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:

Step-by-step explanation:
Multiply the two polynomials by multiplying each term

Answer:
2
Step-by-step explanation:
Answer:
60
Step-by-step explanation:
Divide the number of questions answered correctly by his percentage
51 / 85 = 0.6
Now multiply that answer by 100 to see how many questions are on the quiz
0.6 x 100 = 60
Check your work by dividing that number by the number he answered correctly
51 / 60 = 0.85
Every piece of cord is 1/10 of a meter long