STAT 1000Q Lecture Notes - Lecture 9: Chi Distribution, Random Variable

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

Document Summary

No probabilities in between values, no errors. E(cid:374)dpoi(cid:374)ts (cid:373)atte(cid:396): p(cid:894)(cid:1006) < y < (cid:1011)(cid:895) is (cid:374)ot e(cid:395)ual to p(cid:894)(cid:1006) y (cid:1011)(cid:895) Points do not exist in the real world. The p(cid:396)o(cid:271)a(cid:271)ilit(cid:455) of (cid:373)easu(cid:396)i(cid:374)g so(cid:373)eo(cid:374)e"s e(cid:454)a(cid:272)t height = (cid:1004) We know a measurement lies between certain numbers. Whe(cid:374) (cid:272)al(cid:272)ulati(cid:374)g i(cid:374)te(cid:396)(cid:448)als, it does(cid:374)"t (cid:373)atte(cid:396) if (cid:455)ou e(cid:454)(cid:272)lude the e(cid:374)dpoi(cid:374)ts o(cid:396) (cid:374)ot. The probability that x is between a and b = the area under the graph between a and b. The total area of graph = 1. Chi distribution: when skewed to the right: rom 0 to infinity. Beta distribution: 0 - 1, total area = 1. Normal distribution: symmetrical, once you know a(cid:374)d , (cid:272)a(cid:374) g(cid:396)aph it. If mean changes, shifts from left to right. If sd is changed, changes how spread out it is. Tall, thin bell or short, fat bell. If x has a normal distribution, possible values of x are negative infinity to positive infinity.