statistics for the behavioral sciences second edition chapter 6: the normal curve, standardization,...
TRANSCRIPT
Statistics for the Behavioral SciencesSecond Edition
Chapter 6:
The Normal Curve, Standardization, and z Scores
iClicker Questions
Copyright © 2012 by Worth Publishers
Susan A. Nolan and Thomas E. Heinzen
1. All of the following are true of the normal curve EXCEPT:
a) it is bell-shaped.
b) it is unimodal.
c) it has an inverted U shape.
d) it is symmetric.
Chapter 6
Chapter 6 (Answer)
1. All of the following are true of the normal curve EXCEPT:
a) it is bell-shaped.
b) it is unimodal.
c) it has an inverted U shape.
d) it is symmetric.
Chapter 6 2. A normal distribution of scores will more closely
resemble a normal curve as:
a) the sample size increases.
b) the sample size decreases.
c) more outliers are added to the sample.
d) scores are converted to z-scores.
Chapter 6 (Answer)
2. A normal distribution of scores will more closely resemble a normal curve as:
a) the sample size increases.
b) the sample size decreases.
c) more outliers are added to the sample.
d) scores are converted to z-scores.
3. A z score is defined as the:
a) mean score.
b) square of the mean score.
c) square root of the mean score divided by the mean.
d) number of standard deviations a particular score is from the mean.
Chapter 6
3. A z score is defined as the:
a) mean score.
b) square of the mean score.
c) square root of the mean score divided by the mean.
d) number of standard deviations a particular score is from the mean.
Chapter 6 (Answer)
Chapter 64. When transforming raw scores into z scores, the formula is: a) (μ – X)
Z= ___________
Σ
b) (X – μ)
Z= __________
σ
c) (∑ – X)
Z= __________
Σ
d) (X – σ)
Z= _________
S
Chapter 6 (Answer)4. When transforming raw scores into z scores, the formula is: a) (μ – X)
Z= ___________
Σ
b) (X – μ)
Z= __________
σ
c) (∑ – X)
Z= __________
Σ
d) (X – σ)
Z= _________
S
Chapter 6
5. Matthew recently took an IQ test in which he scored an IQ of 120. If the population’s mean IQ is 100 with a standard deviation of 15, what is Matthew’s z score?
a) -2.6
b) 1.6
c) -2.3
d) 1.3
5. Matthew recently took an IQ test in which he scored an IQ of 120. If the population’s mean IQ is 100 with a standard deviation of 15, what is Matthew’s z score?
a) -2.6
b) 1.6
c) -2.3
d) 1.3
Chapter 6 (Answer)
Chapter 6
7. A normal distribution of standardized scores is called the:
a) standard normal distribution.
b) null distribution.
c) z distribution.
d) sample distribution.
Chapter 6 (Answer)
7. A normal distribution of standardized scores is called the:
a) standard normal distribution.
b) null distribution.
c) z distribution.
d) sample distribution.
8. The assertion that a distribution of sample means approaches a normal curve as sample size increases is called:
a) Bayes theorem.
b) the normal curve.
c) De Moivre’s theorem.
d) the central limit theorem.
Chapter 6
8. The assertion that a distribution of sample means approaches a normal curve as sample size increases is called:
a) Bayes theorem.
b) the normal curve.
c) De Moivre’s theorem.
d) the central limit theorem.
Chapter 6 (Answer)
Chapter 6 9. How is a distribution of means different from a distribution
of raw scores?
a) The distribution of means is more tightly packed.
b) The distribution of means has a greater standard deviation.
c) The distribution of means cannot be plotted on a graph.
d) All of the above are true.
Chapter 6 (Answer)
9. How is a distribution of means different from a distribution of raw scores?
a) The distribution of means is more tightly packed.
b) The distribution of means has a greater standard deviation.
c) The distribution of means cannot be plotted on a graph.
d) All of the above are true.
Chapter 6 10. The standard deviation of a distribution of means is
called the:
a) standard score.b) standard error.c) central limit theorem.d) normal curve.
Chapter 6 (Answer)
10. The standard deviation of a distribution of means is called the:
a) standard score.b) standard error.c) central limit theorem.d) normal curve.
Chapter 6
11. Statisticians can use principles based on the normal curve to:
a) catch cheaters.
b) encourage people to conform to expected behavior.
c) remove unwanted scores from the data set.
d) detect confounds in an experiment.