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1. Normal distributions- studied by France’s de Moivre & Germany’s Gauss to provide foundation of statistics
2. Normal curves- bell curve graph/distribution
3. Upward cup on normal curve- less than x –  or x + 
4. Downward cup on normal curve- between x –  or x + 
5. Symmetry of normal curves- same on both sides of the mean
6. Empirical rule- 68%, 95%, 99.7%
7. Z value or z score- the number of standard deviations between x and  with . z = (x-)/
8. Standard units- mean = 0, greater than the mean = > 0, less than the mean = < 0
9. Standard normal distribution-  = 0 and  = 1, 68% between -1 & 1, 95% between -2 & 2, 99.7% between -3 & 3
10. Raw score, x = z + 
11. Area under the standard normal curve = probability using z score
12. Area under any normal curve = probability given x or raw score
13. Population parameter- numerical measures mean (), variance (2), standard deviation ), and proportion (p)
14. Statistic - numerical measures…mean (x̄ ), variance (s2), standard deviation (s) and proportion (p̂)
15. Sampling distribution- probability distribution based on a random sample
x = symbol for mean of a sampling distribution. It is = population mean 
x = symbol for standard error of the mean in a sampling distribution. Formula =/√n
18. Standard error of the mean- formula = /√n
19. Central limit theorem- z = (x-)/ (/n) when n >30
20. Normal approx. binomial dist. - n trials, r successes, p prob., q = 1-p, np> 5, nq > 5,  = np, and = √npq
21. Continuity correction- left point -0.5, right point +0.5
22. estimation- calculating the interval around the mean based on your confidence level
23. Testing- calculating whether a hypothesis claim has sufficient supporting evidence
24. average – mean (x̄ ) for sample or mean  for population