L6 & L7 Social Science Statistics
By the end of lesson 6, you should be able to:
- Explain how a sampling distribution is created.
- Determine the mean,
- standard deviation of a sample mean= st. dev. (of original data) divided by square root of n (or the sample size of each sample)
- and shape of a distribution of sample means.
- any sample size larger than 30 (n>30) will be a normal distribution
- State and apply the Central Limit Theorem
- and the Law of Large Numbers: if the sample size is large then the sample mean will be close to the population mean.
- also called Law of Averages
- also applies to proportions
REMEMBER:
* Understanding Sampling Distributions is key to understanding all of the statistical inference methods that will be talked about this year.
* The Z-score determines what you shade on the Normal Probability Aplet. If it is negative and asks for a value less than a number, you only shade once to the left. If the z-score is positive and asks for a value less than a number, shade to the left twice. If the z-score is positive and asks for a value greater than a number, shade to the right once and so on.
> x=z0+u (z-score times st. dev. + mean)
By the end of lesson 7, you should be able to:
- Calculate probabilities using a distribution of sample means
z=value−meanstandard deviation=x¯−μσ/n−−√
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