Question 1

A symmetrical curve means that a symbolic measurement has been used.

Accepted Answer

A) True 
 B)False  False

Question 2

The approximate percentage of cases between the mean and negative one standard deviation (-1 s)in a normal curve is:

Accepted Answer

A) 34% 
B) 68% 
C) 95% 
D) 47.5% 
A) 34% 
B) 68% 
C) 95% 
D) 47.5% A

Question 3

Every type of curve actually represents a probability distribution.

Accepted Answer

A) True 
 B)False  True

Question 4

When the normal curve is paired with the standard deviation,a fixed set of point emerges that

Accepted Answer

A) provide us with probabilities of a score occurring 
B) can be used with Z-scores to determine the probability of kurtosis occurring 
C) provide a unique assessment of probability in comparison to uniform distributions 
D) all of the above 
A) provide us with probabilities of a score occurring 
B) can be used with Z-scores to determine the probability of kurtosis occurring 
C) provide a unique assessment of probability in comparison to uniform distributions 
D) all of the above

Question 5

At ±3 s more than 99% of all scores are accounted for.

Accepted Answer

A) True 
 B)False

Question 6

A mean of 10 and a standard deviation of 5 would result in a standard score of

Accepted Answer

A) 50 
B) 0.5 
C) 2.0 
D) there is not enough information to calculate a standard score 
A) 50 
B) 0.5 
C) 2.0 
D) there is not enough information to calculate a standard score

Question 7

Another name for a normal curve is the unique normal distribution.

Accepted Answer

A) True 
 B)False

Question 8

In a normal curve,the lowest probability of a score occurring would be found at the end of the curve.

Accepted Answer

A) True 
 B)False

Question 9

A normal distribution (the theoretical concept)will be:

Accepted Answer

A) 68 percent of all cases located at the ends of the curve 
B) asymptotic 
C) symmetrical 
D) b and c above 
A) 68 percent of all cases located at the ends of the curve 
B) asymptotic 
C) symmetrical 
D) b and c above

Question 10

If a variable is normally distributed,it would have approximately:

Accepted Answer

A) 95 percent of the cases within 1 standard deviation around the mean 
B) 47.5 percent of the cases between the mean and +2 standard deviations 
C) 68 percent of the cases within 2 standard deviations around the mean 
D) 68 percent of the cases between the mean and -1 standard deviation 
A) 95 percent of the cases within 1 standard deviation around the mean 
B) 47.5 percent of the cases between the mean and +2 standard deviations 
C) 68 percent of the cases within 2 standard deviations around the mean 
D) 68 percent of the cases between the mean and -1 standard deviation

Question 11

As long as you have a large enough sample,a random sampling process is not necessary in order to assume a normal curve.

Accepted Answer

A) True 
 B)False

Question 12

Scores between the ends and the peak of a normal curve have an increasing probability of occurring as the point on the curve moves toward the peak.

Accepted Answer

A) True 
 B)False

Question 13

A uniform distribution has a uniformly increasing number of scores throughout the curve.

Accepted Answer

A) True 
 B)False

Question 14

If the mean of a distribution is assigned the value of zero,then all scores above the mean would be negative numbers.

Accepted Answer

A) True 
 B)False

Question 15

Which of the following represent the concept of skewness?

Accepted Answer

A) the degree of peakedness of a curve 
B) tails that never touch the baseline of a distribution 
C) a lack of symmetry in the curve 
D) a uniform area under the curve within ± 1 standard deviation 
A) the degree of peakedness of a curve 
B) tails that never touch the baseline of a distribution 
C) a lack of symmetry in the curve 
D) a uniform area under the curve within ± 1 standard deviation

Question 16

If a curve is asymptotic,it means that

Accepted Answer

A) the middle of the curve will never touch the baseline 
B) the ends of the curve will never touch the baseline 
C) both ends of the curve are identical 
D) the scores in the curve increase until they reach infinity 
A) the middle of the curve will never touch the baseline 
B) the ends of the curve will never touch the baseline 
C) both ends of the curve are identical 
D) the scores in the curve increase until they reach infinity

Question 17

The peak of a normal curve has

Accepted Answer

A) some of the lowest scores in the distribution 
B) the highest probability of a score occurring 
C) a relationship to the baseline such that the square of the peak is proportional to the cube of the tails 
D) no relationship to the baseline 
A) some of the lowest scores in the distribution 
B) the highest probability of a score occurring 
C) a relationship to the baseline such that the square of the peak is proportional to the cube of the tails 
D) no relationship to the baseline

Question 18

One way of determining that a curve may differ from the normal curve is called

Accepted Answer

A) kurtosis 
B) a differencing technique 
C) nonnormal variance 
D) the coefficient of normality 
A) kurtosis 
B) a differencing technique 
C) nonnormal variance 
D) the coefficient of normality

Question 19

Two reasons we can be assured that a normal distribution is a reasonable assumption for many variables are:

Accepted Answer

A) the Centrality Theorem and Thompson's Limited Tails Theorem 
B) the Inequality Theorem and the Reasonably Normal Theorem 
C) Tchebychev's Inequality Theorem and the Central Limit Theorem 
D) none of the above 
A) the Centrality Theorem and Thompson's Limited Tails Theorem 
B) the Inequality Theorem and the Reasonably Normal Theorem 
C) Tchebychev's Inequality Theorem and the Central Limit Theorem 
D) none of the above

Question 20

Z-scores are used to describe the relative position of a mean.

Accepted Answer

A) True 
 B)False

Exam 6: Curves and Distributions

A symmetrical curve means that a symbolic measurement has been used.

The approximate percentage of cases between the mean and negative one standard deviation (-1 s)in a normal curve is:

Every type of curve actually represents a probability distribution.

When the normal curve is paired with the standard deviation,a fixed set of point emerges that

At ±3 s more than 99% of all scores are accounted for.

A mean of 10 and a standard deviation of 5 would result in a standard score of

Another name for a normal curve is the unique normal distribution.

In a normal curve,the lowest probability of a score occurring would be found at the end of the curve.

A normal distribution (the theoretical concept)will be:

If a variable is normally distributed,it would have approximately:

As long as you have a large enough sample,a random sampling process is not necessary in order to assume a normal curve.

Scores between the ends and the peak of a normal curve have an increasing probability of occurring as the point on the curve moves toward the peak.

A uniform distribution has a uniformly increasing number of scores throughout the curve.

If the mean of a distribution is assigned the value of zero,then all scores above the mean would be negative numbers.

Which of the following represent the concept of skewness?

If a curve is asymptotic,it means that

The peak of a normal curve has

One way of determining that a curve may differ from the normal curve is called

Two reasons we can be assured that a normal distribution is a reasonable assumption for many variables are:

Z-scores are used to describe the relative position of a mean.

Introduction to Statistics

Levels of Measurement

Graphical Statistics

Measures of Central Tendency

Measures of Dispersion

Frequency Distributions

Elementary Relationships: Crosstabulation Tables

Hypotheses and Sampling Distributions

Statistical Significance

Testing for Significance: the Chi-Square Test

Testing for Significance in Two Groups: the T-Test

Testing for Significance in Multiple Groups: the Analysis of Variance Statistic

The Concept of Association

Testing for Association: Phi

Testing for Association: Pearsons R and Regression

Doing Real Research: Elementary Multivariate Relationships

Box Plots

Skewness and Kurtosis

Ordinal-Level Tests of Significance

Multiple Comparison Tests

Nominal Level Tests of Association

Ordinal Level Tests of Association

Addendum Probability

Filters