; ;
; possible combinations; (N ncr combi) ;
; ; σ = planning value

; ; E=Z∝/2 P(1-P)n
; ;

;
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Beware! Each of the following equations holds only if certain conditions are met: µx = np; µx = µ; and Finity | Infinity | σ=N-nN-1 (σn) | σ=σn | σ=N-nN-1 xp(1-p)n if n/N > 0,05 | σ=p1-pn if n/N <0,05 |

99% confident interval ∝/2 = 0,005 = 2,576
95% confident interval ∝/2 = 0,025 = 1.96
90% confident interval ∝/2 = 0,05 = 1.645
We can be ...% confident that μ is not less than.... but no more than..... * Both and are random variables * Both and have probability distribution called the sampling distribution of * Scetch sampling distribution is mean and standard deviation and shape. [-μ] or │- p│.= sampling error
In order to solve a question you should know 3 things; mean of ; E() which is μ, the standard deviation of , called σ and of course the ‘shape’ is it a normal distribution?
T distribution df(degrees of freedom)=n -1 A. Central Limit Theorem: in selecting simple random samples of size ‘n’ from a population with mean and standard deviation , the sampling distribution of the sample mean, , approaches a normal probability distribution with mean and standard deviation /n, as the sample size becomes large (that is, as n30). Note: first, the sampling distribution of can be approximated by a normal probability distribution whenever the sample size is ‘large,’ i.e., greater than or equal to 30, regardless of the shape of the population distribution; second, whenever the population has a normal probability distribution, the sampling distribution of has a normal probability for any sample size.

., n ≥ 30, the shape of the sampling distribution is normal, the expected value is E() = µ, and the standard