6540 Lecture Notes - Lecture 5: Unimodality, Randomized Experiment, Normal Distribution

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lavenderhornet902

30 Jul 2018

School

University of Canberra

Department

University of Canberra

Course

6540

Professor

Dr Sumaira Qureshi

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Document Summary

Based on mean of all possible collected samples - not original data set, mean of all possible samples. The central limit theorem (clt: the ce(cid:374)t(cid:396)al li(cid:373)it theo(cid:396)e(cid:373) The sampling distribution of any mean becomes nearly normal as the sample size grows. If you keep increasing size then the distribution will look closer to normal shape: re(cid:395)ui(cid:396)e(cid:373)e(cid:374)ts. Sample size condition: the sa(cid:373)pli(cid:374)g dist(cid:396)i(cid:271)utio(cid:374) of the (cid:373)ea(cid:374)s is (cid:272)lose to no(cid:396)(cid:373)al if either: The total compensation of the chief executive officers (ceos) of the 800 largest u. s. companies (the fortune 800) averaged (in thousands of dollars) 10,307. 31 with a standard deviation (also in ) of 17,964. 62. Here is a histogram of their annual compensations (in. The distribution of total compensation for the ceos of the 800 largest u. s. companies is unimodal, but skewed to the right with several large outliers. Explain how these histograms demonstrate what the central limit. Theorem says about the sampling distribution model for sample means.

Related Questions

Which of the following is true about sampling distribution?

Shape of the sampling distribution is always the same shape as the population distribution ,no matter what the sample size is
Sampling distributions are always nearly normal.
Sampling distributions of the mean is always right skewed since means cannot be smaller than 0
Sampling distribution get closer to normally as the sample size increases

violetox628

A probability distribution for all possible values of a sample statistic is known as
A.normal distribution
B. uniform distribution random c.sampling sampling distribution
D.sampling distribution

A function of a random variable which describes the probability that random variable is
Select one
A.Probability distribution function
B. Discrete random variable
C. Sampling distribution function
D. Continuous random variable

Consider the normal distribution the vanability is increased and the height of the curve is decreased for the
A. Larger value of standard deviation
B. Smaller value of mean
C. Smaller valie of stabdard deviation
D. Larger value of mean

The sampling destination of the sample mean is a distribution of
A. Sample statistic /estimator
B. individual population values
C. individual sample values
D. Population parameters

The sampling distribution of the mean is distribution of
A. Sample statistic
B. Population parameters
C. individual population values
D. individual sample values

periwinkleelephant946

Which of the following is not a conclusion of the central limit theorem?

A) The distribution of the sample means will, as the sample size increases, approach a normal distribution.

B) The distribution of the sample data will approach a normal distribution as the sample size increases.

C) The standard deviation of all sample means is the population standard deviation divided by the square root of the sample size.

D) The mean of all sample means is the population mean μ.

periwinkleelephant458

6540 Lecture Notes - Lecture 5: Unimodality, Randomized Experiment, Normal Distribution

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