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- probability - What is the expected number of children until having the . . .
You can consider starting from position 1 for the difference of boys girls and move up and down randomly with 50% probability until reaching zero These type of walks have been described here: What is the distribution of time's to ruin in the gambler's ruin problem (random walk)? and based on the results in those answers we can see that the
- How to resolve the ambiguity in the Boy or Girl paradox?
The net effect is that even if I don't know which one is definitely a boy, the other child can only be a girl or a boy and that is always and only a 1 2 probability (ignoring any biological weighting that girls may represent 51% of births or whatever the reality is)
- Hypothesis testing: Fishers exact test and Binomial test
The result obtained with the Fisher's exact test ("no significant difference between the proportion of girls and boys who finds that the cake tastes good") seems to contradict the results in (1) and (2), which say that the "more than 50% of the population of girls find that the cake tastes good" (1), and "no more than 50% of boys find that the
- what is the difference between a two-sample t-test and a paired t-test
While I was glancing at hypothesis tests, I saw paired and two-sample t-test but couldn't understand the difference For the explanation of these two tests, I saw the following sentence quot; Two-
- Why is gender typically coded 0 1 rather than 1 2, for example?
$\begingroup$ Using a 0 1 coding scheme is essentially useful when applying regression models among others, although several coding schemes are possible, e g -1 1 (but it will change the interpretation of the regression coefficients)
- Interpretation of regression coefficients with multiple categorical . . .
It's the difference between the (predicted) mean of girls in girls-only schools and the (predicted) mean of girls in mixed schools You can see this by looking at the design matrix or solving for a predicted value using the fitted regression equation Let's make some simple data and work through this
- Variable slopes in a fixed effects model - Cross Validated
For example, I might have height vs recorded at 10,11,12,13,14,15 years of age for some boys and girls It seems reasonable to me that in boys the height vs age slope would be steeper than in girls But modelling boys vs girls as fixed effects won't allow for different slopes where as random effects will
- How to measure the shift between two cumulative distribution functions . . .
$\begingroup$ Orthogonal comments, picky if you like, but in graphics details can matter a lot 1 Difficulty distinguishing red and green is a common problem many people have; here it doesn't matter because the two curves would be distinct whatever the colours, but even for this kind of problem, red and green curves that crossed could be a puzzle
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