Using the Central Limit Theorem, it is found that:
The sampling distribution is slightly right skewed, with mean of RM 10 and standard deviation of RM 0.5.
The Central Limit Theorem establishes that, for a normally distributed random variable X, with mean [tex]\mu[/tex] and standard deviation [tex]\sigma[/tex], the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean [tex]\mu[/tex] and standard deviation [tex]s = \frac{\sigma}{\sqrt{n}}[/tex].
- For a skewed variable, if the sample size is at least 30, the shape is approximately normal, otherwise it still is skewed.
In this problem, the mean is of 10 and the standard deviation is of 2.50, hence [tex]\mu = 10, \sigma = 2.5[/tex]
Sample of 25, hence [tex]n = 25, s = \frac{2.5}{\sqrt{25}} = 0.5[/tex]
Since the underlying distribution is skewed and the sample size is less than 30, the shape of the sampling distribution is slightly right skewed.
The sampling distribution is slightly right skewed, with mean of RM 10 and standard deviation of RM 0.5.
A similar problem is given at https://brainly.com/question/25817309
