The mean rate of return on portfolio A (12 stocks) was
calculated to be 11% with a standard deviation of 3.4%; the mean rate of return
on portfolio b (10 stocks) was determined to be 12.8% with a standard deviation
of 4%. At the .05 significance level,:
a. F = 3.02,
we can conclude that there is more variation in portfolio B’s performance.
b. F = 1.38,
we cannot conclude that there is more variation in portfolio B’s performance.
c. F =
3.02, we cannot conclude that there is more variation in portfolio B’s
performance.
d. F = 0.62,
we cannot conclude that there is more variation in portfolio B’s performance.
To conduct an experiment comparing more than two treatments:
a. We should
use separate t tests because there is a smaller likelihood of computational
error,
b. We should
use ANOVA to reduce the possibility of a type II error.
c. We
should use ANOVA to reduce the possibility of a type I error.
d. None of
the above.
Three different fertilizers were applied to a field in 7
controlled applications. In computing F, there should be _______degrees of
freedom in the numerator:
a. 1
b. 2
c. 6
d. 12
Answer questions the next four questions using the following
information:
Test the hypothesis that the treatment means for samples
given below are equal. Use the .01 significance level.
Treatment 1 Treatment 2 Treatment 3
22 34 13
20 31 10
21 25 14
18 25 11
19 32
30
The decision rule is:
a. Reject
the null hypothesis if F > 5.42
b. Reject
the null hypothesis if F > 6.93
c. Accept
the null hypothesis if F > 26.9
d. Reject
the null hypothesis if F > 99.4
SS total is:
a. -4,132.8
b. 755.83
c. 845.33
d. 4,132.8
MSE is:
a. 7.46
b. 377.92
c. 422.66
d. 2,066.4
The F statistic =
a. 1.00
b. 7.46
c. 50.67
d. 54.5
In an experiment in which two of four similar units are each
compressed at three different levels (light, medium, heavy) to determine
resilience, the number of degrees of freedom (numerator, denominator) is:
a. (2,3)
b. (2,6)
c. (1,4)
d. (1,3)
The following data apply to a two-factor ANOVA:
Treatment
Source 1 2 3
A 12 14 8
B 9 11 9
C 7 8 8
SST for the data =
a. 1.36
b. 10.89
c. 31.11
d. 42.22
SSB for the data =
a. 20.22
b. 31.11
c. 53.33
d. 63.11
The regression equation:
a. can be
adjusted to accommodate any number of independent variables.
b. Indicates
an inverse relationship between variables when a “b” coefficient has
a negative sign.
c. Should
only be used to predict values for the dependent variable that are inside the
range of the sample values.
d. Both a
and b.
e. All of
the above
The measure of explained variation is the:
a. coefficient
of multiple determination.
b. Coefficient
of multiple non-determination.
c. Regression
coefficient.
d. Correlation
matrix
An analyst determines the relationship between the time
taken to perform a computer-triggered production function (Y), required memory
to run the function (000 bytes) and amount of input (000 lines of data). The
regression equation representing this relationship is determined to be:
Y1 = 11.43 + 1.26X 1 + 3.11X 2
For required memory of 25,000 bytes of data, and input of
8,000 lines of data, the estimated time to run the function is:
a. 14.233
minutes
b. 67.81
minutes
c. 73.69
minutes
d. 129.43
minutes
e. Not calculable
without additional data.
For a run that required a memory of 15,000 bytes and input
of 8,000 lines the time of the run is 54 minutes; this is:
a. 13
minutes less than expected.
b. 1.2
minutes less than expected.
c. 1.2
minutes more than expected.
d. Not
calculable without additional data.
Please answer the next two questions using the following
information:
A regression analysis yielded the following output:
Constant 23.00371
Std Error of Y estimate 2.91933
R2 0.91404
No. of Observations 21
Degrees of Freedom 15
A B C D E
X coefficients -0.031 0.381 1.452 -0.089 3.554
Std Err of Coef. 0.183 0.158 0.387 0.541 0.833
The multiple regression equation is;
a. Y1 =
-0.31A + 0.381B + 1.452C – 0.089D + 3.554E
b. Y1 = 23.004
– 0.31A + 0.381B + 1.452C – 0.089D + 3.554E
c. Y1 =
-23.004 + 0.31A – 0.381B – 1.452C + 0.089D – 3.554E
d. Y1 =
23.004 – 0.183A + 0.158B + 0.387C – 0.541D + 0.833E
The variable with the greatest impact on Y is:
a. A
b. B
c. C
d. D
e. E
The total of the square of each residual is the basis for
the calculation of:
a. homoscedasticity
b. the
multiple coefficient of correlation
c. The
multiple standard error of the estimate
d. Multicollinearity
When the independent variables of a regression are highly
correlated the results will exhibit:
a. homoscedasticity
b. multiple
correlation
c. autocorrelation
d. multicollinearity
The test investigates whether all of the independent
variable have a zero net regression coefficient.
a. multicollinearity
b. autocorrelation
c. global
d. Pearson
Answer the next five question based on the following
information:
For a sample size of 15 observations, the regression
equation presented in question #14 resulted in total variation is 1,985.7332,
and unexplained variation is 158.7286.
The multiple standard error of the estimate is:
a. 1.05
b. 3.49
c. 3.64
d. 3.80
The mean square of the regression is
a. 13.23
b. 79.36
c. 152.25
d. 913.5
e. 992.87
At a significance level of a = .01, the computed value of F
indicates that:
a. the
independent variables are useful in predicting the value of the dependent
variable.
b. The
independent variables are not useful in predicting the value of the dependent
variable
Based on the analysis of variance we would fail to reject
H0.
a. True
b. False
Using a significance level of 0.05, the null hypothesis for
both the treatments and the blocks will be rejected.
a. True
b. False
