TAILIEUCHUNG - Kinh tế ứng dụng_ Lecture 1: Normal Distribution

For many random variables, the probability distribution is a specific bell-shaped curve, called the normal curve, or Gaussian curve. This is the most common and useful distribution in statistics. 1) Standard normal distribution The standard normal distribution has the probability density function as follows: | Applied Econometrics 1 Normal Distribution Applied Econometrics Lecture 1 Normal Distribution For many random variables the probability distribution is a specific bell-shaped curve called the normal curve or Gaussian curve. This is the most common and useful distribution in statistics. 1 Standard normal distribution The standard normal distribution has the probability density function as follows 1 -L. 2 Y P z e 2 z 2n 2n Features of the curve are 1 z2 increases in the negative exponent. Therefore P z decreases approaching 0 symmetrically in both tails. 2 The mean which is zero p 0 is the balancing point or the center of symmetry. 3 The standard deviation is one ct 1 Example If z has a standard normal distribution find P -2 z 2 1 Solution P -2 z 2 1 - P z -2 - P z 2 1 - 2. 2 General normal distribution The general normal distribution has the probability density function as follows The quantity Y which is the height of the curve at any point along the scale of X is known as the probability density of that particular value of the variable quantity X. Example The local authorities in a certain city install 2 000 electricity lamps in the streets of the city. If these lamps have an average life of 1 000 burning hours with a standard deviation of 200 hours what number of the lamps might be expected to fail in the first 700 burning hours 1 If z is continuous P z c P X c . In other words and can be used interchangeably for any continuous random variable. Written by Nguyen Hoang Bao May 17 2004 Applied Econometrics 2 Normal Distribution Solution In this case we want to find the probability corresponding to the area of the probability curve below t 700-1000 200 . We ignore the sign and enter our table at to find that the probability for lives less than 700 hours is P . Hence the expected number of failures will be 2 000 x 134. Example What number of lamps may be expected to fail between 900 and 1 300 burning hours Solution The number

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