STAT 1000Q Lecture Notes - Lecture 14: Binomial Distribution, Standard Deviation, Sampling Distribution

• School: UConn
• Department: Statistics
• Course: STAT 1000Q
• Professor: Robert Apruzese

Document Summary

Know definition, distributions, calculation of probabilities, (cid:272)al(cid:272)ulatio(cid:374) of the (cid:373)ea(cid:374)(cid:894) (cid:895) a(cid:374)d sta(cid:374)dard de(cid:448)iatio(cid:374) (cid:894) (cid:895) for a discrete rv using calc. Plug in both l1 and l2 on calc. Understand graphical interpretation of the standard deviation. Calculation of probabilities using calc (binompdf and binomcdf) Understand how p affects the shape of the binomial distribution. Definition and properties of continuous random variables and their probability density functions. Ge(cid:374)eral properties, ho(cid:449) a(cid:374)d affe(cid:272)t the (cid:272)e(cid:374)ter a(cid:374)d shape. Use of normalcdf and invnorm, methods of determining if sample data came from a normal distribution, relationship between a standard normal and non-standard normal distribution. Sampling distribution of the sample mean x. K(cid:374)o(cid:449) the for(cid:373)ulas for x a(cid:374)d x . How the shape of the sampling distribution of x changes when population sampled from is normal. Know properties of the central limit theorem (clt) How to apply normalcdf and invnorm to the sampling distribution of x when it is approx normal.