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What makes it possible to interpret negative values of z

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Reference no: EM13861232

Question 1- What makes it possible to interpret negative values of z from a table that includes only positive values?

The fact that normal distributions are symmetrical

The fact that the z distribution has s = 1.0

The fact that there are no negative values of z

The fact that normal distributions all have the same value of the mean

Question 2- In a distribution for which the mean is 25 and the standard deviation is 5, what percentage of all scores occur at 30 or above?

15.87%

20%

34.13%

84.13%

Question 3- In a distribution for which the mean is 25 and the standard deviation is 5, what percentage of all scores occur between 20 and 30?

34.13%

68.26%

84.13%

92.39%

Question 4- Dividing the sample standard deviation by the square root of the number in the sample produces what value?

The parameter value for the standard error of the mean

The estimated population standard deviation

The estimated standard error of the mean

The estimated value of t

Question 5- The central limit theorem maintains ____________.

all data in a population are normal

any distribution can be turned into a z distribution

all values tend toward the center of the distribution

distributions based on samples tend to be normal

Question 6- Degrees of freedom refers to ____________.

how much flexibility the test has

how robust the test is when assumptions are violated

how many values can vary

how many different kinds of data can be analyzed

Question 7- The measure of within-group data variability in the independent t-test is ___________.
the estimated standard error of the mean

the population standard deviation

the estimated standard error of the difference

the population standard error of the mean

Question 8- A type I decision error occurs when someone erroneously determines that a result is not significant.
True

False
Question 9- The law of large numbers affects sampling procedure how?

Large numbers indicate relatively large sampling errors.

Large samples create distributions with small mean values.

Large samples tend to minimize sampling error.

Large numbers eliminate sampling error.

Question 10- If the z transformation is used with population data, M is replaced by what symbol?

s

m

z

X

Reference no: EM13861232

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