Data Set A: problems 1-3.
SAT GPA SES
SAT Pearson Correlation 1 0.778 0.232
Sig. (2-tailed) . 0.002 0.71
N 45200 145200 145200
GPA Pearson Correlation 0.778 1 0.424
Sig. (2-tailed) 0.002 . 0.081
N 145200 145200 145200
SES Pearson Correlation 0.232 0.424 1
Sig. (2-tailed) 0.71 0.081 .
N 145200 145200 145200
1. What is r xy if SAT = x and GPA = y ?
a. 0.778
b. 0.232
c. 0.002
d. 0.71
2. Which of the following correlations is significant (p < .05)?
a. SAT and GPA
b. GPA and SES
c. SAT and SES
d. All the above
3. What is the variance accounted for in SAT due to GPA?
a. 40%
b. 60.5%
c. 5.4%
d. Less than 1%
Research Design A: problems 4-8
A researcher was interested in the effects of sexual arousal on the ability to concentrate, and also wondered whether gender and age are important factors. The researcher had participants read passages that were low, medium, or high in sexual arousal content. The participants included both males and females and were divided into three age categories (18-24, 25-35, and 36-50 years). After reading the passage, concentration was measured by a proofreading task; the researcher measured the number of errors detected on the task.
4. What type of statistical procedure should the researcher use to answer her problems?
a. Standard Deviation
b. Independent Sample t test
c. Correlation
d. Factorial Anova
5. Which of the following are the Independent Variables:
a. Gender, Sexual Arousal, Age
b. Gender, Age, Concentration
c. Sexual Arousal, Age, Concentration
d. Sexual Arousal, Concentration, Gender
6. Which of the following is the Dependent Variable
a. Sexual Arousal
b. Gender
c. Concentration
d. Age
7. Which of the following represents the design?
a. 3 x 3 x 2
b. 3 x 3
c. 2 x 3
d. 2 x 3 x 1
8. Which of the following represents a possible interaction?
a. Gender affects concentration
b. Sexual arousal affects concentration
c. Sexual arousal affects concentration but only for males
d. Only high levels of sexual arousal affects concentration
Data Set 2: problems 9-10
The commissioner of the National Hockey League wants to know if offense (Goals_F), defense (Goals_A) and penalties (Pen_Min) predict winning (Tier).
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .581(a) .338 .314 .966
2 .728(b) .531 .496 .828
3 .734(c) .538 .485 .837
a Predictors: (Constant), Goals_F
b Predictors: (Constant), Goals_F, Goals_A
c Predictors: (Constant), Goals_F, Goals_A, Pen_Min
ANOVA(d)
Model Sum of Squares df Mean Square F Sig.
1 Regression 13.336 1 13.336 14.290 .001(a)
Residual 26.131 28 .933
Total 39.467 29
2 Regression 20.945 2 10.473 15.267 .000(b)
Residual 18.521 27 .686
Total 39.467 29
3 Regression 21.245 3 7.082 10.104 .000(c)
Residual 18.222 26 .701
Total 39.467 29
a Predictors: (Constant), Goals_F
b Predictors: (Constant), Goals_F, Goals_A
c Predictors: (Constant), Goals_F, Goals_A, Pen_Min
d Dependent Variable: Tier
9. If he wants to describe the maximum variability (Adjusted R squared) in winning then which one should he select?
a. Model 1
b. Model 2
c. Model 3
10. How much variability in winning is NOT describeed by the best model (coefficient of alienation converted to a percentage)?
a. 31.4 %
b. 49.6%
c. 68.6%
d. 50.4%