{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

Lecture11 - STAT 350 LECTURE 11 Chapter 5(5.6 Describing...

Info iconThis preview shows pages 1–6. Sign up to view the full content.

View Full Document Right Arrow Icon
STAT 350 L ECTURE 11 Chapter 5 (5.6) Describing Sample Distributions
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
T OPICS Review Central Limit Theorem Sampling Distribution of Sample Proportion Revisiting QQ Plot
Background image of page 2
T HE C ENTRAL L IMIT T HEOREM The sampling distribution of the mean can be approximated by a normal distribution when the sample size n is sufficiently large, irrespective of the sample of the population distribution. The larger the n, the better the approximation
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
T HE C ENTRAL L IMIT T HEOREM The sampling distribution of the sample mean can be approximated by a normal distribution when the sample size n is sufficiently large, irrespective of the sample of the population distribution. In general, n >= 30 is large enough The less symmetric a population is, the larger the sample size will have to be to ensure normality of the mean (exponential distribution requires n=40)
Background image of page 4
S UMMARY Large Sample Size (n>30) is approximately normal for any population distribution (Exponential, Poisson etc)
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 6
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}