Answer:

And using the probability mass function we got:
Step-by-step explanation:
Let X the random variable of interest, on this case we now that:
The probability mass function for the Binomial distribution is given as:
Where (nCx) means combinatory and it's given by this formula:
And we want to find the following probability:

And using the probability mass function we got:
Reliable causal inference based on observational studies is seriously threatened by unmeasured confounding.
What is unmeasured cofounding?
- By definition, an unmeasured confounder is a variable that is connected to both the exposed and the result and could explain the apparent observed link.
- The validity of interpretation in observational studies is threatened by unmeasured confounding. The use of negative control group to reduce unmeasured confounding has grown in acceptance and popularity in recent years.
Although they've been utilised mostly for bias detection, negative controls have a long history in laboratory sciences and epidemiology of ruling out non-causal causes. A pair of negative control exposure and outcome variables can be utilised to non-parametrically determine the average treatment effect (ATE) from observational data that is vulnerable to uncontrolled confounding, according to a recent study by Miao and colleagues.
Reliable causal inference based on observational studies is seriously threatened by unmeasured confounding.
Learn more about unmeasured confounding here:
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What you must keep in mind is that there are 4 jeans in total, which represents 100%
When she bought two jeans, we have the following information:
If both of them are blue:
50% of their jeans is blue now, that is:
x = (50/100) * 4 = 2
2 jeans are blue.
This means that she did NOT have 2 blue jeans before.
Thus:
If they are not blue:
We have 4 jeans that are not blue.
Then, 0% of her jeans is blue now
answer:
0% percentage of her jeans is blue now
Answer:
yes
Step-by-step explanation:
Answer:
1⅚
Step-by-step explanation:
(3 + ⅔) ÷ 2
11/3 ÷ 2
11/3 × 1/2
11/6
1⅚