STAT 3201 Lecture Notes - Lecture 15: Normal Distribution, Random Variable
1 Mar 2017
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Document Summary
Used very often in statistics because it is a appropriate probability model in a wide variety of situations: measurement error, height of individuals, scores on exams, series of repeated measurements. Definition: a random variable y is said to have a normal probability distribution if and only if, for >0 and - < < . Y is a normally distributed random variable with parameters and . Properties: centered at and symmetric about , can"t express probabilities in closed form numerical integration techniques are used instead, if =0 and 2=1, we have the standard normal distribution n(0,1). For a random variable x with a n(0,1) distribution we can use table 4 in the. Appendix 3 to compute probabilities (all the numerical integration work was done for you): the table only gives probabilities of the type: p(z>z, other probabilities must first be converted to that kind:
