Textbook Notes (368,117)
BIOL499A (11)
Chapter 18

# Chapter 18.pdf

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School
Department
Biology (Biological Sciences)
Course
BIOL499A
Professor
Blaine Mullins
Semester
Winter

Description
Chapter 18: Sampling Distribution Models In this chapter, we will talk about:  Sampling distribution of the sample mean,  Central limit theorem,  Sampling distribution of the sample proportion, when sample size is large. Definition: The probability distribution of a statistic is called the sampling distribution of the statistic or the distribution of the statistic. Consider a population with numbers 2, 3, and 4. Suppose n=2 numbers are selected with replacement. Let Y= selected number. Then the probability distribution of population is: y 2 3 4 Total P(y) 1/3 1/3 1/3 1 The possible samples and corresponding sample averages are: Sample (2,2) (2,3) (2,4) (3,2) (3,3) (3,4) (4,2) (4,3) (4,4) y 22 2 2  2.5 2 2 Therefore, the probability distribution of sample meanis: y 2 2.5 3 3.5 4 Total P() 1 Recall that the probability distribution of population was: y 2 3 4 Total P(y) 1/3 1/3 1/3 1 Therefore, the mean and standard deviation of Y are: 1 1 1  Y)2)(3(43p y 3 3 3 1 1 1 Var( )   (y  )2p( )(2 3) (3 3) (4 3) 3 3 3 121 1 ( 1) 2 (0)  (1) 333 3  S..) Y 2 3 In addition, the probability distribution of sample mean was: y 2 2.5 3 3.5 4 Total P() 1/9 2/9 3/9 2/9 1/9 1 Hence, the mean and standard deviation of sample mean are: 1 23 21 Y EY()  p y 2      4 3 9 99 99 2 2 2 2 1 2 3    Var(    ) p(y ) (2 3) (2.5 3) (3 3) 9 9 9 2 2 1 5 3) (4 3) 3 9 1 12 3    2 2 ( )  (0) 9 29 9 12 11 )2 2 (1) 29 93   ..)  1 Y 3 Theorem: The distribution of the sample mean, based on a random sample of size n from a population with mean  and standard deviatio , has a mean ofY EY   and a standard deviation of  Y D()Y (S)E Y n Example 18.1: An automatic grinding machine in an auto parts plant prepares axles with the target diameter   40.125millimeters (mm). The machine has some variability, so the standard deviation of the diameters is  0.002 mm. A sample of 4 axles is inspected each hour for process control purposes, and records are kept of the sample mean diameter. What will be the mean and standard deviation of the numbers recorded? Theorem: In a random sampling from a normal population with mean  and standard deviation  , the sample mean has the normal distribution with mean  and standard deviation / n . Example 18.2: Suppose the electric bill for the month of July for a single- family home in a city has a normal distribution with mean \$63 and standard deviation \$25, respectively. (a) What percentage of single-families wi ll receive an electric bill greater than \$70? (b) What percentage of all samples of 12 of single-families will have a mean of electric bills greater than \$70? Example 18.3: The weights of pears in an orchard are normally distributed with mean 0.32 pound and standard deviation 0.08 pound. (a) If one pear is selected at random, what is the probabili
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