Answer:
By the Central Limit Theorem, both would be approximately normal and have the same mean. The difference is in the standard deviation, since as the sample size increases, the standard deviation decreases. So the SRS of 600 would have a smaller standard deviation than the SRS of 200.
Step-by-step explanation:
The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean
and standard deviation
, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean
and standard deviation
.
For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.
For the sampling distribution of size n of a sample proportion p, the mean is p and the standard deviation is 
Differences between SRS of 200 and of 600
By the Central Limit Theorem, both would be approximately normal and have the same mean. The difference is in the standard deviation, since as the sample size increases, the standard deviation decreases. So the SRS of 600 would have a smaller standard deviation than the SRS of 200.
The term for a point that varies greatly from all other data points is known as an <u>OUTLIER</u>
<u></u>
Explanation:
- An outlier is a data point that differs significantly from other observations. An outlier may be due to variability in the measurement or it may indicate experimental error.
- An outlier can cause serious problems in statistical analyses.
- An outlier is an observation that lies an abnormal distance from other values in a random sample from a population. In a sense, this definition leaves it up to the analyst to decide what will be considered abnormal.
- A point that falls outside the data set's inner fences is classified as a minor outlier, while one that falls outside the outer fences is classified as a major outlier.
- The data here appear to come from a linear model with a given slope and variation except for the outlier which appears to have been generated from some other model.
- Outliers can occur by chance in any distribution, but they often indicate either measurement error or that the population has a heavy-tailed distribution.
Answer:8(3x-2)
Step-by-step explanation: 8 times 3d=24X 8 times -2=-16
Answer:
9.5
Step-by-step explanation:
38 / 4 = 9.5