Answer:
a) Mean is 12.9
b) Standard Error
i) n = 16, Standard Error = 1
The shape of the sampling distribution means is not normal
ii) n = 49 , Standard Error = 0.5714285714
The shape of the sampling distribution means is normal
iii) n = 121, Standard Error = 0.3636363636
The shape of the sampling distribution means is normal
c)Describe the pattern as n increases
As the number of random samples(n) increases the value for the standard error decreases
Step-by-step explanation:
Central Limit Theorem states that
if the sample size is large (generally n ≥ 30), and the standard deviation of the population is finite, then the distribution of sample means will be approximately normal.
a) 16 residents
Mean = 12.9
Standard Error = Standard Deviation /√n
= 4/√16
= 4/4
= 1
n < 30, hence, The shape of the sampling distribution means is not normal
b) 49 residents
= Mean = 12.9
Standard Error = Standard Deviation /√n
= 4/√49
= 4/7
= 0.5714285714
n > 30, hence, The shape of the sampling distribution means is normal
c) 121 residents
Mean = 12.9
Standard Error = Standard Deviation /√n
= 4/√121
= 4/11
= 0.3636363636
n > 30, hence, The shape of the sampling distribution means is normal
Describe the pattern as n increases
As the number of random samples(n) increases the value for the standard error decreases
