As a rule of thumb, the sampling distribution of the sample proportion can be approximated by a normal probability distribution whenever the sample size is large.
<h3>What is the Central limit theorem?</h3>
- The Central limit theorem says that the normal probability distribution is used to approximate the sampling distribution of the sample proportions and sample means whenever the sample size is large.
- Approximation of the distribution occurs when the sample size is greater than or equal to 30 and n(1 - p) ≥ 5.
Thus, as a rule of thumb, the sampling distribution of the sample proportions can be approximated by a normal probability distribution when the sample size is large and each element is selected independently from the same population.
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Answer:
Everything except the last one about rigid transformations. Rigid transformations change the shape in size or the shape completely.
Answer:
The data represents <u>data</u><u> </u><u>right-skewed</u><u> </u>because it's numerical data are in the right place and showing the percentage of an event/object.
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I won't give you the answers but to do unit rate you can use proportion an example is below
In the first equation, moving 2x over gives "y = -2x-11". Placing this into the second equation gives "3x - 4(-2x-11) = 11", or "3x+8x+44 = 11", which can be reduced to "11x= -33" or "x = -3". Replacing the "x" with -3 in the first equation gives "-6 + y = -11" or "y = -5". This gives a solution of (-3, -5), or answer C.