Your answer is A.0.0684 dkg.
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Answer:
1. G
2. Not sure what you are ask
Answer:
The mean birth weight for the sampling distribution is
3,500 grams.
Step-by-step explanation:
The sample mean is the average of the sample values collected divided by the number of the samples, while the population mean is the average or mean of all the values in the population. If the sample is random and the sample size is large enough, then the sample mean would be a good estimator of the population mean. This implies that with a randomly distributed and unbiased sample size, the sample mean and population mean will be equal, according to the central limit theorem. Therefore, the mean of the sample means will always approximate the population mean.
Answer:
-5,3
Step-by-step explanation:
Answer:
Step-by-step explanation:
Given that sample size is 130 >30. Also by central limit theorem, we know that mean (here proportion) of all means of different samples would tend to become normal with mean = average of all means(here proportions)
Hence we can assume normality assumptions here.
Proportion sample given = 92/130 = 0.7077
The mean proportion of different samples for large sample size will follow normal with mean = sample proportion and std error = square root of p(1-p)/n
Hence mean proportion p= 0.7077
q = 1-p =0.2923
Std error = 0.0399
For 95% confidence interval we find that z critical for 95% two tailed is 1,.96
Hence margin of error = + or - 1.96(std error)
= 0.0782
Confidence interval = (p-margin of error, p+margin of error)
= (0.7077-0.0782,0.7077+0.0782)
=(0.6295, 0.7859)
We are 95% confident that average of sample proportions of different samples would lie within these values in the interval for large sample sizes.
