Answer:
+81
Step-by-step explanation:
neg times a neg = a positive
Answer:
No. When all you want to do is estimate a population parameter, you should construct a confidence interval.
Step-by-step explanation:
In this case, there is no other prior estimation about the population to test (a hypothesis to nullify). The only thing you can do is construct a confidence interval of the proportion, where the standard deviation can be calculated in function of the proportion and the sample size.
The right answer is E: "No. When all you want to do is estimate a population parameter, you should construct a confidence interval."
Add the numbers they equal 30 then u divide 30 by 2 since it’s an odd number and you get 15. so 15 is your answer
Answer:
Complete the following statements. In general, 50% of the values in a data set lie at or below the median. 75% of the values in a data set lie at or below the third quartile (Q3). If a sample consists of 500 test scores, of them 0.5*500 = 250 would be at or below the median. If a sample consists of 500 test scores, of them 0.75*500 = 375 would be at or above the first quartile (Q1).
Step-by-step explanation:
The median separates the upper half from the lower half of a set. So 50% of the values in a data set lie at or below the median, and 50% lie at or above the median.
The first quartile(Q1) separates the lower 25% from the upper 75% of a set. So 25% of the values in a data set lie at or below the first quartile, and 75% of the values in a data set lie at or above the first quartile.
The third quartile(Q3) separates the lower 75% from the upper 25% of a set. So 75% of the values in a data set lie at or below the third quartile, and 25% of the values in a data set lie at or the third quartile.
The answer is:
Complete the following statements. In general, 50% of the values in a data set lie at or below the median. 75% of the values in a data set lie at or below the third quartile (Q3). If a sample consists of 500 test scores, of them 0.5*500 = 250 would be at or below the median. If a sample consists of 500 test scores, of them 0.75*500 = 375 would be at or above the first quartile (Q1).
