Growth Charts & Percentiles: Z-Scores, LMS Method & BMI-for-Age

What you'll build

What you will be able to do

Each lab ends with a concrete result that you build yourself and the checker confirms, cell by cell.

• Turn height-for-age z-scores of twelve children into percentiles, find the z-scores of the standard percentile lines and compare the share below −2 SD with what a normal distribution expects.
• Calculate weight-for-age z-scores for ten boys from their L, M and S values, turn them into percentiles, handle the L = 0 case and count the children below −2 SD.
• Invert the LMS formula to compute the 3rd, 50th and 97th percentile weights for ages 2–10 and draw them as a growth chart.
• Calculate BMI for ten boys, turn it into a BMI-for-age z-score with LMS, label the weight-status category and count the children at or above the 85th percentile.
• Compute Noah's exact age at each visit, his height velocity in cm per year between visits and the intervals where growth slowed.
• Place each of Mila's visits between the major percentile lines, count how many lines she has crossed since her first visit and flag the visits that need a closer look.
• Interpolate L, M and S linearly for children aged between table rows, compute their z-scores and see how much error the completed-year shortcut adds.
• Summarise a screening cohort the way nutrition surveys do: the share below −2 SD, the observed distribution in z-score bands and the comparison with the reference normal curve in a chart.