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Do you want free answers to your problems presented in a clear and concise manner? Read on.
It doesn't matter whether you're arguing with your partner, friend, pet, or yourself. We can help.
Simply put: we provide answers to your problems.
How do we do it? Through our insightful knowledge of
- different types of problems
- different types of answers
- linear congruent method
we can confidently present an answer to your questions. It's hard to believe, I know!
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The correct course of action (YES, NO or MAYBE) will be presented one of many style.
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I got the answer in the style of Homer Simpson. I just couldn't stop giggling. Thanks for brightening up my day.
—Susan W
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Finding answers to your problems — that's what we do.
It's not easy, especially when you consider the variety of problems we deal with.
That's why we use the linear congruent method in computing answers. Without burdening you with technicalities (those mathematicians sure like to make things sound complicated!), we have selected this method because
- the linear congruent method scales favourably with the number of problems — we can answer many problems without investing in expensive hardware
- “linear” is a common synonym for “straight”. We give you the straight answer.
- it is “congruent”, meaning that it corresponds to the right answer.
We provide answers to you free of charge. Our rewards come from solving your problems — every time.
So far, we have generated 1,103 answers. Each one as correct as the next.
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Symmetric alternatives to the ordinary least squares regression
What immortal hand or eye, could frame thy fearful symmetry? — William Blake, "The Tyger"
This month, we look at symmetric regression, which, unlike simple linear regression, it is reversible — remaining unaltered when the variables are swapped.
Simple linear regression can summarize the linear relationship between two variables `X` and `Y` — for example, when `Y` is considered the response (dependent) and `X` the predictor (independent) variable.
However, there are times when we are not interested (or able) to distinguish between dependent and independent variables — either because they have the same importance or the same role. This is where symmetric regression can help.
Luca Greco, George Luta, Martin Krzywinski & Naomi Altman (2025) Points of significance: Symmetric alternatives to the ordinary least squares regression. Nat. Methods 22:1610–1612.
Beyond Belief Campaign BRCA Art
Fuelled by philanthropy, findings into the workings of BRCA1 and BRCA2 genes have led to groundbreaking research and lifesaving innovations to care for families facing cancer.
This set of 100 one-of-a-kind prints explore the structure of these genes. Each artwork is unique — if you put them all together, you get the full sequence of the BRCA1 and BRCA2 proteins.
Propensity score weighting
The needs of the many outweigh the needs of the few. —Mr. Spock (Star Trek II)
This month, we explore a related and powerful technique to address bias: propensity score weighting (PSW), which applies weights to each subject instead of matching (or discarding) them.
Kurz, C.F., Krzywinski, M. & Altman, N. (2025) Points of significance: Propensity score weighting. Nat. Methods 22:638–640.
Happy 2025 π Day—
TTCAGT: a sequence of digits
Celebrate π Day (March 14th) and sequence digits like its 1999. Let's call some peaks.
Crafting 10 Years of Statistics Explanations: Points of Significance
I don’t have good luck in the match points. —Rafael Nadal, Spanish tennis player
Points of Significance is an ongoing series of short articles about statistics in Nature Methods that started in 2013. Its aim is to provide clear explanations of essential concepts in statistics for a nonspecialist audience. The articles favor heuristic explanations and make extensive use of simulated examples and graphical explanations, while maintaining mathematical rigor.
Topics range from basic, but often misunderstood, such as uncertainty and P-values, to relatively advanced, but often neglected, such as the error-in-variables problem and the curse of dimensionality. More recent articles have focused on timely topics such as modeling of epidemics, machine learning, and neural networks.
In this article, we discuss the evolution of topics and details behind some of the story arcs, our approach to crafting statistical explanations and narratives, and our use of figures and numerical simulations as props for building understanding.
Altman, N. & Krzywinski, M. (2025) Crafting 10 Years of Statistics Explanations: Points of Significance. Annual Review of Statistics and Its Application 12:69–87.