STAT151 Lecture : Sampling Distribution Done in LaTeX

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silverseal471

22 Dec 2011

School

University of Alberta

Department

Statistics

Course

STAT151

Professor

Alireza Simchi

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STAT151 Full Course Notes

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Mean and standard deviation in sample distribution . 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. The probability distribution of a statistic is called the sampling distribution of the statistic or the distribution of the statistic. From a random sample, the mean x will vary but will centalize around the population mean . The distribution of the sample mean, based on a random sample of size n from a population with mean and standard deviation , has a mean and standard deviation of. X = e(cid:0) x(cid:1) = (the mean of sample averages equals the expectation of the sample average equals the mean of the population) X = s. d. (cid:0) x(cid:1) = s. e. (cid:0) x(cid:1) = n: s. e. is standard error. Sampling distribution: the distribution of possible sample means of a popultion.

Related Questions

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

6. As the test statistic becomes larger, the p-value

(a) gets smaller

(b) becomes larger

(d) becomes negative.

7. To describe the sampling distribution of the sample proportion, we use

(a) The sample proportion

(b) The population proportion

(d) The population mean

8. When small samples are used to estimate a population mean, in cases where the population standard deviation is unknown and the original distribution is normal:

(a) There always will be a large amount of sampling error.

(b) the t-distribution must be used to obtain the critical value.

(d) the z distribution must be used to obtain the critical value.

periwinkleyellowjacket100

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

STAT151 Lecture : Sampling Distribution Done in LaTeX

STAT151 Full Course Notes

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