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.
Learn more about the central limit theorem here:
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Answer:
Quadrant I: (1,1), (4,3)
Quadrant II: (-2, 3), (-1, 1)
Step-by-step explanation:
Quadrant I points have positive x and y values. Quandrant II points have negative x values and positive y values.
Answer:
it's estimated that 750 students in the whole school dislike the 8:30am start time.
Step-by-step explanation:
Since 30 of the 40 students said "no", it means 30/40, or 3/4 of the random sample disliked the 8:30am start time. Multiply this by the whole population: 1,000 × (3/4) = 750.
Another way to check this is using 10/40 = .25 and doing 1000x.25 = 250 (the amount of students who liked it). Then subtract from whole population and it's 750.
Answer:
x ∈ All real numbers
Step-by-step explanation:
When the distributive property is applied to the left side, the parentheses can be eliminated and the equation becomes ...
-2x -6 = -2x -6
This is true for all possible values of x, "all real numbers".
