Answer to Question #320006 in Statistics and Probability for Mark

Question #320006

Solve the given problem. Dont forget to show your solutions.




1.AA pop fourulstion consist of the four numbers 1,2,4 and 5. List all the possible samples of sizes n=3 wich can be drawn with replacement from the population. Find the following : a. Population mean b. Population variance c. Population standard deviation d. Mean of the sampling distribution of sample means e. Variance of the sampling distribution of sample means f standard deviation of the sampling distribution of sample means

1
Expert's answer
2022-03-30T04:35:58-0400

The number of the samples is the number of combinations of size n from a set S of size m with replacement:

"N=\\begin{pmatrix}\n n+m-1\\\\\n n\n\\end{pmatrix}=\\cfrac{(n+m-1)!}{n!\\cdot (m-1)!}=\\\\\n=\\cfrac{(4+3-1)!}{3!\\cdot (4-1)!}=\\cfrac{6!}{3!\\cdot 3!}=\\cfrac{4\\cdot5\\cdot6}{2\\cdot3}=20."

All the possible samples of sizes n=3 wich can be drawn with replacement from the population:

"\\{ (1,1,1), (1,1,2),(1,1,4),(1,1,5),(1,2,2),\\\\\n(1,2,4),(1,2,5),(1,4,4),(1,4,5),(1,5,5),\\\\\n(2,2,2),(2,2,4),(2,2,5),(2,4,4),(2,4,5),\\\\\n(2,5,5),(4,4,4),(4,4,5),(4,5,5),(5,5,5)\\}."


a. Population mean:

"\\mu=\\sum x_i\\cdot P(x_i)=\\\\\n=1\\cdot\\cfrac{1}{4}+2\\cdot\\cfrac{1}{4}+4\\cdot\\cfrac{1}{4}+5\\cdot\\cfrac{1}{4}=3."


b. Population variance:

"\\sigma^2=\\sum(x_i-\\mu)^2\\cdot P(x_i),"

"X-\\mu=\\begin{Bmatrix}\n 1-3, 2-3, 4-3, 5-3\n\\end{Bmatrix}="

"=\\begin{Bmatrix}\n-2, -1, 1, 2\n\\end{Bmatrix},"

"\\sigma^2=(-2)^2\\cdot \\cfrac{1}{4}+(-1)^2\\cdot \\cfrac{1}{4}+1^2\\cdot \\cfrac{1}{4}+2^2\\cdot \\cfrac{1}{4}=2.5."


c. Population standard deviation:

"\\sigma=\\sqrt{2.5}=1.58."


For d., e., f., we'll use the properties of sampling distributions of sample means.


d. Mean of the sampling distribution of sample means:

"\\mu_{\\bar x} =\\mu=3."


e. Variance of the sampling distribution of sample means:

"\\sigma^2_{\\bar x}=\\cfrac{\\sigma^2}{n}=\\cfrac{2.5}{2}=1.25."


f. standard deviation of the sampling distribution of sample means:

"\\sigma_{\\bar x}=\\cfrac{\\sigma}{\\sqrt n}=\\cfrac{1.58}{\\sqrt 2}=1.12."


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