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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%

Statistics and Probability, Statistics

  • Category:- Statistics and Probability
  • Reference No.:- M91452

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