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At least one of them is a boy. What is the probability that both children are boys? Gardner initially gave the answers 1 / 2 and 1 / 3 , respectively, but later acknowledged that the second question was ambiguous. [1] Its answer could be 1 / 2 , depending on the procedure by which the information "at least one of them is a ...
The equation expresses the fact that the first person has no one to share a birthday, the second person cannot have the same birthday as the first ( 364 / 365 ), the third cannot have the same birthday as either of the first two ( 363 / 365 ), and in general the n th birthday cannot be the same as any of the n − 1 preceding birthdays.
Although the 30 samples were all simulated under the null, one of the resulting p-values is small enough to produce a false rejection at the typical level 0.05 in the absence of correction. Multiple comparisons arise when a statistical analysis involves multiple simultaneous statistical tests, each of which has a potential to produce a "discovery".
To determine the suitable name for the newborn child, one first finds out the number of the newborn within the family, and only then chooses the male/female name, according to the gender of the newborn. So, for example, if a baby girl is born after three boys, her name would be Moonaga (4th born, female) as she is the fourth child within the ...
In statistics, multiple correspondence analysis (MCA) is a data analysis technique for nominal categorical data, used to detect and represent underlying structures in a data set. It does this by representing data as points in a low-dimensional Euclidean space .
The small size and simplicity of the example allow simple validation of the rules of interpretation. But the method will be more valuable when the data set is large and complex. Other methods suitable for this type of data are available. Procrustes analysis is compared to the MFA in. [2]
Image credits: reddit.com #9. The Great Stink of London in 1858. One summer the heat dried up the River Thames (where all the human waste went) and an unbearable smell pervaded throughout the ...
The goal of matching is to reduce bias for the estimated treatment effect in an observational-data study, by finding, for every treated unit, one (or more) non-treated unit(s) with similar observable characteristics against which the covariates are balanced out (similar to the K-nearest neighbors algorithm).