²Conclusion: Standard form allows health data to be presented clearly, accurately, and professionally.
Vegetables such as carrots should be included in a diet to maintain a healthy BMI. Below is a list of nutrients found in 100 g of raw carrots. These values should be converted into the same units (milligrams, mg), and then written in standard form. To convert from grams (g) to milligrams (mg), multiply by 1000. To convert from micrograms (mcg) to milligrams (mg), divide by 1000.
Some limitations include:- small sample size,
- lack of background information,
- natural growth changes in children.
Conclusion: Results are informative but cannot be generalised to the whole population.Limitations of the Investigation
Final Summary
From this investigation, learners developed skills in:
Numeracy skills are essential in healthcare. Accurate calculations and careful interpretation help support safe and effective practice
Reflection
Why is accuracy important in health data? Which statistical measure is most reliable? How can BMI data be used responsibly in healthcare?