mathematics

Nan Laird

Nan Laird is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Nan Laird rather than just read about it. In short: Nan McKenzie Laird (born September 18, 1943) is the Harvey V. Fineberg Professor of Public Health, Emerita in Biostatistics at the Harvard T.H.

Key takeaways

Nan Laird belongs to mathematics; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Nan Laird to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Nan Laird from memory before moving on to harder problems.

Reference excerpt

Nan McKenzie Laird (born September 18, 1943) is the Harvey V. Fineberg Professor of Public Health, Emerita in Biostatistics at the Harvard T.H. Chan School of Public Health. She served as Chair of the Department from 1990 to 1999. She was the Henry Pickering Walcott Professor of Biostatistics from 1991 to 1999.

Education Laird began her undergraduate studies at Rice University in 1961, first majoring in mathematics, before switching to French. She left Rice in her junior year in college and moved to New York City. Later, she resumed studies at University of Georgia in computer science before eventually switching to statistics and earned her BA in 1969. Laird worked between 1969 and 1971 as a computer programmer on the Apollo program at MIT's Draper Laboratory before starting her graduate studies at Harvard University in statistics in 1971. She received her PhD from Harvard in 1975 under Arthur Dempster and was hired as a faculty member directly after graduation. She remained at Harvard until her retirement, when she became an emeritus professor.

Career and research Laird is well known for many seminal papers in biostatistics applications and methods, including the expectation–maximization algorithm.

Selected publications

DerSimonian, R.; Laird, N. (1986), "Meta-analysis in clinical trials", Controlled Clinical Trials, 7 (3): 177–188, doi:10.1016/0197-2456(86)90046-2, PMID 3802833 Laird NM and Ware, JH. (1982) "Random effects models for longitudinal data: an overview of recent results". Biometrics; 38:963-974. Dempster, A. P.; Laird, N.; Rubin, D. B. (1977), "Maximum likelihood from incomplete data via the EM algorithm", Journal of the Royal Statistical Society, Series B, 39 (1): 1–38, doi:10.1111/j.2517-6161.1977.tb01600.x, JSTOR 2984875

Honors and awards Her honors include the third International Prize in Statistics in 2021, the 25th Annual Distinguished Statistician Lecture from the University of Connecticut, the American Statistical Association and Pfizer in 2016, the 25th Annual Lowell Reed Lecturer, from the American Public Health Association in 2011, the Samuel S. Wilks Award, from the American Statistical Association in 2011, the Myra Samuels Lecturer award from Purdue University in 2004, the Janet L. Norwood Award in 2003 from the American Statistical Association, the Florence Nightingale David Award in 2001 from the Committee of Presidents of Statistical Societies, and several other fellowships. Laird is a Fellow of the American Statistical Association, as well as the Institute of Mathematical Statistics. She is an Elected Member of the International Statistical Institute.

References

Worked examples

Example 1 — a first encounter with Nan Laird

Start with the simplest possible case. Write down what Nan Laird claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Nan Laird before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Nan Laird ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Nan Laird

In research: Nan Laird appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Nan Laird in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Nan Laird is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1943 births, 20th-century American statisticians, 20th-century American women mathematicians, so understanding it makes those chapters shorter.
In everyday life: Look for Nan Laird outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Nan Laird in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Nan Laird means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Nan Laird out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Nan Laird in simple terms?

Nan McKenzie Laird (born September 18, 1943) is the Harvey V. Fineberg Professor of Public Health, Emerita in Biostatistics at the Harvard T.H.

Why does Nan Laird matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Nan Laird?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Nan Laird.