The normal distributions with the equal variance but different means 、 3111 11
11 The normal distributions with the equal variance but different means 3 1 2
The normal distributions with the same mean but different variances 12
12 The normal distributions with the same mean but different variances 2 1 3
Properties Of Normal Distribution a o&u completely determine the characterization of the normal distribution 一口Mean, median, mode are equal a The curve is symmetric about mean a The relationship between o and the area under the normal curve provides another main characteristic of the normal distribution 13
13 Properties Of Normal Distribution & completely determine the characterization of the normal distribution. Mean, median , mode are equal The curve is symmetric about mean. The relationship between and the area under the normal curve provides another main characteristic of the normal distribution
Areas under the standard normal curve d a variable that has a normal distribution with mean o and variance 1 is called the standard normal variate and is commonly designated by the letter z N(0,1) a As with any continuous variable, probability calculations here are always concerned with finding the probability that the variable assumes any value in an interval between two specific points a and b 14
Areas under the Standard Normal Curve A variable that has a normal distribution with mean 0 and variance 1 is called the standard normal variate and is commonly designated by the letter Z. N(0,1) As with any continuous variable, probability calculations here are always concerned with finding the probability that the variable assumes any value in an interval between two specific points a and b. 14
Cumulative distribution Function( The cdf ox)] for a standard normal distribution a the area under the curve) 4h Prgsx=d(n= area to the left of r from -oo to x, cumulative Probability S(-∞,∞)=1 00 D Example: What is the probability of obtaining a z value of 0. 5 or The edf for a standard normal distribution (@(x) less? a We have 0013 15
Cumulative distribution Function ( the area under the curve) from -∞ to x, cumulative Probability Example: What is the probability of obtaining a z value of 0.5 or less? We have 15 S(-, )=1