The Sampling Distribution of a Sample Mean

The mean of the sampling distribution of the mean is the mean of the population from which the scores were sampled, in this case $14$.

The variance of the sampling distribution of the mean is the population variance divided by $N$. The population SD is $6$, so the population variance is $36$. $36/9 = 4$

The standard error is the standard deviation of the population divided by the square root of $N$. In this case, $12/4 = 3$

According to the central limit theorem, regardless of the shape of the parent population, the sampling distribution of the mean approaches a normal distribution as $N$ increases. In this case, a sample size of $30$ is sufficiently large to cause the sampling distribution of the mean to look about normal.

Mean = $75$, SD = $2$, Skew = about $0$: This problem is asking about the sampling distribution of the mean: Mean = $75$, SD = $10 \sqrt(25)$ = $10/5$ = $2$, Skew = about $0$ because the central limit theorem states that the sampling distribution of the mean would be about normal with a large enough $N$.