Average Weight Percentile Calculator

Average Weight Percentile Calculator & Guide :root { –primary-color: #004a99; –success-color: #28a745; –background-color: #f8f9fa; –text-color: #333; –border-color: #ddd; –card-background: #fff; –shadow: 0 2px 5px rgba(0,0,0,0.1); } body { font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; background-color: var(–background-color); color: var(–text-color); line-height: 1.6; margin: 0; padding: 0; display: flex; flex-direction: column; align-items: center; padding-top: 20px; padding-bottom: 40px; } .container { width: 95%; max-width: 960px; background-color: var(–card-background); padding: 30px; border-radius: 8px; box-shadow: var(–shadow); margin-bottom: 30px; } h1, h2, h3 { color: var(–primary-color); text-align: center; margin-bottom: 20px; } h1 { font-size: 2.2em; } h2 { font-size: 1.8em; margin-top: 30px; border-bottom: 2px solid var(–primary-color); padding-bottom: 10px; } h3 { font-size: 1.4em; margin-top: 25px; color: var(–primary-color); } .calculator-wrapper { background-color: var(–card-background); 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Average Weight Percentile Calculator

Understand where an individual's weight stands relative to others of the same age and sex using our comprehensive percentile calculator and guide.

Enter the age in completed months.
Male Female Select the biological sex.
Enter the weight in kilograms.
Enter the height in centimeters.

Your Results

BMI:
BMI Percentile:
Weight Percentile:
Height Percentile:
Calculations are based on WHO (World Health Organization) growth standards, which use complex statistical models (like the LMS method) to determine percentiles for weight-for-age, height-for-age, and BMI-for-age. This calculator provides an approximation based on simplified interpolation of standard charts.

What is Average Weight Percentile?

The average weight percentile refers to a child's weight measurement in relation to other children of the same age and sex. It's not about an "average" weight in absolute terms, but rather a ranking. For example, if a child is at the 75th percentile for weight, it means their weight is greater than 75% of children of the same age and sex, and less than 25%. This metric is crucial for monitoring a child's growth and identifying potential health concerns like underweight, overweight, or obesity.

Who should use it? Parents, pediatricians, healthcare providers, and anyone involved in child health monitoring should use weight percentile data. It's a standard tool for assessing growth patterns from birth up to age 5, and sometimes beyond, depending on the growth charts used.

Common misconceptions:

  • Myth: The 50th percentile is the "ideal" weight. Reality: The 50th percentile simply means the child is average for their age and sex. Children can be healthy at various percentiles as long as their growth is consistent and follows a typical pattern.
  • Myth: A child must stay on the same percentile line. Reality: While consistent growth is ideal, some fluctuations are normal. Rapid or significant drops or jumps in percentile can be more concerning than a stable position on a higher or lower percentile.
  • Myth: Percentiles are only about weight. Reality: Percentiles are calculated for weight-for-age, height-for-age, and BMI-for-age, providing a more complete picture of a child's physical development.

Average Weight Percentile Formula and Mathematical Explanation

Calculating exact percentiles for growth charts is complex, typically involving the LMS (Lambda, Mu, Sigma) method developed by the WHO. This method uses statistical modeling to fit curves to reference data. For practical purposes and simpler calculators, approximations or interpolations based on published WHO growth charts are often used. The core idea is to compare a child's measurement (weight, height, or BMI) against a distribution of measurements from a large, healthy reference population.

The process generally involves:

  1. Determining the child's age and sex.
  2. Obtaining the child's measurement (e.g., weight in kg).
  3. Looking up the corresponding reference data for that age and sex from a standard growth chart (e.g., WHO standards).
  4. Calculating the percentile rank based on the reference data.

For BMI-for-age, the calculation is:

BMI = Weight (kg) / (Height (m))^2

Where Height is converted to meters (Height in cm / 100).

The percentile is then determined by comparing this BMI value against the BMI-for-age distribution for the specific age and sex.

Variables Table

Variable Meaning Unit Typical Range
Age Age of the child Months 0 – 60 (for WHO standards)
Sex Biological sex of the child Categorical Male, Female
Weight Child's measured weight Kilograms (kg) 0.5 – 30+
Height Child's measured height Centimeters (cm) 45 – 120+
BMI Body Mass Index kg/m² Varies widely with age
Percentile Rank compared to peers % 0 – 100

Practical Examples (Real-World Use Cases)

Understanding the average weight percentile is best done through examples:

Example 1: A 2-Year-Old Boy

  • Inputs:
    • Age: 24 months
    • Sex: Male
    • Weight: 12.5 kg
    • Height: 86 cm
  • Calculations:
    • Height in meters: 86 cm / 100 = 0.86 m
    • BMI: 12.5 kg / (0.86 m)^2 = 12.5 / 0.7396 ≈ 16.9 kg/m²
  • Outputs (Approximate):
    • Weight Percentile: 50th
    • Height Percentile: 50th
    • BMI Percentile: 50th
  • Interpretation: This 24-month-old boy is at the 50th percentile for weight and height, meaning he is average compared to other boys his age. His BMI percentile is also average. This indicates consistent growth along the average curve.

Example 2: A 4-Year-Old Girl

  • Inputs:
    • Age: 48 months
    • Sex: Female
    • Weight: 18 kg
    • Height: 105 cm
  • Calculations:
    • Height in meters: 105 cm / 100 = 1.05 m
    • BMI: 18 kg / (1.05 m)^2 = 18 / 1.1025 ≈ 16.3 kg/m²
  • Outputs (Approximate):
    • Weight Percentile: 75th
    • Height Percentile: 85th
    • BMI Percentile: 60th
  • Interpretation: This 4-year-old girl is heavier than 75% of girls her age but lighter than 25%. She is taller than 85% of girls her age. Her BMI percentile of 60th suggests she is in the healthy weight range, but on the higher side of average. It's important to note her height percentile is higher than her weight percentile, which is a positive sign. Consistent monitoring is key.

How to Use This Average Weight Percentile Calculator

Using our calculator is straightforward. Follow these steps to get your child's weight percentile:

  1. Enter Age: Input the child's age in completed months. For example, 3 years and 6 months is 3 * 12 + 6 = 42 months.
  2. Select Sex: Choose 'Male' or 'Female' based on the child's biological sex.
  3. Enter Weight: Provide the child's current weight in kilograms (kg).
  4. Enter Height: Provide the child's current height in centimeters (cm).
  5. Calculate: Click the "Calculate Percentile" button.

How to Read Results:

  • Primary Result (Weight Percentile): This is the main output, showing where the child's weight ranks among peers. A higher percentile means a heavier child relative to others.
  • Intermediate Values:
    • BMI: The calculated Body Mass Index.
    • BMI Percentile: How the child's BMI ranks compared to peers. This is often used to classify weight status (underweight, healthy weight, overweight, obesity).
    • Height Percentile: How the child's height ranks compared to peers.
  • Formula Explanation: Provides context on the calculation method.

Decision-Making Guidance:

Consult with a pediatrician or healthcare provider for a definitive interpretation. Generally:

  • Percentiles below 5th: May indicate underweight.
  • Percentiles between 5th and 85th: Typically considered healthy weight.
  • Percentiles between 85th and 95th: May indicate overweight.
  • Percentiles above 95th: Often indicate obesity.

Remember that growth patterns and individual factors are important. A stable percentile is often more significant than the specific percentile number itself. Use the "Copy Results" button to easily share findings with your healthcare provider.

Key Factors That Affect Average Weight Percentile Results

Several factors influence a child's weight percentile, and understanding them is key to interpreting the results accurately:

  1. Genetics: Family history plays a significant role in a child's natural body composition, metabolism, and potential growth trajectory. Some children are genetically predisposed to be larger or smaller than average.
  2. Nutrition: The quality and quantity of food intake are primary drivers of weight gain. A balanced diet rich in nutrients supports healthy growth, while deficiencies or excessive calorie intake can skew percentiles. This is a critical factor that parents can influence.
  3. Physical Activity Levels: Regular exercise helps build muscle mass and manage body fat. Children with sedentary lifestyles may gain weight more easily, potentially increasing their weight percentile, while active children might have a lower BMI percentile.
  4. Health Conditions: Certain medical conditions, such as hormonal imbalances, metabolic disorders, or chronic illnesses, can significantly impact a child's growth rate and weight. Endocrine issues, for instance, can lead to slower or faster growth than expected.
  5. Sleep Patterns: Adequate sleep is essential for growth hormone release and overall metabolic regulation. Insufficient or disrupted sleep can negatively affect growth and weight management, potentially influencing percentiles.
  6. Socioeconomic Factors: Access to nutritious food, safe environments for physical activity, and quality healthcare can be influenced by socioeconomic status. These factors indirectly affect a child's nutritional status and growth patterns.
  7. Prematurity and Birth Weight: Premature babies or those with very low birth weights may follow different growth trajectories initially. Growth charts often have specific considerations for these infants, especially in the early months.

Frequently Asked Questions (FAQ)

What is the difference between weight percentile and BMI percentile?
Weight percentile ranks a child's weight against peers of the same age and sex. BMI percentile ranks their Body Mass Index (BMI), which considers both weight and height, against peers. BMI percentile is often considered a better indicator of body fatness and potential weight-related health risks.
At what age are weight percentiles most important?
Weight percentiles are particularly important during infancy and early childhood (birth to age 5), as this is a critical period for physical development. The WHO growth standards are widely used for this age range.
Can a child be in a high weight percentile but still be healthy?
Yes. If a child is consistently in a high percentile (e.g., 75th or 85th) and their height percentile is similar, and they are otherwise healthy and active, they may simply be a larger-framed child. However, percentiles above the 85th often warrant closer monitoring for potential overweight or obesity risks.
What if my child's weight and height percentiles are very different?
A significant difference can indicate disproportionate growth. For example, if height percentile is much lower than weight percentile, it might suggest slower growth in height relative to weight gain. Conversely, if height percentile is much higher, it might indicate rapid linear growth. A pediatrician should evaluate such discrepancies.
How often should weight percentiles be checked?
Well-child check-ups typically include weight and height measurements. For infants, this might be monthly or bi-monthly. For older children, check-ups every 6-12 months are common. Your pediatrician will advise on the appropriate frequency based on your child's individual growth pattern.
Does the calculator use CDC or WHO growth charts?
This calculator is based on WHO (World Health Organization) growth standards, which are recommended for infants and children from birth up to age 5 globally. The CDC charts are often used for children aged 2 and older in the United States.
What are the WHO growth standards?
The WHO growth standards are a set of international references based on breastfed infants and young children who are part of a study population representing optimal growth conditions. They provide benchmarks for assessing the growth of infants and children worldwide.
Can I use this calculator for adults?
No, this calculator is specifically designed for children using pediatric growth standards (WHO). Adult weight assessment typically uses BMI categories without age-specific percentiles, as growth has ceased.

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This is a placeholder approximation. // For accurate results, integrate with a library or detailed lookup tables. function getApproximatePercentile(ageMonths, sex, bmi) { // Placeholder data – replace with actual WHO data lookup/interpolation // These are rough estimates for demonstration purposes. var data = { male: [ { age: 0, bmi_p3: 11.5, bmi_p5: 12.0, bmi_p15: 13.0, bmi_p50: 14.5, bmi_p85: 16.0, bmi_p95: 17.5, bmi_p97: 18.0 }, { age: 6, bmi_p3: 14.0, bmi_p5: 14.5, bmi_p15: 15.5, bmi_p50: 17.0, bmi_p85: 19.0, bmi_p95: 21.0, bmi_p97: 22.0 }, { age: 12, bmi_p3: 14.5, bmi_p5: 15.0, bmi_p15: 16.0, bmi_p50: 17.5, bmi_p85: 19.5, bmi_p95: 21.5, bmi_p97: 22.5 }, { age: 18, bmi_p3: 14.8, bmi_p5: 15.3, bmi_p15: 16.3, bmi_p50: 17.8, bmi_p85: 19.8, bmi_p95: 21.8, bmi_p97: 22.8 }, { age: 24, bmi_p3: 15.0, bmi_p5: 15.5, bmi_p15: 16.5, bmi_p50: 18.0, bmi_p85: 20.0, bmi_p95: 22.0, bmi_p97: 23.0 }, { age: 36, bmi_p3: 15.2, bmi_p5: 15.7, bmi_p15: 16.7, bmi_p50: 18.2, bmi_p85: 20.2, bmi_p95: 22.2, bmi_p97: 23.2 }, { age: 48, bmi_p3: 15.3, bmi_p5: 15.8, bmi_p15: 16.8, bmi_p50: 18.3, bmi_p85: 20.3, bmi_p95: 22.3, bmi_p97: 23.3 }, { age: 60, bmi_p3: 15.4, bmi_p5: 15.9, bmi_p15: 16.9, bmi_p50: 18.4, bmi_p85: 20.4, bmi_p95: 22.4, bmi_p97: 23.4 } ], female: [ { age: 0, bmi_p3: 11.0, bmi_p5: 11.5, bmi_p15: 12.5, bmi_p50: 14.0, bmi_p85: 15.5, bmi_p95: 17.0, bmi_p97: 17.5 }, { age: 6, bmi_p3: 13.5, bmi_p5: 14.0, bmi_p15: 15.0, bmi_p50: 16.5, bmi_p85: 18.5, bmi_p95: 20.5, bmi_p97: 21.5 }, { age: 12, bmi_p3: 14.0, bmi_p5: 14.5, bmi_p15: 15.5, bmi_p50: 17.0, bmi_p85: 19.0, bmi_p95: 21.0, bmi_p97: 22.0 }, { age: 18, bmi_p3: 14.5, bmi_p5: 15.0, bmi_p15: 16.0, bmi_p50: 17.5, bmi_p85: 19.5, bmi_p95: 21.5, bmi_p97: 22.5 }, { age: 24, bmi_p3: 14.8, bmi_p5: 15.3, bmi_p15: 16.3, bmi_p50: 17.8, bmi_p85: 19.8, bmi_p95: 21.8, bmi_p97: 22.8 }, { age: 36, bmi_p3: 15.0, bmi_p5: 15.5, bmi_p15: 16.5, bmi_p50: 18.0, bmi_p85: 20.0, bmi_p95: 22.0, bmi_p97: 23.0 }, { age: 48, bmi_p3: 15.1, bmi_p5: 15.6, bmi_p15: 16.6, bmi_p50: 18.1, bmi_p85: 20.1, bmi_p95: 22.1, bmi_p97: 23.1 }, { age: 60, bmi_p3: 15.2, bmi_p5: 15.7, bmi_p15: 16.7, bmi_p50: 18.2, bmi_p85: 20.2, bmi_p95: 22.2, bmi_p97: 23.2 } ] }; var relevantData = data[sex]; if (!relevantData) return "N/A"; // Find the closest age data point var closestEntry = relevantData.reduce(function(prev, curr) { return (Math.abs(curr.age – ageMonths) < Math.abs(prev.age – ageMonths) ? curr : prev); }); // Simple linear interpolation between percentiles if needed (basic) // This is highly simplified. Real LMS requires more complex math. if (bmi < closestEntry.bmi_p3) return "<3%"; if (bmi < closestEntry.bmi_p5) return "3%"; if (bmi < closestEntry.bmi_p15) return "5%"; if (bmi < closestEntry.bmi_p50) return "15%"; if (bmi < closestEntry.bmi_p85) return "50%"; if (bmi < closestEntry.bmi_p95) return "85%"; if (bmi 97%"; } // Placeholder functions for weight and height percentiles – these require complex lookup tables function getApproximateWeightPercentile(ageMonths, sex, weightKg) { // Placeholder data – replace with actual WHO data lookup/interpolation var data = { male: [ { age: 0, w_p3: 2.5, w_p5: 2.6, w_p15: 3.0, w_p50: 3.5, w_p85: 4.0, w_p95: 4.5, w_p97: 4.7 }, { age: 6, w_p3: 6.0, w_p5: 6.3, w_p15: 7.0, w_p50: 8.0, w_p85: 9.0, w_p95: 10.0, w_p97: 10.5 }, { age: 12, w_p3: 7.5, w_p5: 7.8, w_p15: 8.5, w_p50: 9.5, w_p85: 10.5, w_p95: 11.5, w_p97: 12.0 }, { age: 18, w_p3: 8.5, w_p5: 8.8, w_p15: 9.5, w_p50: 10.5, w_p85: 11.5, w_p95: 12.5, w_p97: 13.0 }, { age: 24, w_p3: 9.2, w_p5: 9.5, w_p15: 10.2, w_p50: 11.2, w_p85: 12.2, w_p95: 13.2, w_p97: 13.7 }, { age: 36, w_p3: 10.5, w_p5: 10.8, w_p15: 11.5, w_p50: 12.5, w_p85: 13.5, w_p95: 14.5, w_p97: 15.0 }, { age: 48, w_p3: 11.5, w_p5: 11.8, w_p15: 12.5, w_p50: 13.5, w_p85: 14.5, w_p95: 15.5, w_p97: 16.0 }, { age: 60, w_p3: 12.5, w_p5: 12.8, w_p15: 13.5, w_p50: 14.5, w_p85: 15.5, w_p95: 16.5, w_p97: 17.0 } ], female: [ { age: 0, w_p3: 2.3, w_p5: 2.4, w_p15: 2.8, w_p50: 3.2, w_p85: 3.7, w_p95: 4.1, w_p97: 4.3 }, { age: 6, w_p3: 5.5, w_p5: 5.7, w_p15: 6.3, w_p50: 7.2, w_p85: 8.2, w_p95: 9.2, w_p97: 9.7 }, { age: 12, w_p3: 7.0, w_p5: 7.3, w_p15: 8.0, w_p50: 9.0, w_p85: 10.0, w_p95: 11.0, w_p97: 11.5 }, { age: 18, w_p3: 8.0, w_p5: 8.3, w_p15: 9.0, w_p50: 10.0, w_p85: 11.0, w_p95: 12.0, w_p97: 12.5 }, { age: 24, w_p3: 8.8, w_p5: 9.1, w_p15: 9.8, w_p50: 10.8, w_p85: 11.8, w_p95: 12.8, w_p97: 13.3 }, { age: 36, w_p3: 10.0, w_p5: 10.3, w_p15: 11.0, w_p50: 12.0, w_p85: 13.0, w_p95: 14.0, w_p97: 14.5 }, { age: 48, w_p3: 11.0, w_p5: 11.3, w_p15: 12.0, w_p50: 13.0, w_p85: 14.0, w_p95: 15.0, w_p97: 15.5 }, { age: 60, w_p3: 12.0, w_p5: 12.3, w_p15: 13.0, w_p50: 14.0, w_p85: 15.0, w_p95: 16.0, w_p97: 16.5 } ] }; var relevantData = data[sex]; if (!relevantData) return "N/A"; var closestEntry = relevantData.reduce(function(prev, curr) { return (Math.abs(curr.age – ageMonths) < Math.abs(prev.age – ageMonths) ? curr : prev); }); if (weightKg < closestEntry.w_p3) return "<3%"; if (weightKg < closestEntry.w_p5) return "3%"; if (weightKg < closestEntry.w_p15) return "5%"; if (weightKg < closestEntry.w_p50) return "15%"; if (weightKg < closestEntry.w_p85) return "50%"; if (weightKg < closestEntry.w_p95) return "85%"; if (weightKg 97%"; } function getApproximateHeightPercentile(ageMonths, sex, heightCm) { // Placeholder data – replace with actual WHO data lookup/interpolation var data = { male: [ { age: 0, h_p3: 48.0, h_p5: 48.5, h_p15: 50.0, h_p50: 52.0, h_p85: 54.0, h_p95: 55.5, h_p97: 56.0 }, { age: 6, h_p3: 62.0, h_p5: 63.0, h_p15: 65.0, h_p50: 67.0, h_p85: 69.0, h_p95: 71.0, h_p97: 72.0 }, { age: 12, h_p3: 71.0, h_p5: 72.0, h_p15: 74.0, h_p50: 76.0, h_p85: 78.0, h_p95: 80.0, h_p97: 81.0 }, { age: 18, h_p3: 77.0, h_p5: 78.0, h_p15: 80.0, h_p50: 82.0, h_p85: 84.0, h_p95: 86.0, h_p97: 87.0 }, { age: 24, h_p3: 81.0, h_p5: 82.0, h_p15: 84.0, h_p50: 86.0, h_p85: 88.0, h_p95: 90.0, h_p97: 91.0 }, { age: 36, h_p3: 88.0, h_p5: 89.0, h_p15: 91.0, h_p50: 93.0, h_p85: 95.0, h_p95: 97.0, h_p97: 98.0 }, { age: 48, h_p3: 94.0, h_p5: 95.0, h_p15: 97.0, h_p50: 99.0, h_p85: 101.0, h_p95: 103.0, h_p97: 104.0 }, { age: 60, h_p3: 99.0, h_p5: 100.0, h_p15: 102.0, h_p50: 104.0, h_p85: 106.0, h_p95: 108.0, h_p97: 109.0 } ], female: [ { age: 0, h_p3: 47.0, h_p5: 47.5, h_p15: 49.0, h_p50: 51.0, h_p85: 53.0, h_p95: 54.5, h_p97: 55.0 }, { age: 6, h_p3: 61.0, h_p5: 62.0, h_p15: 64.0, h_p50: 66.0, h_p85: 68.0, h_p95: 70.0, h_p97: 71.0 }, { age: 12, h_p3: 70.0, h_p5: 71.0, h_p15: 73.0, h_p50: 75.0, h_p85: 77.0, h_p95: 79.0, h_p97: 80.0 }, { age: 18, h_p3: 75.0, h_p5: 76.0, h_p15: 78.0, h_p50: 80.0, h_p85: 82.0, h_p95: 84.0, h_p97: 85.0 }, { age: 24, h_p3: 79.0, h_p5: 80.0, h_p15: 82.0, h_p50: 84.0, h_p85: 86.0, h_p95: 88.0, h_p97: 89.0 }, { age: 36, h_p3: 86.0, h_p5: 87.0, h_p15: 89.0, h_p50: 91.0, h_p85: 93.0, h_p95: 95.0, h_p97: 96.0 }, { age: 48, h_p3: 92.0, h_p5: 93.0, h_p15: 95.0, h_p50: 97.0, h_p85: 99.0, h_p95: 101.0, h_p97: 102.0 }, { age: 60, h_p3: 97.0, h_p5: 98.0, h_p15: 100.0, h_p50: 102.0, h_p85: 104.0, h_p95: 106.0, h_p97: 107.0 } ] }; var relevantData = data[sex]; if (!relevantData) return "N/A"; var closestEntry = relevantData.reduce(function(prev, curr) { return (Math.abs(curr.age – ageMonths) < Math.abs(prev.age – ageMonths) ? curr : prev); }); if (heightCm < closestEntry.h_p3) return "<3%"; if (heightCm < closestEntry.h_p5) return "3%"; if (heightCm < closestEntry.h_p15) return "5%"; if (heightCm < closestEntry.h_p50) return "15%"; if (heightCm < closestEntry.h_p85) return "50%"; if (heightCm < closestEntry.h_p95) return "85%"; if (heightCm 97%"; } function calculatePercentile() { var age = getElement("age").value; var sex = getElement("sex").value; var weight = getElement("weight").value; var height = getElement("height").value; var ageError = getElement("ageError"); var weightError = getElement("weightError"); var heightError = getElement("heightError"); var resultsContainer = getElement("resultsContainer"); // Reset errors ageError.innerText = "; ageError.classList.remove('visible'); weightError.innerText = "; weightError.classList.remove('visible'); heightError.innerText = "; heightError.classList.remove('visible'); var isValid = true; if (!validateInput(age, "age", 0, 60, "Age")) isValid = false; // WHO charts typically up to 60 months if (!validateInput(weight, "weight", 0.1, 50, "Weight")) isValid = false; // Reasonable upper limit for children if (!validateInput(height, "height", 40, 120, "Height")) isValid = false; // Reasonable range for children if (!isValid) { resultsContainer.style.display = 'none'; return; } var ageMonths = parseInt(age); var weightKg = parseFloat(weight); var heightCm = parseFloat(height); var bmi = calculateBMI(weightKg, heightCm); var bmiPercentile = getApproximatePercentile(ageMonths, sex, bmi); var weightPercentile = getApproximateWeightPercentile(ageMonths, sex, weightKg); var heightPercentile = getApproximateHeightPercentile(ageMonths, sex, heightCm); getElement("primaryResult").innerText = weightPercentile; getElement("bmiResult").innerHTML = 'BMI: ' + bmi.toFixed(2) + ' kg/m²'; getElement("bmiPercentileResult").innerHTML = 'BMI Percentile: ' + bmiPercentile + ''; getElement("weightPercentileResult").innerHTML = 'Weight Percentile: ' + weightPercentile + ''; getElement("heightPercentileResult").innerHTML = 'Height Percentile: ' + heightPercentile + ''; resultsContainer.style.display = 'block'; updateChart(ageMonths, sex, weightKg, bmi); } function resetCalculator() { getElement("age").value = "36"; getElement("sex").value = "male"; getElement("weight").value = "15.0"; getElement("height").value = "95.0"; getElement("resultsContainer").style.display = 'none'; // Clear errors getElement("ageError").innerText = "; getElement("ageError").classList.remove('visible'); getElement("weightError").innerText = "; getElement("weightError").classList.remove('visible'); getElement("heightError").innerText = "; getElement("heightError").classList.remove('visible'); // Reset chart if needed if (myChart) { myChart.destroy(); myChart = null; getElement('chartCanvas').remove(); var canvasContainer = document.createElement('div'); canvasContainer.id = 'chartCanvas'; getElement('resultsContainer').appendChild(canvasContainer); } } function copyResults() { var primaryResult = getElement("primaryResult").innerText; var bmiValue = getElement("bmiResult").innerText.replace('BMI: ', "); var bmiPercentile = getElement("bmiPercentileResult").innerText.replace('BMI Percentile: ', "); var weightPercentile = getElement("weightPercentileResult").innerText.replace('Weight Percentile: ', "); var heightPercentile = getElement("heightPercentileResult").innerText.replace('Height Percentile: ', "); var assumptions = "Age: " + getElement("age").value + " months, Sex: " + getElement("sex").value + ", Weight: " + getElement("weight").value + " kg, Height: " + getElement("height").value + " cm"; var textToCopy = "Average Weight Percentile Results:\n\n" + "Primary Result (Weight Percentile): " + primaryResult + "\n" + "BMI: " + bmiValue + "\n" + "BMI Percentile: " + bmiPercentile + "\n" + "Height Percentile: " + heightPercentile + "\n\n" + "Key Assumptions:\n" + assumptions; // Use navigator.clipboard for modern browsers if (navigator.clipboard && navigator.clipboard.writeText) { navigator.clipboard.writeText(textToCopy).then(function() { alert('Results copied to clipboard!'); }).catch(function(err) { console.error('Failed to copy text: ', err); fallbackCopyTextToClipboard(textToCopy); }); } else { fallbackCopyTextToClipboard(textToCopy); } } function fallbackCopyTextToClipboard(text) { var textArea = document.createElement("textarea"); textArea.value = text; textArea.style.position = "fixed"; textArea.style.top = "0"; textArea.style.left = "0"; textArea.style.width = "2em"; textArea.style.height = "2em"; textArea.style.padding = "0"; textArea.style.border = "none"; textArea.style.outline = "none"; textArea.style.boxShadow = "none"; textArea.style.background = "transparent"; document.body.appendChild(textArea); textArea.focus(); textArea.select(); try { var successful = document.execCommand('copy'); var msg = successful ? 'successful' : 'unsuccessful'; alert('Results copied to clipboard! (' + msg + ')'); } catch (err) { alert('Oops, unable to copy'); } document.body.removeChild(textArea); } // Charting Logic var myChart = null; var chartCanvasContainer = document.createElement('div'); chartCanvasContainer.id = 'chartCanvas'; getElement('resultsContainer').appendChild(chartCanvasContainer); function updateChart(ageMonths, sex, weightKg, bmi) { var canvasContainer = getElement('chartCanvas'); if (myChart) { myChart.destroy(); } canvasContainer.innerHTML = "; // Recreate canvas var canvas = getElement('percentileChart'); var ctx = canvas.getContext('2d'); // Simplified data points for chart – replace with more robust data if available var chartData = { labels: ['3%', '5%', '15%', '50%', '85%', '95%', '97%'], datasets: [ { label: 'Weight (kg)', data: [], // Placeholder, needs actual weight data points for the age/sex borderColor: 'var(–primary-color)', backgroundColor: 'rgba(0, 74, 153, 0.1)', fill: false, tension: 0.1, pointRadius: 5, pointHoverRadius: 7 }, { label: 'BMI (kg/m²)', data: [], // Placeholder, needs actual BMI data points for the age/sex borderColor: 'var(–success-color)', backgroundColor: 'rgba(40, 167, 69, 0.1)', fill: false, tension: 0.1, pointRadius: 5, pointHoverRadius: 7 } ] }; // Populate chart data based on the approximate percentile data used earlier var percentileData = { male: [ { age: 0, w_p3: 2.5, w_p5: 2.6, w_p15: 3.0, w_p50: 3.5, w_p85: 4.0, w_p95: 4.5, w_p97: 4.7, bmi_p3: 11.5, bmi_p5: 12.0, bmi_p15: 13.0, bmi_p50: 14.5, bmi_p85: 16.0, bmi_p95: 17.5, bmi_p97: 18.0 }, { age: 6, w_p3: 6.0, w_p5: 6.3, w_p15: 7.0, w_p50: 8.0, w_p85: 9.0, w_p95: 10.0, w_p97: 10.5, bmi_p3: 14.0, bmi_p5: 14.5, bmi_p15: 15.5, bmi_p50: 17.0, bmi_p85: 19.0, bmi_p95: 21.0, bmi_p97: 22.0 }, { age: 12, w_p3: 7.5, w_p5: 7.8, w_p15: 8.5, w_p50: 9.5, w_p85: 10.5, w_p95: 11.5, w_p97: 12.0, bmi_p3: 14.5, bmi_p5: 15.0, bmi_p15: 16.0, bmi_p50: 17.5, bmi_p85: 19.5, bmi_p95: 21.5, bmi_p97: 22.5 }, { age: 18, w_p3: 8.5, w_p5: 8.8, w_p15: 9.5, w_p50: 10.5, w_p85: 11.5, w_p95: 12.5, w_p97: 13.0, bmi_p3: 14.8, bmi_p5: 15.3, bmi_p15: 16.3, bmi_p50: 17.8, bmi_p85: 19.8, bmi_p95: 21.8, bmi_p97: 22.8 }, { age: 24, w_p3: 9.2, w_p5: 9.5, w_p15: 10.2, w_p50: 11.2, w_p85: 12.2, w_p95: 13.2, w_p97: 13.7, bmi_p3: 15.0, bmi_p5: 15.5, bmi_p15: 16.5, bmi_p50: 18.0, bmi_p85: 20.0, bmi_p95: 22.0, bmi_p97: 23.0 }, { age: 36, w_p3: 10.5, w_p5: 10.8, w_p15: 11.5, w_p50: 12.5, w_p85: 13.5, w_p95: 14.5, w_p97: 15.0, bmi_p3: 15.2, bmi_p5: 15.7, bmi_p15: 16.7, bmi_p50: 18.2, bmi_p85: 20.2, bmi_p95: 22.2, bmi_p97: 23.2 }, { age: 48, w_p3: 11.5, w_p5: 11.8, w_p15: 12.5, w_p50: 13.5, w_p85: 14.5, w_p95: 15.5, w_p97: 16.0, bmi_p3: 15.3, bmi_p5: 15.8, bmi_p15: 16.8, bmi_p50: 18.3, bmi_p85: 20.3, bmi_p95: 22.3, bmi_p97: 23.3 }, { age: 60, w_p3: 12.5, w_p5: 12.8, w_p15: 13.5, w_p50: 14.5, w_p85: 15.5, w_p95: 16.5, w_p97: 17.0, bmi_p3: 15.4, bmi_p5: 15.9, bmi_p15: 16.9, bmi_p50: 18.4, bmi_p85: 20.4, bmi_p95: 22.4, bmi_p97: 23.4 } ], female: [ { age: 0, w_p3: 2.3, w_p5: 2.4, w_p15: 2.8, w_p50: 3.2, w_p85: 3.7, w_p95: 4.1, w_p97: 4.3, bmi_p3: 11.0, bmi_p5: 11.5, bmi_p15: 12.5, bmi_p50: 14.0, bmi_p85: 15.5, bmi_p95: 17.0, bmi_p97: 17.5 }, { age: 6, w_p3: 5.5, w_p5: 5.7, w_p15: 6.3, w_p50: 7.2, w_p85: 8.2, w_p95: 9.2, w_p97: 9.7, bmi_p3: 13.5, bmi_p5: 14.0, bmi_p15: 15.0, bmi_p50: 16.5, bmi_p85: 18.5, bmi_p95: 20.5, bmi_p97: 21.5 }, { age: 12, w_p3: 7.0, w_p5: 7.3, w_p15: 8.0, w_p50: 9.0, w_p85: 10.0, w_p95: 11.0, w_p97: 11.5, bmi_p3: 14.0, bmi_p5: 14.5, bmi_p15: 15.5, bmi_p50: 17.0, bmi_p85: 19.0, bmi_p95: 21.0, bmi_p97: 22.0 }, { age: 18, w_p3: 8.0, w_p5: 8.3, w_p15: 9.0, w_p50: 10.0, w_p85: 11.0, w_p95: 12.0, w_p97: 12.5, bmi_p3: 14.5, bmi_p5: 15.0, bmi_p15: 16.0, bmi_p50: 17.5, bmi_p85: 19.5, bmi_p95: 21.5, bmi_p97: 22.5 }, { age: 24, w_p3: 8.8, w_p5: 9.1, w_p15: 9.8, w_p50: 10.8, w_p85: 11.8, w_p95: 12.8, w_p97: 13.3, bmi_p3: 14.8, bmi_p5: 15.3, bmi_p15: 16.3, bmi_p50: 17.8, bmi_p85: 19.8, bmi_p95: 21.8, bmi_p97: 22.8 }, { age: 36, w_p3: 10.0, w_p5: 10.3, w_p15: 11.0, w_p50: 12.0, w_p85: 13.0, w_p95: 14.0, w_p97: 14.5, bmi_p3: 15.0, bmi_p5: 15.5, bmi_p15: 16.5, bmi_p50: 18.0, bmi_p85: 20.0, bmi_p95: 22.0, bmi_p97: 23.0 }, { age: 48, w_p3: 11.0, w_p5: 11.3, w_p15: 12.0, w_p50: 13.0, w_p85: 14.0, w_p95: 15.0, w_p97: 15.5, bmi_p3: 15.1, bmi_p5: 15.6, bmi_p15: 16.6, bmi_p50: 18.1, bmi_p85: 20.1, bmi_p95: 22.1, bmi_p97: 23.1 }, { age: 60, w_p3: 12.0, w_p5: 12.3, w_p15: 13.0, w_p50: 14.0, w_p85: 15.0, w_p95: 16.0, w_p97: 16.5, bmi_p3: 15.2, bmi_p5: 15.7, bmi_p15: 16.7, bmi_p50: 18.2, bmi_p85: 20.2, bmi_p95: 22.2, bmi_p97: 23.2 } ] }; var relevantData = percentileData[sex]; if (!relevantData) return; var closestEntry = relevantData.reduce(function(prev, curr) { return (Math.abs(curr.age – ageMonths) < Math.abs(prev.age – ageMonths) ? curr : prev); }); chartData.datasets[0].data = [ closestEntry.w_p3, closestEntry.w_p5, closestEntry.w_p15, closestEntry.w_p50, closestEntry.w_p85, closestEntry.w_p95, closestEntry.w_p97 ]; chartData.datasets[1].data = [ closestEntry.bmi_p3, closestEntry.bmi_p5, closestEntry.bmi_p15, closestEntry.bmi_p50, closestEntry.bmi_p85, closestEntry.bmi_p95, closestEntry.bmi_p97 ]; // Add current values as points chartData.datasets.push({ label: 'Current Values', data: [ (chartData.labels.indexOf(getApproximateWeightPercentile(ageMonths, sex, weightKg)) !== -1 ? weightKg : null), // Weight point (chartData.labels.indexOf(getApproximatePercentile(ageMonths, sex, bmi)) !== -1 ? bmi : null) // BMI point ], borderColor: 'orange', backgroundColor: 'rgba(255, 165, 0, 0.5)', fill: false, pointRadius: 7, pointHoverRadius: 9, type: 'scatter' // Use scatter for individual points }); myChart = new Chart(ctx, { type: 'line', data: chartData, options: { responsive: true, maintainAspectRatio: false, scales: { y: { beginAtZero: false, title: { display: true, text: 'Value' } }, x: { title: { display: true, text: 'Percentile Rank' } } }, plugins: { title: { display: true, text: 'Weight and BMI Percentiles Over Age (Simplified)' }, tooltip: { callbacks: { label: function(context) { var label = context.dataset.label || ''; if (label) { label += ': '; } if (context.parsed.y !== null) { label += context.parsed.y.toFixed(2); } return label; } } } } } }); } // Initialize chart on load with default values document.addEventListener('DOMContentLoaded', function() { var defaultAge = getElement("age").value || "36"; var defaultSex = getElement("sex").value || "male"; var defaultWeight = getElement("weight").value || "15.0"; var defaultHeight = getElement("height").value || "95.0"; var defaultBmi = calculateBMI(parseFloat(defaultWeight), parseFloat(defaultHeight)); updateChart(parseInt(defaultAge), defaultSex, parseFloat(defaultWeight), defaultBmi); // Add event listeners for FAQ toggles var faqQuestions = document.querySelectorAll('.faq-question'); faqQuestions.forEach(function(question) { question.addEventListener('click', function() { var faqItem = this.parentElement; faqItem.classList.toggle('open'); }); }); });

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