MATH1005 Lecture Notes - Lecture 11: Simple Random Sample, Central Limit Theorem, Standard Deviation

• School: University of Sydney
• Department: Mathematics
• Course: MATH1005
• Professor: Sarah Romanes

Document Summary

We have to first assume to know the contents of a box, specifically population mean and sd. To do this, we have to take a simple random sample of size n, then consider the chance error of its mean. Refer to the diagram for a summarised understanding. This is the code that you"d need to calculate the mean and sd of the box. To understand this further, remember using the box model for the sum and means of draws. This can be calculated by the individual measurement = exact value + chance error. This is being aware of the face that despite how information is extracted from the box, there is a potential to chance error. In summary, this is how it is represented in formula. The central limit theorem for the sample mean. We know that at random from a population box that the mean is normally distributed, regardless.