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Exam Code: 200-155
Exam Name: Introducing Cisco Data Center Technologies
Q&As: 85 Free Pass4itsure 200-155 Dumps Exam Questions and Answers:

QUESTION 51
An internal auditor for a large automotive parts retailer wishes to perform a risk analysis and wants to use an appropriate statistical tool to help identify stores that
would be out of line compared to the majority of stores. The most appropriate statistical tool to use is:
A. Lineartime series analysis.
B. Cross-sectional regression analysis.
C. Cross tabulations with chi-square analysis of significance

D. Time series multiple regression analysis to identify changes in individual stores over time.
Explanation
Explanation/Reference:
Explanation:
Time series data pertain to a given entity over a number of prior time periods. Cross-sectional data, however, pertain to different entities for a given time period or
at a given time. Thus, cross-sectional regression analysis is the most appropriate statistical tool because it compares attributes of all stores’ operating statistics at
one moment in time.
QUESTION 52
What coefficient of correlation results from the following data?
A. 0
B. -1
C. +1
D. Cannot be determined from the data given.
Explanation
Explanation/Reference:
Explanation:
The coefficient of correlation (in standard notation, r) measures the strength of the linear relationship. The magnitude of ris independent of the scales of
measurement of x and y. Its range is -1 to +1. A value of -1 indicates a perfectly inverse linear relationship between x and y. A value of zero indicates no linear
relationship between x and y. A value of 1 indicates a perfectly direct relationship between x and y As x increases by 1, y consistently decreases by 2. Hence, a
perfectly inverse relationship exists, and r must be equal to -1.
QUESTION 53
In regression analysis, which of the following correlation coefficients represents the strongest relationship between the independent and dependent variables?
A. 1.03
B. -.02
C. -.89
D. .75
Explanation
Explanation/Reference:
Explanation:
Because the range of values is between-1 and 1.-89 suggests a very strong inverse relationship between the independent and dependent variables. A value of –1
signifies a perfect inverse relationship, and a value of 1 signifies a perfect direct relationship.
QUESTION 54
The following data on variables x and y was collected from June to October.
The correlation coefficient between variables x and y is nearest to:
A. 1.0
B. -1.0
C. 0.5
D. 0.0
Explanation
Explanation/Reference:
Explanation:
The simplest way to solve this problem is to use a scatter diagram:

The data points appear to form a straight line with a negative slope. Thus,-1 is the best estimate of the coefficient of correlation.
QUESTION 55
The correlation coefficient that indicates the weakest linear association between two variables is:
A. -0.73
B. -0.11
C. 0.12
D. 0.35
Explanation
Explanation/Reference:
Explanation:
The correlation coefficient can vary from -1 to +1. A–1 relationship indicates a perfect inverse correlation, and a +1 relationship indicates a perfect direct
correlation. A zero correlation coefficient indicates no linear association between the variables. Thus, the correlation coefficient that is nearest to zero would
indicate the weakest linear association. Of the options given in the question, the correlation coefficient that is nearest to zero is -0.11.
QUESTION 56
Correlation is a term frequently used in conjunction with regression analysis and is measured by the value of the coefficient of correlation, r. The best explanation
of the value r is that it:
A. Is always positive.
B. Interprets variances in terms of the independent variable.
C. Ranges in size from negative infinity to positive infinity.
D. Is a measure of the relative relationship between two variables.
Explanation
Explanation/Reference:
Explanation:
The coefficient of correlation (r) measures the strength of the linear relationship between the dependent and independent variables. The magnitude of r is
independent of the scales of measurement of xand y. The coefficient has a range of-1 to +1. A value of zero indicates no linear relationship between the x and y
variables. A value of+1 indicates a perfectly direct relationship, and a value of –1 indicates a perfectly inverse relationship.
QUESTION 57
In preparing the annual profit plan for the coming year.
Based upon the data derived from the regression analysis, 420 maintenance hours in a month would mean the maintenance costs (rounded to the nearest US
dollar) would be budgeted at:
A. US\$3,780
B. US\$3,600
C. US\$3,790
D. US\$3,746
Explanation
Explanation/Reference:
Explanation:
Substituting the given data into the regression equation results in a budgeted cost of US \$3,746 (rounded to the nearest US dollar).
y=a + bx
= 684.65 + 7.2884(420)
= US \$3,746
QUESTION 58
In preparing the annual profit plan for the coming year.
What is the percentage of the total variance that can be explained by the regression equation?
A. 99.724%
B. 69.613%
C. 80.982%
D. 99.862%
Explanation
Explanation/Reference:
Explanation:
The coefficient of determination (r2) measures the percentage of the total variance in cost that can be explained by the regression equation. If the coefficient of
determination is .99724,99.724% of the variance is explained by the regression equation. Thus, the values in the regression equation explain virtually the entire
amount of total cost.
QUESTION 59
The internal auditor of a bank has developed a multiple regression model that has been used for a number of years to estimate the amount of interest income from
commercial loans. During the current year, the auditor applies the model and discovers that the r2 value has decreased dramatically, but the model otherwise
seems to be working.
Which of the following conclusions is justified by the change?
A. Changing to a cross-sectional regression analysis should cause r2to increase.
B. Regression analysis is no longer an appropriate technique to estimate interest income.
C. Some new factors not included in the model are causing interest income to change.
D. A linear regression analysis would increase the model’s reliability.