Chapter 14 Making Use of Associations Tests
1) What type of analysis would be used to answer a question such as "Which customer
demographic is most strongly related to product purchase/nonpurchase"?
A) association or relationship analysis
B) predictive analysis
C) predictive or relationship analysis
D) analysis of variance
E) canine/feline regression analysis
Answer: A
2) Pontiac wants to know what types of persons respond favorably to proposed style changes
in the Firebird. Frito-Lay wants to know what kinds of people buy from the Frito-Lay line.
These are questions that may be answered through:
A) relationship analysis.
B) chi-square analysis.
C) associative analysis.
D) analysis of variance.
E) regression analysis.
Answer: C
3) Associative analyses determine whether stable relationships exist between:
A) costs and expenses.
B) two variables.
C) 12 or more variables.
D) marketing and sales.
E) statistics and results.
Answer: B
4) When a scale has "labels" as opposed to "levels," we can normally assume the level of
measurement is:
A) nominal or categorical.
B) ratio.
C) interval or ordinal.
D) interval or ratio.
E) metric.
Answer: A
5) When a consistent and systematic relationship is found between two variables, the linkage
is:
A) causal.
B) statistical.
C) managerially significant.
D) important.
E) relevant.
Answer: B
6) You do not have a "relationship" that links the labels (or levels) for two variables unless
the relationship is:
A) causal and consistent.
B) consistent and systematic.
C) systematic and causal.
D) systematic and important.
E) consistent and important.
Answer: B
7) Associative analysis procedures are useful because they determine if there is a consistent
and systematic relationship between the presence (label) or amount of one variable and the:
A) nonpresence of a regressive relationship.
B) presence (label) or amount of another variable.
C) presence (label) or amount of the same variable.
D) covariance of the other variable.
E) presence of unobservable variables.
Answer: B
8) The four basic types of relationships between two variables are:
A) a non-monotonic, duotonic, linear, and curvilinear.
B) nonmonotonic, duotonic, sublinear, and curvilinear.
C) nonmonotonic, monotonic, linear, and curvilinear.
D) causal, consistent, systematic, and linear.
E) duotonic, linear, sublinear, and alinear.
Answer: C
9) A relationship in which the presence (or absence) of one variable is systematically
associated with the presence (or absence) of another is:
A) causal relationship.
B) linear relationship.
C) monotonic relationship.
D) nonmonotonic relationship.
E) alinear relationship.
Answer: D
10) We know that McDonald's customers drink coffee for breakfast and soft drinks at lunch.
This is an example of what type of relationship?
A) causal relationship
B) linear relationship
C) monotonic relationship
D) nonmonotonic relationship
E) curvilinear relationship
Answer: D
11) Which type of relationship would you have when you have a general direction assigned
to the relationship; that is, as one variable increases, the other variable may increase (or
decrease)?
A) causal relationship
B) duotonic relationship
C) monotonic relationship
D) nonmonotonic relationship
E) curvilinear relationship
Answer: C
12) The owner of a shoe store knows that as children increase in age, their shoe size tends to
get larger. This is an example of what type of relationship?
A) causal relationship
B) duotonic relationship
C) monotonic relationship
D) nonmonotonic relationship
E) linear relationship
Answer: C
13) Which type of relationship is a "straight-line" relationship between two variables and for
which allows us to know the knowledge of one variable if we have knowledge of the other?
A) causal relationship
B) linear relationship
C) monotonic relationship
D) nonmonotonic relationship
E) curvilinear relationship
Answer: B
14) Which type of relationship is described by the formula: y = a + bx?
A) causal relationship
B) linear relationship
C) monotonic relationship
D) nonmonotonic relationship
E) curvilinear relationship
Answer: B
15) Which type of relationship is described by relationships that may be S-shaped or Jshaped?
A) causal relationship
B) linear relationship
C) curvilinear relationship
D) nonmonotonic relationship
E) irregular relationship
Answer: C
16) The product life cycle curve that describes the sales pattern of a new product over time
(growing slowly during its introduction and then spurting upward rapidly during its growth
stage and finally plateauing or slowing down considerably as the market becomes saturated)
is an example of which type of relationship?
A) causal relationship
B) linear relationship
C) nonmonotonic relationship
D) monotonic relationship
E) curvilinear relationship
Answer: E
17) Which of the following were discussed by the authors as being ways that we may
characterize relationships?
A) presence, direction, concreteness
B) direction, strength, concreteness
C) presence, direction, strength
D) presence, strength, concreteness
E) presence, pattern, concreteness
Answer: C
18) Strength of association might be described as:
A) strong.
B) weak.
C) moderate.
D) all of the above
E) none of the above
Answer: D
19) Which of the following refers to the finding that a systematic relationship exists between
the two variables of interest in the population?
A) presence
B) direction
C) strength
D) pattern
E) concreteness
Answer: A
20) Which of the following is used when describing the general pattern of nonmonotonic
relationships?
A) presence
B) direction
C) strength
D) pattern
E) concreteness
Answer: D
21) What is used to determine whether a nonmonotonic relationship exists between two
nominal-scaled variables?
A) tabulation analysis and t tests
B) cross-tabulation and chi-square tests
C) cross-tabulation and t tests
D) tabulation analysis and chi-square tests
E) only t tests
Answer: B
22) A cross-tabulation table is sometimes referred to as a:
A) tabular table.
B) nonmonotonic display table.
C) r × c table.
D) t × n table.
E) c × x table.
Answer: C
23) The intersection of a row and column in a cross-tabulation table is called:
A) a cross-tabulation cell.
B) a dangerous intersection.
C) a chi-square.
D) a cross-cell interaction.
E) a row box.
Answer: A
24) The chi-square test is useful for determining:
A) if a nonmonotonic relationship exists between two nominal-scaled variables.
B) if a monotonic relationship exists between two nominal-scaled variables.
C) if a nonmonotonic relationship exists between two interval-scaled variables.
D) if a duotonic relationship exists between two variables.
E) if a duotonic relationship exists between three variables.
Answer: A
25) Which of the following is NOT a number that can be found in each cross-classification
table cell?
A) frequency
B) raw percentage
C) column percentage
D) overall percentage
E) row percentage
Answer: D
26) The logic of the chi-square test would argue that, for a significant relationship to exist:
A) there should be large differences between the observed and expected frequencies.
B) there should be few differences between the observed and expected frequencies.
C) there should be no differences between the observed and expected frequencies.
D) there should be negative differences between the observed and expected frequencies.
E) there should be only one difference between the observed and expected frequencies.
Answer: A
27) In chi-square analysis, the greater the number of cells, the larger the degrees of freedom.
The greater the number of cells, the more opportunity exists to calculate a large chi-square
value. In other words, the chi-square value can be "inflated" not due to a real association but
simply due to the fact that there are more cells in the analysis. This is why degrees of
freedom are used to:
A) determine how many cells you should analyze.
B) determine whether or not the computed chi-square value should be used for a post hoc
test.
C) determine whether or not the chi-square value has a probability high enough to support, or
not support, the null hypothesis.
D) all of the above
E) None of the above; degrees of freedom is not calculated with the chi-square test.
Answer: C
28) If we run the chi-square test and we get a .02 level of significance to support the null
hypothesis, this means:
A) there is adequate support for the null hypothesis.
B) there is no association between the two nominally scaled variables.
C) there is only a 2 percent chance that the two nominally scaled variables are systematically
related.
D) there is a significant association between the two nominally scaled variables, and this
information alone is adequate to explain the nature of the association.
E) there is a significant association between the two nominally scaled variables, but this
information alone is a "flag" that we need to look more closely at the variables to discern the
nature of the relationship.
Answer: E
29) In order to run a chi-square test using SPSS, the proper command sequence is:
A) ANALYZE; CHI-SQUARE; GO.
B) ANALYZE; SUMMARIZE; CROSSTABS; CHI-SQUARE.
C) ASSOCIATIONS; NONMONO; CHI-SQUARE.
D) ANALYZE; DESCRIPTIVE STATISTICS; CROSSTABS.
E) ASSOCIATIONS; STATISTICS; CROSSTABS.
Answer: D
30) In the textbook you were given an example of running a chi-square test using SPSS. The
output shows a "Pearson Chi-Square" value of 23.272. This value alone means:
A) there is a significant difference.
B) there is no significant difference.
C) the difference is associative.
D) the difference must immediately be rounded up.
E) none of the above
Answer: E
31) In the textbook you were given an example of running a chi-square test using SPSS. The
output shows a "Pearson Chi-Square" value of 23.272 and df =7. This information alone
means:
A) there is a significant difference.
B) there is no significant difference.
C) the difference is associative.
D) the test was run incorrectly.
E) none of the above
Answer: E
32) In the textbook you were given an example of running a chi-square test using SPSS. The
output shows a "Pearson Chi-Square" value of 23.272, df = 7 and the Asymp. Sig. = .002.
This means:
A) there is a significant association.
B) there is no significant association.
C) the difference is associative.
D) the test was run incorrectly.
E) none of the above
Answer: A
33) If you were to find a significant association between two nominally scaled variables, a
good way to present the findings in your cross-tabulation table would be to use:
A) a p value for each nonmonotonic relationship found.
B) a Sig. value for each nonmonotonic relationship found.
C) graphical presentations.
D) numerical presentations that clearly indicate the direction and strength of the relationship.
E) numerical presentations that clearly indicate the linear values in the relationship.
Answer: C
34) A correlation coefficient is an index number constrained to fall between the range of:
A) 0 and 1.00.
B) 0 and 100.
C) -1.00 and +1.00.
D) -1.00 and 0.
E) -100 and 0.
Answer: C
35) Assume that a researcher and client determine that a p value of .05 or less determines
significance. Listed below are several correlation coefficients and their respective
significance levels. Which correlation coefficient demonstrates an association not likely due
to chance; that is, is it significant?
A) .22, .06
B) .75, .05
C) .32, .15
D) .76, .95
E) .05, 1.00
Answer: B
36) Which of the following correlation coefficients would indicate a "moderate" association?
A) .85
B) -.85
C) -.75
D) .35
E) .60
Answer: C
37) Let's assume we find in a study that the correlation coefficient between number of years
of education and cigarette smoking is -.89. This means that as education level increases:
A) smoking tends to increase.
B) smoking tends to decrease.
C) smoking changes 89 percent.
D) smoking is nonexistent.
E) only 89 out of every 100 people in the study would not smoke.
Answer: B
38) If we were to graph two variables, let's say, height (in inches) and GPA, and the graph
showed points scattered about in a formless shape, we could say there is:
A) likely no significant relationship between height and GPA.
B) likely a positive relationship between height and GPA.
C) likely a negative relationship between height and GPA.
D) a need to re-graph the data.
E) likely a curvilinear relationship between height and GPA.
Answer: A
39) The Pearson product moment correlation measures the linear relationship between two:
A) nominal- or ordinal-scaled variables.
B) nominal- or interval-scaled variables.
C) interval- or ratio-scaled variables.
D) nominal- or ratio-scaled variables.
E) ordinal- or interval-scaled variables.
Answer: C
40) If you wished to compute a Pearson product moment correlation coefficient using SPSS,
which command sequence would you use?
A) CORRELATE; PEARSON; GO
B) ANALYZE; CORRELATE; PEARSON
C) ANALYZE; CORRELATE; PEARSON; R
D) ANALYZE; CORRELATE; PEARSON; GO
E) ANALYZE; CORRELATE; BIVARIATE
Answer: E
41) Let's assume we use SPSS to run a correlation analysis, and we get a Pearson correlation
coefficient of .941 and a Sig. value of .000. These figures mean:
A) there is 94.1 percent probability for supporting the null hypothesis.
B) there is little or no probability for supporting the null hypothesis.
C) there is little or no probability for supporting the null hypothesis, and there is a strong
association.
D) there is little or no probability for supporting the null hypothesis, and there is a strong,
positive association.
E) that more information is needed.
Answer: D
42) The manager of the city's professional hockey team conducted a large survey. He wanted
to know if there was an association between fans being "season ticket holders" versus
"nonseason ticket holders" and whether they "bought" versus "didn't buy" hockey team
merchandise at the game. Because his survey included these measurements, he used SPSS to
run a Pearson product moment correlation coefficient that turned out to be .88 with a Sig.
value of .001. This meant that:
A) there is no significant relationship.
B) there is a significant, strong, positive relationship.
C) there is a significant, strong, negative relationship.
D) there is a significant, moderate, positive relationship.
E) None of the above; Pearson's product moment correlation is not the appropriate measure
of association here because both variables are nominally scaled.
Answer: D
43) Which of the following are caveats of correlation?
A) Its use is limited to metric variables (interval or ratio scaled).
B) It examines the relationship between only two variables.
C) Do not assume cause and effect.
D) It is limited to linear relationships.
E) all of the above
Answer: E
44) A researcher runs a correlation analysis between two variables that she is certain are
associated but the analysis indicates the two variables are not associated. The researcher may
then want to:
A) run another association test and add three variables.
B) adopt a lower standard for determining significance, that is, a p value of .20.
C) do nothing; if the association is deemed insignificant it is inappropriate to run further
analyses.
D) run a scatter plot in search of a curvilinear relationship.
E) run a scatter plot in search of a linear relationship.
Answer: D
45) Associative analyses determine whether stable relationships exist between two variables.
Answer: True
46) When the descriptors on a scale measure levels or amounts (i.e., the level of sales
dollars), the scale is metric (interval or ratio).
Answer: True
47) When the descriptors on a scale measure levels or amounts (i.e., the level of sales
dollars), the scale is categorical (nominal or ordinal).
Answer: True
48) Significant association relationships are always causal.
Answer: False
49) Associative analysis procedures identify if there are consistent and systematic
relationships between variables.
Answer: True
50) The key to establishing a nonmonotonic relationship between two variables is
determining if one is present (or absent) when the other is present (or absent).
Answer: True
51) The fact that most tourists at sunny beach resorts use sun block is a curvilinear
relationship.
Answer: False
52) Coffee orders at a restaurant are present at breakfast and soft drink orders are present at
lunch. This is an example of a monotonic relationship.
Answer: False
53) The key to establishing a monotonic relationship between two variables is determining if
there is a general direction (increasing or decreasing) between the two variables.
Answer: True
54) If we determine a precise linear relationship between two variables, then by knowing the
value of one variable we should be able to predict the other variable.
Answer: True
55) Presence refers to the strength of a relationship between three or more variables.
Answer: False
56) The characteristic of direction, in describing the relationship between two variables, is
applicable only when the relationship is either monotonic or linear.
Answer: True
57) Both low-level and high-level scales can incorporate very precise linear relationships.
Answer: False
58) We would use cross-tabulation if we wanted to visualize the nonmonotonic relationship
between two nominal scaled variables.
Answer: True
59) Cross-tabulation tables allow us to look at two variables simultaneously and are arranged
in a row and column format.
Answer: True
60) Frequencies tables, part of cross-tabulation, contain raw data.
Answer: True
61) With the information used to construct a cross-tabulation table, a column percentages
table can also be constructed, but a row percentages table cannot.
Answer: False
62) Row cell percentage is calculated by dividing a cell frequency by the cell row total.
Answer: True
63) Chi-square analysis always begins with the assumption that no association exists between
the two nominal scaled variables under analysis.
Answer: True
64) The chi-square analysis would be appropriate to determine if there is an association
between the number of dollars spent on books and the number of years of education.
Answer: False
65) Generally speaking, the chi-square value is calculated by dividing the sum of the squared
differences between observed and expected frequencies by the expected frequencies.
Answer: True
66) In the chi-square analysis, the greater the differences between the observed frequencies
and the expected frequencies, the less likely it is that there will be a statistically significant
relationship.
Answer: False
67) Chi-square analysis is a form of a "goodness of fit" test.
Answer: True
68) The degrees of freedom in chi-square are calculated by multiplying the rows, minus one
times the columns, minus one.
Answer: True
69) It is more difficult for a chi-square value with high degrees of freedom (i.e., there are
more cells) to achieve "significance" than for a chi-square value with fewer degrees of
freedom (i.e., there are fewer cells).
Answer: True
70) The manager of the city's professional hockey team conducted a large survey. He wanted
to know if there was an association between fans being "season ticket holders" versus "nonseason ticket holders" and whether they "bought" versus "didn't buy" hockey team
merchandise at the game. Because his survey included these measurements, he used SPSS to
run a cross-tabulation analysis and selected the chi-square test to test for the significance of
any association. The Pearson Chi-Square turned out to be 81.6 with an Asymp. Sig. value of
.001. This meant there is a significant relationship.
Answer: True
71) To run chi-square analysis in SPSS, you should go to the Crosstabs command, click
Statistics, then select Chi-Square.
Answer: True
72) In chi-square, the null hypothesis states that there is no association. When the "Asymp.
Sig." on the SPSS output is low, this means there is low support for the null hypothesis.
Answer: True
73) When we present findings on cross-tabulations with chi-square analysis, we cannot use
the characteristics of direction or strength because we are dealing with categorical, or
nominal scaled, variables.
Answer: True
74) When we present findings on cross-tabulations with chi-square analysis, graphical
presentations such as bar or stacked bar charts are often useful.
Answer: True
75) A correlation coefficient is an index number constrained to fall between the range of -1.0
and +1.0 that communicates both the strength and the direction of association between three
or more variables.
Answer: False
76) Covariation is defined as the amount of change in one variable systematically associated
with a change in another variable.
Answer: True
77) To use a correlation, you must first establish that it is statistically significant from 1.
Answer: False
78) Regardless of the size of the correlation value, if it is not significant, it is meaningless.
Answer: True
79) When it comes to determining the statistical significance of the correlation coefficient,
there are rules of thumb. For example, .81 to 1.00 is considered to be "strong."
Answer: True
80) There is no connection between scatter diagrams and correlation coefficients.
Answer: False
81) If you plotted data between two variables and the points all fell precisely in a straight line
that was sloping upward to the right, your correlation coefficient would be equal to +1.0.
Answer: True
82) If you plotted data between two variables and the points all fell precisely in a straight line
that was sloping downward to the right, your correlation coefficient would be equal to +1.0.
Answer: False
83) The Pearson product moment correlation measures the linear relationship between two
interval-scaled and/or ratio-scaled variables.
Answer: True
84) We would use the Pearson correlation table if we wanted to visualize the nonmonotonic
relationship between two nominal scaled variables.
Answer: False
85) The Pearson product moment correlation measures the linear relationship between two
nominal and/or ordinal scaled variables.
Answer: False
86) The SPSS command used to execute the Pearson product moment correlation is:
ANALYZE; CORRELATE; BIVARIATE.
Answer: True
87) If you are examining the SPSS output for Pearson correlation, you will find the value of
"1" along the diagonal of the correlation matrix indicating that each variable is perfectly
correlated with itself.
Answer: True
88) If you have a significant and very strong correlation, greater than .90, you may assume
there is a causal relationship between the two variables.
Answer: False
89) You are an officer in your college's Student Marketing Association. You are looking for
ways to ensure that members will join again the following year. Students tend to join for one
semester or one year and then drop out. You decide to take a simple random sample of this
year's members and give them a survey. One of the questions asks: Will you join the SMA
next semester? Yes, No, Don't Know. Another question asks respondents to check all the
following that they feel provides them with "value" by virtue of being in the SMA: free food
at meetings, getting to socialize in a relaxed setting with fellow classmates, learning about
businesses through the guest speaker program, getting job search information through the
organization's "Career Search" program, and getting to know your professors on a more
personal basis. You want to know which of these are related to whether or not students will
join the SMA in the next semester. What analysis should you run?
A) association analysis between the question that asks if they will join and each one of the
remaining questions
B) differences analysis between the question that asks if they will join and each one of the
remaining questions
C) predictive analysis between the question that asks if they will join and each one of the
remaining questions
D) determinant analysis between the question that asks if they will join and each one of the
remaining questions
E) None of the above; there is no analysis that will help identify which of these issues are
related to rejoining the SMA.
Answer: A
90) You are an officer in your college's Student Marketing Association. You are looking for
ways to ensure that members will join again the following year. Students tend to join for one
semester or one year and then drop out. You decide to take a simple random sample of this
year's members and give them a survey. One of the questions asks: Will you join the SMA
next semester? Yes, No, Don't Know. Another question asks respondents to check all the
following that they feel provides them with "value" by virtue of being in the SMA: free food
at meetings, getting to socialize in a relaxed setting with fellow classmates, learning about
businesses through the guest speaker program, getting job search information through the
organization's "Career Search" program, and getting to know your professors on a more
personal basis. You want to know which of these are related to whether or not students will
join the SMA in the next semester. What analysis should you run?
A) Pearson product moment correlation analysis
B) independent samples t tests
C) cross-tabulation
D) cross-tabulation with chi-square tests
E) paired samples tabulation analysis
Answer: D
91) The advertising director in your firm announced her resignation this morning to take
another job, and she is leaving this afternoon. Your boss has asked you to take charge of
advertising. Unfortunately, you learn the former director was just beginning planning for an
upcoming promotion of one of the company's new products. Your immediate decision is to
determine a brand name for the product. As the former director leaves, she stops by to drop
off some marketing research reports she had just received, which includes several tests on
brand names that were proposed for the new brand. The research company tested 30 potential
brand names. For each brand name, they collected data on a number of variables such as
"intention to purchase" and "attitude toward the brand name." All these variables were
collected using 5-point intensity continuum scales. Thus, all the variables possess an interval
level of measurement. Just focusing on the two variables mentioned ("intention to purchase"
and "attitude toward the brand name"), what type of analysis would you conduct to help you
make the decision?
A) Pearson product moment correlation analysis
B) independent samples t tests
C) cross-tabulation
D) cross-tabulation with chi-square tests
E) paired samples tabulation analysis
Answer: A
92) Michelle Steward is a marketing professor at Wake Forest University. Michelle had been
asked by the administration to study a sample of classes at Wake to help the university
understand the student population better, particularly in terms of factors that differentiate
students with high versus low GPAs. One of the questions asked was: "Did you pass or fail
the last test you took?" and another question in the study asked "Did you study or not study
for the last test you took?" Michelle decided to run a cross-tabulation analysis on these two
questions. When she did she also ran the chi-square test. The result was a Pearson Chi-Square
Value of 8.64 and a p value reported as a "Sig." in SPSS of .03. Michelle knew that this
meant:
A) there was a significant, positive association between the two variables
B) there was the presence of an association; the probability of supporting the null hypothesis
that there is no association is only 3 percent
C) there was the presence of a negative association; the probability of supporting the null
hypothesis that there is no association is only 3 percent
D) there was the presence of a positive, "very strong" association because the probability of
supporting the null hypothesis that there is no association is only .03 percent
E) None of the above; Michelle should not have run a chi-square test because the two
variables are both metric.
Answer: B
93) Michelle Steward is a marketing professor at Wake Forest University. Michelle had been
asked by the administration to study a sample of classes at Wake to help the university
understand the student population better particularly in terms of factors that differentiate
students with high versus low GPAs. One of the questions asked was, "What score did you
earn (0 to 100) on the last test that you took?" and another question in the study asked, "How
much time, estimated in numbers of minutes, did you study for the last test you took?"
Michelle decided to run a cross-tabulation analysis on these two questions. When she did, she
also ran the chi-square test. The result was a Pearson Chi-Square Value of 8.64 and a p value
reported as a "Sig." in SPSS of .03. Michelle knew that this meant:
A) there was a significant, positive association between the two variables.
B) there was the presence of an association; the probability of supporting the null hypothesis
that there is no association is only 3 percent.
C) there was the presence of a negative association; the probability of supporting the null
hypothesis that there is no association is only 3 percent.
D) there was the presence of a positive, "very strong" association because the probability of
supporting the null hypothesis that there is no association is only .03 percent.
E) None of the above; Michelle should not have run a chi-square test because the two
variables are both metric.
Answer: E
94) Michelle Steward is a marketing professor at Wake Forest University. Michelle had been
asked by the administration to study a sample of classes at Wake to help the university
understand the student population better particularly in terms of factors that differentiate
students with high versus low GPAs. One of the questions asked was, "What score did you
earn (0 to 100) on the last test that you took?" and another question in the study asked, "How
much time, estimated in numbers of minutes, did you study for the last test you took?"
Michelle decided to run a Pearson product moment correlation analysis on these two
questions. When she did, SPSS generated the following output: Pearson Correlation .98; Sig.
(2 tailed) .0001. Michelle knew that this meant:
A) there was a significant, nonmonotonic association between the two variables.
B) there was the presence of an association because the probability of supporting the
alternative hypothesis is very low, less than 1 percent.
C) there was the presence of a negative association; the probability of supporting the null
hypothesis that there is no association is only .01 percent.
D) there was the presence (aka "significant") of a positive, "very strong" association between
the variables.
E) None of the above; Michelle should not have run a Pearson product moment correlation
because the two variables are both categorical (aka nominal).
Answer: D
Test Bank for Marketing Research
Alvin C. Burns, Ronald F. Bush
9780133074673, 9780134895406, 9780134167404
