1. Use the following data to answer
Questions 1a and 1b.

Total no. of problems correct (out of a possible 20)	Attitude toward test taking (out of a possible 100)
17	94
13	73
12	59
15	80
16	93
14	85
16	66
16	79
18	77
19	91

a. Use SPSS and paste your output in this worksheet.

b. Construct a scatterplot for these 10 values. Based
on the scatterplot, would you predict the correlation to be direct or indirect?
Why?

2. Rank the following correlation
coefficients on strength of their relationship (list the weakest first):

+.71

+.36

–.45

.47

–.62

3. Determine the correlation
between hours of studying and grade point average for these honor students. Why
is the correlation so low?

Hours of studying	GPA
23	3.95
12	3.90
15	4.00
14	3.76
16	3.97
21	3.89
14	3.66
11	3.91
18	3.80
9	3.89

4. Look at the following table.
What type of correlation coefficient would you use to examine the relationship
between sex (defined as male or female) and political affiliation?

How about family configuration (two-parent
or single-parent) and high school GPA? Explain why you selected the answers you
did.

Level of Measurement and Examples
Variable X	Variable Y	Type of correlation	Correlation being computed
Nominal (voting preference, such as Republican or Democrat)	Nominal (gender, such as male or female)	Phi coefficient	The correlation between voting preference and gender
Nominal (social class, such as high, medium, or low)	Ordinal (rank in high school graduating class)	Rank biserial coefficient	The correlation between social class and rank in high school
Nominal (family configuration, such as intact or single parent)	Interval (grade point average)	Point biserial	The correlation between family configuration and grade point average
Ordinal (height converted to rank)	Ordinal (weight converted to rank)	Spearman rank correlation coefficient	The correlation between height and weight
Interval (number of problems solved)	Interval (age in years)	Pearson product-moment correlation coefficient	The correlation between number of problems solved and the age in years

5. When two variables are
correlated (such as strength and running speed), it also means that they are
associated with one another. But if they are associated with one another, why
does one not cause the other?

6. Use Table B.4 below to determine
whether the correlations are significant and how you would interpret the
results.

a. The correlation between speed and strength for 20
women is .567. Test these results at the .01 level using a one-tailed test.

b. The correlation between the number correct on a
math test and the time it takes to complete the test is –.45. Test whether this
correlation is significant for 80 children at the .05 level of significance.
Choose either a one- or a two-tailed test and justify your choice.

c. The correlation between number of friends and grade
point average (GPA) for 50 adolescents is .37. Is this significant at the .05
level for a two-tailed test?

7. Use the following data set to answer
the questions. Do this manually.

a. Compute the correlation between age in months and
number of words known.

b. Test for the significance of the correlation at the
.05 level of significance.

c. Based on what you know about correlation
coefficients – interpret this
correlation.

Age in months	Number of words known
12	6
15	8
9	4
7	5
18	14
24	18
15	7
16	6
21	12
15	17

8. How does linear regression
differ from analysis of variance?

9. Betsy is interested in
predicting how many 75-year-olds will develop Alzheimer’s disease and is using level
of education and general physical health graded on a scale from 1 to 10 as
predictors. But she is interested in using other predictor variables as well.
Answer the following questions.

a. What criteria should she use in the selection of
other predictors? Why?

b. Name two other predictors that you think might be
related to the development of Alzheimer’s disease.

c. With the four predictor variables (level of education,
general physical health, and the two new ones that you name), draw out what the
model of the regression equation would look like.