Data.txtPatient_number Disease_duration Improvement1 .docx

Data.txt
Patient_number Disease_duration Improvement
1 1 40
2 1 90
3 3 30
4 2 30
5 1 80
6 5 60
7 1 10
8 4 -10
9 2 50
10 6 40
11 1 60
12 4 0
13 2 50
14 2 100
15 3 20

16 3 70
17 5 -30
18 3 20
19 1 40
20 6 0
Question.docx
· PLEASE ANSWER QUESTIONS FROM (a) to (f)
· PLEASE USE R STATISTICAL SOFTWARE TO ANSWER
ALL QUESTIONS. NO MANUAL CALCULATION
ANYWHERE.
· INCLUDE ALL THE NECESSARY CODES
· INCLUDE ONLY THE RELEVANT OUTPUTS FROM THE
SOFTWARE.
· ALL PLOTS SHOULD BE PROPERLY LABELLED. (X-
AXIS, Y-AXIS, TITLE, ETC.).
· PLEASE GIVE PROPER EXPLANATION FOR THE STEPS
AND CONCLUSIONS WHEREVER APPLICABLE
Anti-Anginal Response against Disease History
Treadmill stress tests were applied to patients with angina
pectoris before and 4 weeks after once-daily dosing with an
experimental anti-anginal medication. The main objective for
carrying out this study is to check if the improvement in
exercise duration is dependent on the patient’s disease history.
This data file represents the disease duration time (in years)
since initial diagnosis and percent-improvement in treadmill
walking times.
QUESTIONS :
(a) Identify the response and the explanatory variables with
proper reasoning

(b) Check graphically if the dependency between
“improvement” and “disease duration time” can be considered
to be linear.
(c) Now, use a formal test of hypothesis to check for the linear
relationship in part (b) and draw appropriate conclusion
(d) How does the disease duration effect the percentage
improvement in exercise duration
(e) What assumption did you make to carry out the testing in
part (c). Is there any other assumptions required to formulate a
linear model for “improvement” and “disease duration time”
(f) Check all the model assumptions made in part (d)
graphically and comment on the violation or non violation of
the model assumptions with proper reasoning
Patient_number Disease_duration Improvement
1 1 40
2 1 90
3 3 30
4 2 30
5 1 80
6 5 60
7 1 10
8 4 -10
9 2 50
10 6 40
11 1 60
12 4 0
13 2 50
14 2 100
15 3 20
16 3 70
17 5 -30
18 3 20
19 1 40

20 6 0
Q1. Which of the following is true regarding the sampling
distribution of the mean for a large sample size?
a. It has the same shape, mean, and standard deviation as the
population.
b. It has a normal distribution with the same mean and
standard deviation as the population.
c. It has the same shape and mean as the population, but has a
smaller standard deviation.
d. It has a normal distribution with the same mean as the
population but with a smaller standard deviation.
Q2. According to a survey of American households, the
probability that the residents own 2 cars if annual household
income is over $25,000 is 80%. Of the households surveyed,
60% had incomes over $25,000 and 70% had 2 cars. The
probability that annual household income is over $25,000 if the
residents of a household do not own 2 cars is:
a. 0.12
b. 0.18
c. 0.40
d. 0.55
Q3. The collection and summarization of the socioeconomic and
physical characteristics of the employees of a particular firm is
an example of:
a. inferential statistics.
b. descriptive statistics.
c. a parameter.
d. a statistic.

Q4. The head librarian at the Library of Congress has asked her
assistant for an interval estimate of the mean number of books
checked out each day. The assistant provides the following
interval estimate: from 740 to 920 books per day.
If the head librarian knows that the population standard
deviation is 150 books checked out per day, and she asked her
assistant to use 25 days of data to construct the interval
estimate, what confidence level can she attach to the interval
estimate?
a. 99.7%
b. 99.0%
c. 98.0%
d. 95.4%
Q5. Referring to the histogram, how many graduating seniors
attended the luncheon?
a. 4
b. 152
c. 275
d. 388
Q6. The following are the durations in minutes of a sample of
long-distance phone calls made within the continental United
States reported by one long-distance carrier.
Time (in Minutes)
Relative Frequency
0 but less than 5
0.37
5 but less than 10
0.22
10 but less than 15
0.15
15 but less than 20
0.10
20 but less than 25
0.07
25 but less than 30
0.07

30 or more
0.02
Referring to the table, if 10 calls lasted 30 minutes or more,
how many calls lasted less than 5 minutes?
a. 10
b. 185
c. 295
d. 500
Q7. The collection of all possible events is called:
a. a simple probability.
b. a sample space.
c. a joint probability.
d. the null set.
Q8. A lab orders 100 rats a week for each of the 52 weeks in the
year for experiments that the lab conducts. Prices for 100 rats
follow the following distribution:
Price:
$10.00
$12.50
$15.00
Probability:
0.35
0.40
0.25
How much should the lab budget for next year's rat orders be,
assuming this distribution does not change?
a. $520
b. $637
c. $650
d. $780
Q9. A professor of economics at a small Texas university
wanted to determine what year in school students were taking
his tough economics course. Shown below is a pie chart of the
results. What percentage of the class took the course prior to
reaching their senior year?
a. 14%

b. 44%
c. 54%
d. 86%
Q10. Since a _______ is not a randomly selected probability
sample, there is no way to know how well it represents the
overall population.
a. simple random sample
b. quota sample
c. stratified sample
d. cluster sample
Q11. A study is under way in Yosemite National Forest to
determine the adult height of American pine trees. Specifically,
the study is attempting to determine what factors aid a tree in
reaching heights greater than 60 feet tall. It is estimated that the
forest contains 25,000 adult American pines. The study involves
collecting heights from 250 randomly selected adult American
pine trees and analyzing the results. Identify the population
from which the study was sampled.
a. The 250 randomly selected adult American pine trees in the
forest.
b. The 25,000 adult American pine trees in the forest.
c. All the adult American pine trees taller than 60 feet in the
world.
d. All American pine trees, of any age, in the forest.
Q12. A population frame for a survey contains a listing of
72,345 names. Using a table of random numbers, how many
digits will the code numbers for each member of your
population contain?
a. 3
b. 4
c. 5
d. 6
Q13. The chancellor of a major university was concerned about
alcohol abuse on her campus and wanted to find out the portion
of students at her university who visited campus bars every
weekend. Her advisor took a random sample of 250 students.

The portion of students in the sample who visited campus bars
every weekend is an example of a __________.
a. categorical random variable
b. discrete random variable
c. parameter
d. statistic
Time (in Minutes)
Relative Frequency
0 but less than 5
0.37
5 but less than 10
0.22
10 but less than 15
0.15
15 but less than 20
0.10
20 but less than 25
0.07
25 but less than 30
0.07
30 or more
0.02
Referring to Table 2-5, if 100 calls were randomly sampled,
how many calls lasted 15 minutes or longer?
a. 10
b. 14
c. 26
d. 74
Q15. If two events are mutually exclusive, what is the
probability that one or the other occurs?
a. 0
b. 0.50
c. 1.00

d. Cannot be determined from the information given.
Q16. According to a survey of American households, the
probability that the residents own 2 cars if annual household
income is over $25,000 is 80%. Of the households surveyed,
60% had incomes over $25,000 and 70% had 2 cars. The
probability that the residents of a household do not own 2 cars
and have an income over $25,000 a year is:
a. 0.12
b. 0.18
c. 0.22
d. 0.48
Q17. If two events are mutually exclusive and collectively
exhaustive, what is the probability that one or the other occurs?
a. 0.
b. 0.50.
c. 1.00.
Q18. Why is the Central Limit Theorem so important to the
study of sampling distributions?
a. It allows us to disregard the size of the sample selected
when the population is not normal.
b. It allows us to disregard the shape of the sampling
distribution when the size of the population is large.
c. It allows us to disregard the size of the population we are
sampling from.
d. It allows us to disregard the shape of the population
whennis large.
Q19. A sample of 200 students at a Big-Ten university was
taken after the midterm to ask them whether they went bar
hopping the weekend before the midterm or spent the weekend
studying, and whether they did well or poorly on the midterm.
The following table contains the result.
Did Well on Midterm
Did Poorly on Midterm
Studying for Exam

80
20
Went Bar Hopping
30
70
Referring to the table, of those who did well on the midterm in
the sample, _______ percent of them went bar hopping the
weekend before the midterm.
a. 15
b. 27.27
c. 30
d. 50
Q20. Tim was planning for a meeting with his boss to discuss a
raise in his annual salary. In preparation, he wanted to use the
Consumer Price Index to determine the percentage increase in
his salary in terms of real income over the last three years.
Which method of data collection was involved when he used the
Consumer Price Index?
a. Published sources
b. Experimentation
c. Surveying
d. Observation
Q21. A survey was conducted to determine how people rated the
quality of programming available on television. Respondents
were asked to rate the overall quality from 0 (no quality at all)
to 100 (extremely good quality). The stem-and-leaf display of
the data is shown below.
Stem
Leaves
3
24
4
3478999
5
112345
6

12566
7
1
8
9
2
Referring to the table, what percentage of the respondents rated
overall television quality with a rating between 50 and 75?
a. 0.11
b. 0.40
c. 0.44
d. 0.56
Q22. The process of using sample statistics to draw conclusions
about true population parameters is called:
a. statistical inference.
b. the scientific method.
c. sampling.
d. descriptive statistics.
Q23. If two equally likely events A and B are mutually
exclusive and collectively exhaustive, what is the probability
that event A occurs?
a. 0
b. 0.50
c. 1.00
Q24. Which of the following is most likely a parameter as
opposed to a statistic?
a. The average score of the first five students completing an
assignment.
b. The proportion of females registered to vote in a county.
c. The average height of people randomly selected from a
database.
d. The proportion of trucks stopped yesterday that were cited
for bad brakes.
Q25. Selection of raffle tickets from a large bowl is an example

of:
a. sampling with replacement.
b. sampling without replacement.
c. subjective probability.
d. None of the above.
Q26. The portfolio expected return of two investments
a. will be higher when the covariance is zero.
b. will be higher when the covariance is negative.
c. will be higher when the covariance is positive.
d. does not depend on the covariance.
Q27. Given the numbers: 1, 3, 5, 7, 8 what are the average and
the median?
a. Average = 4.8; Median = 5.0
b. Average = 5.0; Median = 5.0
c. Average = 4.8; Median = 4.8
d. Average = 5.0; Median = 4.8
Q28. The employees of a company were surveyed on questions
regarding their educational background and marital status. Of
the 600 employees, 400 had college degrees, 100 were single,
and 60 were single college graduates. The probability that an
employee of the company is single or has a college degree is:
a. 0.10
b. 0.25
c. 0.667
d. 0.733
Q29. A catalog company that receives the majority of its orders
by telephone conducted a study to determine how long
customers were willing to wait on hold before ordering a
product. The length of time was found to be a random variable
best approximated by an exponential distribution with a mean
equal to 3 minutes. What proportion of customers having to
hold more than 1.5 minutes will hang up before placing an
order?
a. 0.86466
b. 0.60653
c. 0.39347

d. 0.13534
Time (in Minutes)
Relative Frequency
0 but less than 5
0.37
5 but less than 10
0.22
10 but less than 15
0.15
15 but less than 20
0.10
20 but less than 25
0.07
25 but less than 30
0.07
30 or more
0.02
Referring to the table, what is the width of each class?
a. 1 minute
b. 5 minutes
c. 2%
d. 100%
Q31. The probability that
· house sales will increase in the next 6 months is estimated to
be 0.25.
· the interest rates on housing loans will go up in the same
period is estimated to be 0.74
· house sales or interest rates will go up during the next 6
months is estimated to be 0.89
The probability that house sales will increase but interest rates
will not during the next 6 months is:

a. 0.065
b. 0.15
c. 0.51
d. 0.89
Q32. Which of the mean, median, mode, and geometric mean
are resistant measures of central tendency?
a. The mean and median.
b. The median and mode.
c. The mode and geometric mean.
d. The mean and mode.
Q33. The probability that
· house sales will increase in the next 6 months is estimated to
be 0.25
· the interest rates on housing loans will go up in the same
period is estimated to be 0.74
· house sales or interest rates will go up during the next 6
months is estimated to be 0.89
The probability that neither house sales nor interest rates will
increase during the next 6 months is:
a. 0.11
b. 0.195
c. 0.89
d. 0.90
Q34. Which of the following statistics is not a measure of
central tendency?
a. Mean.
b. Median.
c. Mode.
d. Q3.
Q35. The width of each bar in a histogram corresponds to the:
a. differences between the boundaries of the class.
b. number of observations in each class.
c. midpoint of each class.
d. percentage of observations in each class.

Q36. In left-skewed distributions, which of the following is the
correct statement?
a. The distance from Q1 to Q2 is smaller than the distance
from Q2 to Q3.
b. The distance from the smallest observation to Q1 is larger
than the distance from Q3 to the largest observation.
c. The distance from the smallest observation to Q2 is smaller
than the distance from Q2 to the largest observation.
d. The distance from Q1 to Q3 is twice the distance from Q1
to Q2.
Q37. The Central Limit Theorem is important in statistics
because:
a. for a largen, it says the population is approximately normal.
b. for any population, it says the sampling distribution of the
sample mean is approximately normal, regardless of the sample
size.
c. for a largen, it says the sampling distribution of the sample
mean is approximately normal, regardless of the shape of the
population.
d. for any sized sample, it says the sampling distribution of
the sample mean is approximately normal.
Q38. A sample of 200 students at a Big-Ten university was
taken after the midterm to ask them whether they went bar
hopping the weekend before the midterm or spent the weekend
studying, and whether they did well or poorly on the midterm.
The following table contains the result.
Did Well on the midterm
Did Poorly on Midterm
Studying for Exam
80
20
Went Bar Hopping
30
70
Referring to the table, _______ percent of the students in the

sample went bar hopping the weekend before the midterm and
did well on the midterm.
a. 15
b. 27.27
c. 30
d. 50
Q39. Which of the following statements about the median is not
true?
a. It is more affected by extreme values than the mean.
b. It is a measure of central tendency.
c. It is equal to Q2.
d. It is equal to the mode in bell-shaped "normal"
distributions.
Q40. A confidence interval was used to estimate the proportion
of statistics students that are females. A random sample of 72
statistics students generated the following 90% confidence
interval: (0.438, 0.642). Based on the interval above, is the
population proportion of females equal to 0.60?
a. No, and we are 90% sure of it.
b. No. The proportion is 54.17%.
c. Maybe. 0.60 is a believable value of the population
proportion based on the information above.
Q1. The sample correlation coefficient between X and Y is
0.375. It has been found out that the p-value is 0.256 when
testing H0: ρ = 0 against the two-sided alternative H1: ρ ≠ 0. To
test H0: ρ = 0 against the one-sided alternative H1: ρ > 0 at a
significance level of 0.193, the p-value is
a. 0.256/2
b. 0.256
c. 1 - 0.256
d. 1 - 0.256/2
Q2. A researcher randomly sampled 30 graduates of an MBA
program and recorded data concerning their starting salaries. Of
primary interest to the researcher was the effect of gender on
starting salaries. Analysis of the mean salaries of the females

and males in the sample is given below.
Size
Mean
Std Dev
Females
18
48,266.7
13,577.63
Males
12
55,000
11,741.29
Std Error = 4,764.82
Means Diff = -6,733.3
Z = -1.4528 2-tailed p value = 0.1463
T = -1.4221 2-tailed p value = 0.1574
Referring to the table, the researcher was attempting to show
statistically that the female MBA graduates have a significantly
lower mean starting salary than the male MBA graduates. What
assumptions were necessary to conduct this hypothesis test?
a. Both populations of salaries (male and female) must have
approximate normal distributions.
b. The population variances are approximately equal.
c. The samples were randomly and independently selected.
d. All of the above assumptions were necessary.
Q3. The Y-intercept (b0) represents the:
a. predicted value of Y when X = 0.
b. change in estimated average Y per unit change in X.
c. predicted value of Y.
d. variation around the sample regression line.
Q4. A local real estate appraiser analyzed the sales prices of
homes in 2 neighborhoods to the corresponding appraised values

of the homes. The goal of the analysis was to compare the
distribution of sale-to-appraised ratios from homes in the 2
neighborhoods. Random and independent samples were selected
from the 2 neighborhoods from last year's homes sales, 8 from
each of the 2 neighborhoods. Identify the nonparametric method
that would be used to analyze the data.
a. the Wilcoxon Signed-Ranks Test, using the test statistic Z
b. the Wilcoxon Signed-Ranks Test, using the test statistic W
c. the Wilcoxon Rank Sum Test, using the test statistic T1
d. the Wilcoxon Rank Sum Test, using the test statistic Z
Q5. A survey claims that 9 out of 10 doctors recommend aspirin
for their patients with headaches. To test this claim against the
alternative that the actual proportion of doctors who recommend
aspirin is less than 0.90, a random sample of 100 doctors results
in 83 who indicate that they recommend aspirin. The value of
the test statistic in this problem is approximately equal to:
a. -4.12
b. -2.33
c. -1.86
d. -0.07
Q6. A realtor wants to compare the average sales-to-appraisal
ratios of residential properties sold in four neighborhoods (W,
X, Y, and Z). Four properties are randomly selected from each
neighborhood and the ratios recorded for each, as shown below.
W: 1.2, 1.1, 0.9, 0.4
X: 2.5, 2.1, 1.9, 1.6
Y: 1.0, 1.5, 1.1, 1.3
Z: 0.8, 1.3, 1.1, 0.7
Interpret the results of the analysis summarized in the following
table:
Source

df
SS
MS
F
PR > F
Neighborhoods
2.97
0.990
8.31
0.0260
Error
12
Total
4.40
Referring to the table, the within group mean squares is
a. 0.119
b. 0.990
c. 1.109
d. 8.31
Q7. If a group of independent variables are not significant
individually but are significant as a group at a specified level of
significance, this is most likely due to:
a. autocorrelation.
b. the presence of dummy variables.
c. the absence of dummy variables.
d. collinearity.
Q8. In a multiple regression model, the adjusted r2
a. cannot be negative.
b. can sometimes be negative.

c. can sometimes be greater than +1.
d. has to fall between 0 and +1.
Q9. Why would you use the Tukey-Kramer procedure?
a. To test for normality.
b. To test for homogeneity of variance.
c. To test independence of errors.
d. To test for differences in pairwise means.
Q10. A campus researcher wanted to investigate the factors that
affect visitor travel time in a complex, multilevel building on
campus. Specifically, he wanted to determine whether different
building signs (building maps versus wall signage) affect the
total amount of time visitors require to reach their destination
and whether that time depends on whether the starting location
is inside or outside the building. Three subjects were assigned
to each of the combinations of signs and starting locations, and
travel time in seconds from beginning to destination was
recorded. How should the data be analyzed?
Starting Room
Interior
Exterior
Wall Signs
141
224
119
339
238
139
Map
85
226

94
129
126
130
a. Completely randomized design
b. Randomized block design
c. 2 x 2 factorial design
d. Kruskal-Wallis rank test
Q11. As a business statistics project, a student examined the
factors that determine parking meter rates throughout the
campus and downtown area. The campus is a group of buildings
located in the center of downtown, with an open central
quadrangle. Data were collected for the price of parking per
hour and the number of blocks to the quadrangle. In addition,
two dummy variables were coded to indicate the location of the
parking meter (See below). The population regression model
hypothesized is
Yi = ß0 + ß1x1i + ß2x2i + ß3x3i + ei
where
Y is the price per hour
x1 is a numerical variable = the number of blocks to the
quadrangle
(Note that if x1 is less than 2, then the meter is on campus; if
x1 is less than 3, then the meter is downtown)
x2 is a dummy variable = 1 if inside downtown and off campus,
0 otherwise
x3 is a dummy variable = 1 if outside downtown and off
campus, 0 otherwise
The following Excel results are obtained.

Regression Statistics
Multiple R
9.9659
R Square
0.9331
Adjusted R Square
0.9294
Standard Error
0.0327
Observations
58
ANOVA
df
SS
MS
F
Signif F
Regression
3
0.8094
0.2698
251.1995
1.0964E-31
Residual
54
0.0580
0.0010
Total
57
0.8675
Coeff
StdError
t Stat
P-value

Intercept
0.5118
0.0136
37.4675
2.4904
X1
-0.0045
0.0034
-1.3275
0.1898
X2
-0.2392
0.0123
-19.3942
5.3581E-26
X3
-0.0002
0.0123
-0.0214
0.9829
Referring to the tables, predict the meter rate per hour if one
parks outside of downtown and off campus, 3 blocks from the
quad.
a. $-0.0139
b. $0.2589
c. $0.2604
d. $0.4981
Q12. If we use the chi-squared method of analysis to test for the
differences among 4 proportions, the degrees of freedom are
equal to:
a. 3
b. 4

c. 5
d. 1
Q13. An economist is interested to see how consumption for an
economy (in $ billions) is influenced by gross domestic product
($ billions) and aggregate price (consumer price index). The
Microsoft Excel output of this regression is partially reproduced
below.
SUMMARY OUTPUT
Multiple R
0.991
R Square
0.982
Adjusted R Square
0.976
Standard Error
0.299
Observations
10
ANOVA
df
SS
MS
F
Signif F
Regression
2
33.4163
16.7082
186.325
0.0001
Residual
7
0.6277

0.0897
Total
9
34.0440
Coeff
StdError
t Stat
P-value
Intercept
-0.0861
0.5674
-0.152
0.8837
GDP
0.7654
0.0574
13.340
0.0001
Price
-0.0006
0.0028
-0.219
0.8330
Referring to the tables, one economy in the sample had an
aggregate consumption level of $4 billion, a GDP of $6 billion,
and an aggregate price level of 200. What is the residual for this
data point?
a. $4.39 billion
b. $0.39 billion
c. -$0.39 billion
d. -$1.33 billion

Q14. Testing for the existence of correlation is equivalent to
a. testing for the existence of the slope (β1).
b. testing for the existence of the Y-intercept (β0).
c. the confidence interval estimate for predicting Y.
d. testing for the existence of the slope (β10).
Q15. As a business statistics project, a student examined the
factors that determine parking meter rates throughout the
campus and downtown area. The campus is a group of buildings
located in the center of downtown, with an open central
quadrangle. Data were collected for the price of parking per
hour and the number of blocks to the quadrangle. In addition,
two dummy variables were coded to indicate the location of the
parking meter (See below). The population regression model
hypothesized is
Yi = ß0 + ß1x1i + ß2x2i + ß3x3i + ei
where
Y is the price per hour
x1 is a numerical variable = the number of blocks to the
quadrangle
(Note that if x1 is less than 2, then the meter is on campus; if
x1 is less than 3, then the meter is downtown)
x2 is a dummy variable = 1 if inside downtown and off campus,
0 otherwise
x3 is a dummy variable = 1 if outside downtown and off
campus, 0 otherwise
The following Excel results are obtained.
Multiple R
9.9659
R Square
0.9331

Adjusted R Square
0.9294
Standard Error
0.0327
Observations
58
ANOVA
df
SS
MS
F
Signif F
Regression
3
0.8094
0.2698
251.1995
1.0964E-31
Residual
54
0.0580
0.0010
Total
57
0.8675
Coeff
StdError
t Stat
P-value
Intercept
0.5118
0.0136
37.4675

2.4904
X1
-0.0045
0.0034
-1.3275
0.1898
X2
-0.2392
0.0123
-19.3942
5.3581E-26
X3
-0.0002
0.0123
-0.0214
0.9829
Referring to the tables, if one is already outside of downtown
and off campus but decides to park an additional 3 blocks from
the quadrangle, the estimated average parking meter rate will:
a. decrease by 0.0045.
b. decrease by 0.0135.
c. decrease by 0.0139.
d. decrease by 0.4979.
Q16. The following EXCEL output contains the results of a test
to determine if the proportions of satisfied guests at two resorts
are the same or different.
Hypothesized Difference0
Level of Significance 0.05
Group 1
Number of Successes163
Sample Size227
Group 2

Number of Successes154
Sample Size262
Group 1 Proportion0.718061674
Group 2 Proportion0.58778626
Difference in Two Proportions 0.130275414
Average Proportion0.648261759
Test Statistic3.00875353
Two-Tailed Test
Lower Critical Value -1.959961082
Upper Critical Value 1.959961082
p-Value 0.002623357
Referring to the data above, if you want to test the claim that
"Resort 1 (Group 1) has a higher proportion of satisfied guests
compared to Resort 2 (Group 2)," the p-value of the test will be
a. 0.00262
b. 0.00262/2
c. 2*(0.00262)
d. 1 - (0.00262/2)
Q17. A real estate builder wishes to determine how house size
(House) is influenced by family income (Income), family size
(Size), and education of the head of household (School). House
size is measured in hundreds of square feet, income is measured
in thousands of dollars, and education is in years. The builder
randomly selected 50 families and ran the µltiple regression.
Microsoft Excel output is provided below:
SUMMARY OUTPUT
Multiple R
0.865
R Square
0.748
Adjusted R Square
0.726

Standard Error
5.195
Observations
50
ANOVA
df
SS
MS
F
Signif F
Regression
3605.7736
901.4434
0.0001
Residual
1214.2264
26.9828
Total
49
4820.0000
Coeff
StdError
t Stat
P-value
Intercept
-1.6335
5.8078
-0.281
0.7798
Income
0.4485
0.1137

3.9545
0.0003
Size
4.2615
0.8062
5.286
0.0001
School
-0.6517
0.4319
-1.509
0.1383
Referring to the tables, one individual in the sample had an
annual income of $10,000, a family size of 1, and an education
of 8 years. This individual owned a home with an area of 1,000
square feet (House = 10.00). What is the residual (in hundreds
of square feet) for this data point?
a. 8.10
b. 5.40
c. -5.40
d. -8.10
Q18. A manager of a product sales group believes the number of
sales made by an employee (Y) depends on how many years that
employee has been with the company (X1) and how he/she
scored on a business aptitude test (X2). A random sample of 8
employees provides the following:
Employee
Y
X1
X2
1
100
10

7
2
90
3
10
3
80
8
9
4
70
5
4
5
60
5
8
6
50
7
5
7
40
1
4
8
30
1
1
Referring to the table, for these data, what is the estimated
coefficient for the variable representing years an employee has
been with the company, b1?
a. 0.998
b. 3.103

c. 4.698
d. 21.293
Q19. In testing for differences between the means of 2
independent populations, the null hypothesis is:
a. H0: μ1 - μ2 = 2.
b. H0: μ1 - μ2 = 0.
c. H0: μ1 - μ2 > 0.
d. H0: μ1 - μ2 < 2.
Q20. A manager of a product sales group believes the number of
sales made by an employee (Y) depends on how many years that
employee has been with the company (X1) and how he/she
scored on a business aptitude test (X2). A random sample of 8
employees provides the following:
Employee
Y
X1
X2
1
100
10
7
2
90
3
10
3
80
8
9
4
70
5
4
5

60
5
8
6
50
7
5
7
40
1
4
8
30
1
1
Referring to Table 14-1, for these data, what is the estimated
coefficient for the variable representing scores on the aptitude
test, b2?A manager of a product sales group believes the
number of sales made by an employee (Y) depends on how
many years that employee has been with the company (X1) and
how he/she scored on a business aptitude test (X2). A random
sample of 8 employees provides the following:
Employee
Y
X1
X2
1
100
10
7
2
90
3
10

3
80
8
9
4
70
5
4
5
60
5
8
6
50
7
5
7
40
1
4
8
30
1
1
Referring to the table, what is the estimated coefficient for the
variable representing scores on the aptitude test, b2?
a. 0.998
b. 3.103
c. 4.698
d. 21.293
Q21. To test the effects of a business school preparation course,
8 students took a general business test before and after the
course. The results are given below.

Student
Exam Score Before Course
Exam Score After Course
1
530
670
2
690
770
3
910
1,000
4
700
710
5
450
550
6
820
870
7
820
770
8
630
610
Referring to the table, at the 0.05 level of significance, the
conclusion for this hypothesis test would be:
a. the business school preparation course does improve exam
score.
b. the business school preparation course does not improve
exam score.
c. the business school preparation course has no impact on

exam score.
d. It cannot be drawn from the information given.
Q22. If the p value is less than α in a two-tailed test,
a. the null hypothesis should not be rejected.
b. the null hypothesis should be rejected.
c. a one-tailed test should be used.
d. no conclusion should be reached.
Q23. If the Type I error (α) for a given test is to be decreased,
then for a fixed sample size n
a. the Type II error (β) will also decrease.
b. the Type II error (β) will increase.
c. the power of the test will increase.
d. a one-tailed test must be utilized.
Q24. Psychologists have found that people are generally
reluctant to transmit bad news to their peers.
This phenomenon has been termed the "MM effect." To
investigate the cause of the MM effect, 40 undergraduates
at Duke University participated in an experiment. Each subject
was asked to administer an IQ test to another student
and then provide the test taker with his or her percentile score.
Unknown to the subject, the test taker was a bogus
student who was working with the researchers. The
experimenters manipulated two factors: subject visibility and
success of test taker, each at two levels. Subject visibility was
either visible or not visible to the test taker. Success of
the test taker was either visible or not visible to the test taker.
Success of the test taker was either
top 20% or bottom 20%. Ten subjects were randomly assigned
to each of the 2 x 2 = 4 experimental conditions, then
the time (in seconds) between the end of the test and the
delivery of the percentile score from the subject to the test
taker was measured. (This variable is called the latency to
feedback.) The data were subjected to appropriate analyses

with the following results.
Source
df
SS
MS
F
PR > F
Subject visibility
1
1380.24
1380.24
4.26
0.043
Test taker success
1
1325.16
1325.16
4.09
0.050
Interaction
1
3385.80
3385.80
10.45
0.002
Error
36
11,664.00
324.00
Total
39
17,755.20

Referring to the table, in the context of this study, interpret the
statement: "Subject visibility and test taker success interact."
a. The difference between the mean feedback time for visible
and nonvisible subjects depends on the success of the test taker.
b. The difference between the mean feedback time for test
takers scoring in the top 20% and bottom 20% depends on the
visibility of the subject.
c. The relationship between feedback time and subject
visibility depends on the success of the test taker.
d. All of the above are correct interpretations.
Q25. A candy bar manufacturer is interested in trying to
estimate how sales are influenced by the price of their product.
To do this, the company randomly chooses 6 small cities and
offers the candy bar at different prices. Using candy bar sales as
the dependent variable, the company will conduct a simple
linear regression on the data below:
City
Price ($)
Sales
River Falls
1.30
100
Hudson
1.60
90
Ellsworth
1.80
90
Prescott
2.00
40
Rock Elm
2.40
38
Stillwater

2.90
32
Referring to the table, what is the coefficient of correlation for
these data?
a. -0.8854
b. -0.7839
c. 0.7839
d. 0.8854
Q26. A ____________ is a numerical quantity computed from
the data of a sample and is used in reaching a decision on
whether or not to reject the null hypothesis.
a. significance level
b. critical value
c. test statistic
d. parameter
Q27. An investment specialist claims that if one holds a
portfolio that moves in opposite direction to the market index
like the S&P 500, then it is possible to reduce the variability of
the portfolio's return. In other words, one can create a portfolio
with positive returns but less exposure to risk.
A sample of 26 years of S&P 500 index and a portfolio
consisting of stocks of private prisons, which are believed to be
negatively related to the S&P 500 index, is collected. A
regression analysis was performed by regressing the returns of
the prison stocks portfolio (Y) on the returns of S&P 500 index
(X) to prove that the private prisons stock portfolio is
negatively related to the S&P 500 index at a 5% level of
significance. The results are given in the following EXCEL
output.
Coefficients
Standard Error

T Stat
P-value
Intercept
4.866004258
0.35743609
13.61363441
8.7932E-13
S&P
-0.502513506
0.071597152
-7.01862425
2.94942E-07
Referring to the table, which of the following will be a correct
conclusion?
a. We cannot reject the null hypothesis and, therefore,
conclude that there is sufficient evidence to show that the
prison stock portfolio and S&P 500 index are negatively related.
b. We can reject the null hypothesis and, therefore, conclude
that there is sufficient evidence to show that the prison stock
portfolio and S&P 500 index are negatively related.
c. We cannot reject the null hypothesis and, therefore,
conclude that there is not sufficient evidence to show that the
prison stock portfolio and S&P 500 index are negatively related.
d. We can reject the null hypothesis and conclude that there
is not sufficient evidence to show that the prison stock portfolio
and S&P 500 index are negatively related.
Q28. One criterion used to evaluate employees in the assembly
section of a large factory is the number of defective pieces per
1,000 parts produced. The quality control department wants to
find out whether there is a relationship between years of
experience and defect rate. Since the job is repetitious, after the
initial training period any improvement due to a learning effect

might be offset by a loss of motivation. A defect rate is
calculated for each worker in a yearly evaluation. The results
for 100 workers are given in the table below.
Years Since Training Period
< 1 Year
1 - 4 Years
5 - 9 Years
Defect Rate
High
6
9
9
Average
9
19
23
Low
7
8
10
Referring to the table, find the rejection region necessary for
testing at the 0.05 level of significance whether there is a
relationship between defect rate and years of experience.
a. Reject H0 if chi-square > 16.919
b. Reject H0 if chi-square > 15.507
c. Reject H0 if chi-square > 11.143
d. Reject H0 if chi-square > 9.488

Q29. A campus researcher wanted to investigate the factors that
affect visitor travel time in a complex, multilevel building on
campus. Specifically, he wanted to determine whether different
building signs (building maps versus wall signage) affect the
total amount of time visitors require to reach their destination
and whether that time depends on whether the starting location
is inside or outside the building. Three subjects were assigned
to each of the combinations of signs and starting locations, and
travel time in seconds from beginning to destination was
recorded. An Excel output of the appropriate analysis is given
below:
ANOVA
Source of Variation
SS
df
MS
F
P-value
F crit
Signs
14008.33
14008.33
0.11267
5.317645
Starting Location
12288
2.784395
0.13374
5.317645
Interaction
48
48
0.919506
5.317645

Within
35305.33
4413.167
Total
61649.67
11
Referring to the table, the within (error) degrees of freedom is
a. 1
b. 4
c. 8
d. 11
Q30. If we wish to determine whether there is evidence that the
proportion of successes is higher in group 1 than in group 2, the
appropriate test to use is
a. the Z test.
b. the chi-squared test.
c. the W test.
d. the X test.
Size
Mean
Std Dev
Females
18

48,266.7
13,577.63
Males
12
55,000
11,741.29
Z = -1.4528 2-tailed p value = 0.1463
T = -1.4221 2-tailed p value = 0.1574
lower mean starting salary than the male MBA graduates. From
the analysis in the table, the correct test statistic is:
a. 4,634.72
b. -1.4221
c. -1.4528
d. -6,733.33
Q32. If we are performing a two-tailed test of whether μ = 100,
the probability of detecting a shift of the mean to 105 will be
________ the probability of detecting a shift of the mean to
110.
a. less than
b. greater than
c. equal to
d. not comparable to
Q33. Parents complain that children read too few storybooks
and watch too µch television nowadays. A survey of 1,000
children reveals the following information on average time
spent watching TV and average time spent reading storybooks
Average time spent reading storybooks
Average time

spent watching TV
Less than
1 hour
Between
1 and 2 hours
More than
2 hours
Less than 2 hours
90
85
130
More than 2 hours
655
32
8
Referring to the table, to test whether there is any relationship
between average time spent watching TV and average time spent
reading storybooks, the value of the measured test statistic is:
a. -12.59
b. 1.61
c. 481.49
d. 1,368.06
Q34. A large national bank charges local companies for using
their services. A bank official reported the results of a
regression analysis designed to predict the bank's charges (Y) --
measured in dollars per month -- for services rendered to local
companies. One independent variable used to predict service
charge to a company is the company's sales revenue (X) --
measured in millions of dollars. Data for 21 companies who use
the bank's services were used to fit the model:
E(Y) = ß0 + ß1X

The results of the simple linear regression are provided below.
Y = -2,700+20X, syx = 65, two-tailed p value = 0.034 (for
testing ß1)
Referring to Table 13-1, interpret the p value for testing
whether ß1 exceeds 0.
a. There is sufficient evidence (at the α = 0.05) to conclude
that sales revenue (X) is a useful linear predictor of service
charge (Y).
b. There is insufficient evidence (at the α = 0.10) to conclude
that sales revenue (X) is a useful linear predictor of service
charge (Y).
c. Sales revenue (X) is a poor predictor of service charge
(Y).
d. For every $1 million increase in sales revenue, we expect a
service charge to increase $0.034.
Q35. A study published in the American Journal of Public
Health was conducted to determine whether the use of seat belts
in motor vehicles depends on ethnic status in San Diego County.
A sample of 792 children treated for injuries sustained from
motor vehicle accidents was obtained, and each child was
classified according to (1) ethnic status (Hispanic or non-
Hispanic) and (2) seat belt usage (worn or not worn) during the
accident. The number of children in each category is given in
the table below.
Hispanic
Non-Hispanic
Seat belts worn
31
148
Seat belts not worn
283
330

Referring to the table, which test would be used to properly
analyze the data in this experiment?
a. chi-square test for independence in a two-way contingency
table.
b. chi-square test for equal proportions in a one-way table.
c. ANOVA F test for interaction in a 2 x 2 factorial design.
d. chi-square test for a 2 x 2 factorial design.
Q36. The Journal of Business Venturing reported on the
activities of entrepreneurs during the organization creation
process. As part of a designed study, a total of 71 entrepreneurs
were interviewed and divided into 3 groups: those that were
successful in founding a new firm (n1 = 34), those still actively
trying to establish a firm (n2 = 21), and those who tried to start
a new firm but eventually gave up (n3 = 16). The total number
of activities undertaken (e.g., developed a business plan, sought
funding, looked for facilities) by each group over a specified
time period during organization creation was measured. The
objective is to compare the mean number of activities of the 3
groups of entrepreneurs. Because of concerns over necessary
assumption of the parametric analysis, it was decided to use a
nonparametric analysis. Identify the nonparametric method that
would be used to analyze the data.
a. Wilcoxon Rank Sums Test
b. Wilcoxon Signed Rank Test
c. Kruskal-Wallis Rank Test for Differences in Medians
d. One-way ANOVA F test
Size

Mean
Std Dev
Females
18
48,266.7
13,577.63
Males
12
55,000
11,741.29
Z = -1.4528 2-tailed p value = 0.1463
T = -1.4221 2-tailed p value = 0.1574
lower mean starting salary than the male MBA graduates.
According to the test run, which of the following is an
appropriate alternative hypothesis?
a. H1: μfemales > μmales
b. H1: μfemales < μmales
c. H1: μfemales ≠ μmales
d. H1: μfemales = μmales
Q38. A computer used by a 24-hour banking service is supposed
to randomly assign each transaction to one of 5 memory
locations. A check at the end of a day's transactions gave the
counts shown in the table to each of the 5 memory locations,
along with the number of reported errors.
Memory Location:
1
2
3
4

5
Number of Transactions:
82
100
74
92
102
Number of Reported Errors
11
12
6
9
10
The bank manager wanted to test whether the proportion of
errors in transactions assigned to each of the 5 memory
locations differ. Referring to the table, which test would be
used to properly analyze the data in this experiment?
a. chi-square test for independence in a two-way contingency
table
b. chi-square test for equal proportions in a one-way table
c. ANOVA F test for main treatment effect
d. Z test for the difference in two proportions
Q39. If a test of hypothesis has a Type I error probability (α) of
0.01, we mean
a. if the null hypothesis is true, we don't reject it 1% of the
time.
b. if the null hypothesis is true, we reject it 1% of the time.
c. if the null hypothesis is false, we don't reject it 1% of the
time.
d. if the null hypothesis is false, we reject it 1% of the time.
Q40. The following EXCEL output contains the results of a test
to determine if the proportions of satisfied guests at two resorts

are the same or different.
Hypothesized Difference 0
Level of Significance 0.05
Group 1
Number of Successes 163
Sample Size 227
Group 2
Number of Successes 154
Sample Size 262
Group 1 Proportion 0.718061674
Group 2 Proportion 0.58778626
Difference in Two Proportions 0.130275414
Average Proportion 0.648261759
Test Statistic 3.00875353
Two-Tailed Test
Lower Critical Value -1.959961082
Upper Critical Value 1.959961082
p-Value 0.002623357
Referring to the table, if you want to test the claim that "Resort
1 (Group 1) has a higher proportion of satisfied guests
compared to Resort 2 (Group 2)," the p-value of the test will be
a. 0.00262
b. 0.00262/2
c. 2*(0.00262)
d. 1 - (0.00262/2)

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