Instructions:
1. Using annual data on GDP from the U.S. Census Bureau complete the following:
a. Use a simple trend projection and discuss the meaning of the estimated coefficient for the trend variable.
b. Assume that you only have data up through 1999 and conduct a 3-period moving average for GDP through 2007. Find the in-sample and out of sample MAE, MAPE, and RMSE using the forecast errors.
c. Conduct a simple exponential smoothing model using α = 0.3 and α = 0.7 using the GDP data set. Again assume that we only have data through 1999.
annual data on GDP from the U.S. Census Bureau
DATE
GDP
1980-01-01
2789.5
1981-01-01
3128.4
1982-01-01
3255.0
1983-01-01
3536.7
1984-01-01
3933.2
1985-01-01
4220.3
1986-01-01
4462.8
1987-01-01
4739.5
1988-01-01
5103.8
1989-01-01
5484.4
1990-01-01
5803.1
1991-01-01
5995.9
1992-01-01
6337.7
1993-01-01
6657.4
1994-01-01
7072.2
1995-01-01
7397.7
1996-01-01
7816.9
1997-01-01
8304.3
1998-01-01
8747.0
1999-01-01
9268.4
2000-01-01
9817.0
2001-01-01
10128.0
2002-01-01
10469.6
2003-01-01
10960.8
2004-01-01
11685.9
2005-01-01
12433.9
2006-01-01
13194.7
2007-01-01
13843.8
2. Complete the Forecasting for Tracway for mowers using the Tracway data. For this exercise, forecast industry sales as well as market share. Compare the moving average (3 month) method against Winter's method. Since we are forecasting mowers, think about which model you would expect to be more accurate.
After working through the problems, go to Lesson 9: Individual Exercises 9 and answer the associated multiple choice questions.
Forecasting for Tracway
An important input to planning manufacturing capacity is a good forecast of sales. In reviewing the Tracway database, Henry Hudson is interested in forecasting sales for mowers and tractors in each marketing region. Although Henry has obtained expert opinions on sales forecasting using the Delphi process, he would like to generate time series forecasts for the next year for each product by region. Henry plans to then compare and incorporate the judgmental forecasts from the Delphi process to the quantitative forecasts developed via the Delphi process.
Tracway data.
See attached worksheet labeled (Tracway data)
Questions:
Using annual data on GDP from the U.S. Census Bureau answer questions 1-6.
1.
The trend analysis reveals
A) no evidence of any clear trend either upward or downward.
B) an upward trend.
C) an downward trend.
D) a positive but insignificant coefficient for the trend variable.
2.
According to the F-statistic we cannot reject the null hypothesis that R2 = 0 when using trend analysis.
A) True
B) False
3.
When conducting a 3-period moving average for GDP through 2007 the out of sample Mean Absolute Error (MAE) is found to be approximately
A) $682
B) $696
C) $1,043
D) none of the above
4.
The moving average analysis shows forecasts that consistently underestimate actual GDP.
A) True
B) False
5.
When using an exponential smoothing model .
Instructions 1. Using annual data on GDP from the U.S. Cens.docx
1. Instructions:
1. Using annual data on GDP from the U.S. Census Bureau
complete the following:
a. Use a simple trend projection and discuss the meaning of the
estimated coefficient for the trend variable.
b. Assume that you only have data up through 1999 and conduct
a 3-period moving average for GDP through 2007. Find the in-
sample and out of sample MAE, MAPE, and RMSE using the
forecast errors.
c. Conduct a simple exponential smoothing model using α = 0.3
and α = 0.7 using the GDP data set. Again assume that we only
have data through 1999.
annual data on GDP from the U.S. Census Bureau
DATE
GDP
1980-01-01
2789.5
1981-01-01
3128.4
1982-01-01
3255.0
1983-01-01
3536.7
1984-01-01
3933.2
1985-01-01
4220.3
1986-01-01
4462.8
1987-01-01
4739.5
3. 2006-01-01
13194.7
2007-01-01
13843.8
2. Complete the Forecasting for Tracway for mowers using the
Tracway data. For this exercise, forecast industry sales as well
as market share. Compare the moving average (3 month) method
against Winter's method. Since we are forecasting mowers,
think about which model you would expect to be more accurate.
After working through the problems, go to Lesson 9: Individual
Exercises 9 and answer the associated multiple choice
questions.
Forecasting for Tracway
An important input to planning manufacturing capacity is a
good forecast of sales. In reviewing the Tracway database,
Henry Hudson is interested in forecasting sales for mowers and
tractors in each marketing region. Although Henry has obtained
expert opinions on sales forecasting using the Delphi process,
he would like to generate time series forecasts for the next year
for each product by region. Henry plans to then compare and
incorporate the judgmental forecasts from the Delphi process to
the quantitative forecasts developed via the Delphi process.
Tracway data.
See attached worksheet labeled (Tracway data)
4. Questions:
Using annual data on GDP from the U.S. Census Bureau answer
questions 1-6.
1.
The trend analysis reveals
A) no evidence of any clear trend either upward or downward.
5. B) an upward trend.
C) an downward trend.
D) a positive but insignificant coefficient for the trend variable.
2.
According to the F-statistic we cannot reject the null hypothesis
that R2 = 0 when using trend analysis.
A) True
B) False
3.
When conducting a 3-period moving average for GDP through
2007 the out of sample Mean Absolute Error (MAE) is found to
be approximately
A) $682
B) $696
C) $1,043
D) none of the above
4.
The moving average analysis shows forecasts that consistently
underestimate actual GDP.
A) True
B) False
5.
When using an exponential smoothing model for α = 0.3, the out
of sample MAPE is ____________ and the within sample MAPE
is ____________.
A) 21.85%, 11.45%
B) 20.62%, 8.35%
C) 27.83%, 16.29%
D) none of the above
6.
The exponential smoothing forecast is more accurate at α = 0.7
6. than at α = 0.3.
A) True
B) False
Complete the Forecasting for Tracway for mowers and answer
questions 7-12.
7.
Using the 3 month moving average for industry sales, which
region is found to have the smallest MAE?
A) North America
B) South America
C) Europe
D) Pacific
8.
Using Winter’s method for industry sales, which region is found
to have the smallest RMSE?
A) North America
B) South America
C) Europe
D) Pacific
9.
The MAPE reveals that the average forecast misses the target by
3.00% using Winter’s method in South America for industry
sales.
A) True
B) False
10.
Which statement most accurately reflects the meaning of the
optimal smoothing constants found for industry sales in South
America and Europe?
A) The model misses the target by 1.00% using Winter’s
7. method.
B) The model reacts slowly to changes in level, but quickly to
trend and season for Winter's method.
C) The model reacts right away to changes in level, but almost
never reacts to trend for Winter's method.
D) none of the above
11.
Market share forecast errors are all smaller in comparison to
sales which makes sense because one would not expect any
region to have significant gains or losses in market share over
the course of the 12 months forecasted in the models.
A) True
B) False
12.
The regional sectors forecast for industry sales and market share
are better using Winter's method which can be seen by the
results for MAE and MAPE.
A) True
B) False
Focus of the Final Paper
This assignment focuses on how the management practices of
planning, leading, organizing, staffing, and controlling are
implemented in your workplace. If you are not currently
working, you may use a previous employer. In this assignment,
you must:
· Analyze the application of these management concepts to your
place of work; the paper will not simply be a report on the five
functions in general.
· Identify specific examples and explain of how each applies to
the functions practiced in your place of work.
Be sure to integrate vocabulary learned throughout this course
8. and citations from the text to support your analysis. The paper
should be five to six pages in length and formatted according
APA style guidelines as outlined in the Ashford Writing Center.
Writing the Final Paper
The Final Paper:
1. Must be five to six double-spaced pages in length, excluding
the title and reference pages, and formatted according to APA
style as outlined in the Ashford Writing Center.
2. Must include a title page with the following:
1. Title of paper
2. Student’s name
3. Course name and number
4. Instructor’s name
5. Date submitted
3. Must begin with an introductory paragraph that has a succinct
thesis statement.
4. Must address the topic of the paper with critical thought.
5. Must end with a conclusion that reaffirms your thesis.
6. Must use at least five scholarly sources, including a minimum
of three from the Ashford University Library, in addition to the
course textbook.
7. Must document all sources in APA style, as outlined in the
Ashford Writing Center.
8. Must include a separate reference page, formatted according
to APA style as outlined in the Ashford Writing Center.
Baack, D., Reilly, M., & Minnick, C., & (2014). The five
functions of effective management(2nd ed.). San Diego, CA:
Bridgepoint Education, Inc.
Instructions:
Download and read the Regression Experiment and answer the
questions in part C of the document titled Analysis and
Interpretation.
After working through the problems, go to Lesson 8: Individual
9. Exercises 8 and answer the associated multiple choice
questions.
Regression Experiment: An Overview of Model
Development and Interpretation
THE DEMAND FOR ROSES
A. The Market for Roses
Roses have an almost universal identification with the act of
sending flowers, and consequently, they are one of the major
products that the retail florist sells either alone or as part of
floral arrangements. The wholesale cut flower supplier must
therefore be able to supply roses to retail florists. However, a
number of factors have recently affected the growth in demand
for roses. First, there has been a general breakdown of "old
country social customs" such as the tradition of always sending
flowers to funerals. Second, there has been a growth in demand
for competing products, such as carnations and wild flowers,
which live longer than roses. Likewise, there has been
increased use by retailers of other flowers that are larger and
require smaller quantities per arrangement. Also, in this
context, there has been accelerated growth in the demand for
green plants. Green plant sales only accounted for 22 percent
of the total sales for the floral industry in 1975, but had jumped
to 42 percent by 1984, and are predicted to rise above 50
percent in the 1990s. Third, the costs of growing roses have
been increasing significantly.B. Developing a Demand
Function (Model) for Roses
Matthew’s and Sons is wholesale supplier of rose to retail
florists in the Detroit metropolitan area. This firm is concerned
with developing a model that will aid in forecasting rose sales
(Qt) and developing appropriate strategies for the firm's
operations. Data on Matthew’s rose sales over the past sixteen
quarters was available from their monthly sales summaries. See
Table 1 for data on variables that Matthew and Sons thought
10. would be key to their demand analysis.
Price data for roses and carnations was available form Matthew
and Sons' billing records; unemployment data for the Detroit
area was available in the Michigan Manpower Review;
information on births, deaths, and marriages ("flower events")
was obtained from the Michigan Department of Health; average
family income is available from the U.S. Department of
Labor.C. Analysis and Interpretation
1. Your task is to use the data in Table 1 to develop a demand
model for Matthew and Sons' sales of roses. It would be a great
learning experience to try and do this on your own first, then
use my suggestions below to compare your analysis to mine.
Note: you should begin by including the price of roses
(RosePr), the price of carnations (CarnatPr) and the trend
variable (time) as well as a constant (i.e. an intercept). Then
explain the expected sign (+, -, or ?) for each of those variables
as well as any other variables you deem appropriate to include
in your model. Finally, use multiple regression to estimate the
coefficients for your model.
2. To what extent was multicollinearity a problem in
choosing an appropriate model to estimate? Use theory and
computer output to support your answer.
3. Run a regression using the following variables: price of
roses (Rosepr), the price of carnations (CarnatPr), the trend
variable (time), Births, Deaths, Wedding, Unemp, Income as
well as a constant. Describe each variable coefficient as
insignificant, significant at .10, significant at .05 or significant
at .01 and whether you used a one- or two-tail test and why.
What is the advantage of using Births, Deaths and Wedding
instead of just Events? Would it be wise to include Events and
also Births, Deaths and Wedding?
4. Interpret precisely the parameter (coefficient) associated
11. with the price of roses.
5. Calculate and interpret the price elasticity for roses. Using
elasticity formula from iMBA 501: Ep=%change in dozen /
%change in rosepr = Δdozen/ ΔQrosepr x mean rosepr/mean
dozen = price coefficient x mean rosepr/mean dozen.
6. What level of substitutability exists between roses and
carnations? Provide support for your answer.
7 What is the R2 of your model? Interpret its magnitude.
8. Is the apparent explanatory power of the model as
indicated by the value of R2 statistically significant? Do the
appropriate F-test to confirm or refute.
9. Interpret the coefficient on the trend variable. Why may
such a result have occurred?
10. Suppose the florists believe that first-quarter sales are
greater than sales in any other quarter. Create and define a
variable(s) that will allow you to test this hypothesis. Re-
estimate the equation with your new variable. Are the florists
correct about first-quarter sales?
11. What are some of the shortcomings of this model? What
variable(s) may have been omitted from this model?
Table 1
Demand for Roses Data
You can copy this data and paste directly in Excel.
Births, Deaths, and Weddings are flower events. Events =
Births + Deaths + Weddings.
CarnatPr = the price of a dozen carnations.
12. RosePr = the price of a dozen roses.
Dozen = the number of roses sold (in dozens).
Time = trend variable.
Unemp = unemployment rate.
Income = Average family income ($100 units)
Date
Births
CarnatPr
Deaths
Events
Wedding
RosePr
Dozen
Time
Unemp
Income
1992.Q3
19284
18.49
8819
40022
11919
32.26
11484
1
7.6
173.36
1992.Q4
18062
17.85
9334
36996
9600
32.54
17. 15
6.2
195.67
1996.Q3
14769
18.53
8324
33907
10814
33.69
5872
16
5.4
208.00
Questions:
Run a regression using the following variables: price of roses
(Rosepr), the price of carnations (CarnatPr), the trend variable
(time), Births, Deaths, Wedding, Unemp, Income as well as a
constant. Answer questions 1-10 based on this model.
1.
Which of the following is true of the expected coefficients in
the model?
A) Births, deaths, wedding, price of carnations, and income are
expected to be positive and the price of roses and
unemployment are expected to be negative.
B) Births, deaths, wedding, and income are expected to be
positive and the price of roses, price of carnations, and
18. unemployment are expected to be negative.
C) Price of carnations and income are expected to be positive
and births, deaths, wedding, the price of roses and
unemployment are expected to be negative.
D) none of the above
2.
The price elasticity for roses is approximately
A) -10.9
B) -3.7
C) -1.9
D) none of the above
3.
The cross price elasticity for roses and carnations is
approximately
A) 2.0
B) 3.2
C) 4.5
D) none of the above
4.
The coefficient associated with the price of roses can be
interpreted as follows: For every $1 increase in the price of a
dozen roses, there are 2526.23 dozen less sold holding all other
variables constant.
A) True
B) False
5.
The estimated coefficient for wedding is found to be
approximately
A) 0.47 and is insignificant.
B) 0.12 and is significant at the .01 level.
C) -0.29 and is insignificant.
D) none of the above.
19. 6.
The estimated coefficient for Deaths is found to be insignificant
and therefore should be dropped from the regression model.
A) True
B) False
7.
Which of the following pairs of variables exhibits the highest
degree of multicollinearity?
A) wedding and events
B) births and events
C) income and unemployment
D) none of the above.
8.
Which of the following statements best represents the
interpretation of the R2 for this model?
A) The variables included in the model explain 91.9% of the
variation in the number of dozens of roses sold.
B) The variables included in the model explain 97.9% of the
variation in the number of dozens of roses sold.
C) The variables included in the model explain 95.5% of the
variation in the number of dozens of roses sold.
D) none of the above
9.
Which of the following statements best represents the
interpretation of the explanatory power of the R2 for this
model?
A) The F-critical is greater than the F-statistic so we can reject
the null hypothesis of no explanatory power for the model.
B) The F-critical is less than the F-statistic so we cannot reject
the null hypothesis of no explanatory power for the model.
C) The F-critical is greater than the F-statistic so we cannot
reject the null hypothesis of no explanatory power for the
20. model.
D) none of the above
10.
According to the coefficient associated with the trend variable,
the number of dozens of roses sold was decreasing at a rate of
472.67% per quarter more than could be explained by changes
in the other variables.
A) True
B) False
Suppose the florists believe that first-quarter sales are greater
than sales in any other quarter. Create and define a variable(s)
that will allow you to test this hypothesis. Re-estimate the
equation with your new variable and answer question 11.
11.
The florists are correct about the first-quarter sales being
greater than sales in any other quarter.
A) True
B) False
12.
A potential shortcoming of this model could be
A) the high degree of substitutability between the price of roses
and the price of carnations
B) multicollinearity between sales of roses and the price of
carnations
C) an omitted variable such as green plant price
D) none of the above
1.
Pearson Correlation cannot be used to identify non-linear
relationships between two variables.
21. A) True
B) False
2.
Regression cannot be used to identify non-linear relationships
between two variables.
A) True
B) False
3.
Suppose you have a regression model that depicts the
relationship between sales in dollars (S) and the price of the
product in dollars (P) such that S = 10 - 2P. Which of the
following is the correct interpretation of the price coefficient
(i.e., -2)?
A) A $1 increase in price will decrease sales by $2.
B) A $1 increase in sales will occur if there is a $2 decrease in
price.
C) A 1% increase in price will cause a 2% decrease in sales.
D) A 1% increase in sales will be caused by a 2% decrease in
price.
4.
Suppose you have a regression model that depicts the
relationship between sales in dollars (S) and the price of the
product in dollars (P) such that log(S) = 10 - 2(logP). Which of
the following is the correct interpretation of the price
coefficient (i.e., -2)?
A) A $1 increase in price will decrease sales by $2.
B) A $1 increase in sales will occur if there is a $2 decrease in
price.
C) A 1% increase in price will decrease sales by 2%.
D) A 1% increase in sales will result from a 2% decrease in
price.
Situation 8.1:
22. A chemist employed by a pharmaceutical company has
developed a muscle relaxant. She took a sample of 14 people
suffering from extreme muscle constriction. She gave each a
vial of the liquid drug and recorded the time to relief (measured
in seconds)for each. She then estimated a regression model to
this data and found the following: Relief = 1283 (3.65) -
25.22Dose (2.92) - 0.86DoseSquared (2.13) where numbers in
parentheses are t-statistics for the corresponding variable
coefficient.
5.
The chemist ask you to determine if the there is a significant
relationship between dose and time to relief. Using a one-tail
test, your best answer would be:
A) Given the results provided, dose is significant at .01 level.
B) Given the results provided, dose is significant at the .05
level.
C) Given the results provided, dose is significant at the .10
level.
D) Given the results provided, dose does not significantly affect
time to relief.
6.
Suppose the chemist decides to determine if there is a quadratic
effect (e.g. DoseSquared). Should the chemist use a one-tail or
two-tail test?
A) A one-tail since the effect of increasing the dosage will
eventually taper off so expect dose squared to be negative.
B) A one-tail test since the effect of increasing the dosage will
be to accelerate time to relief so expect sign of dose squared is
positive.
C) A two-tail test since we do not know what effect an
increased dosage will have so do not know sign to expect for
dose squared.
D) One could select either a one-tail or two-tail test as long as
test at alpha = 0.01.
23. 7.
The chemist concludes that there is a quadratic effect (Dose-
squared) for the muscle relaxation medication. This conclusion
is appropriate using a two-tail test and alpha = .05.
A) True
B) False
8.
A dummy variable is used when:
A) Two variables are collinear.
B) The model is non-linear
C) The variable is categorical or qualitative.
D) The preferred variable is missing and you must use a proxy
variable.
You are hired by a major refrigerator manufacturer to estimate
the demand for their refrigerators. You use the following
independent variables in this effort.
i.
Population (POP), age 21-35, in thousands.
24. ii.
Housing starts (H), in thousands.
iii.
Refrigerator price index (P).
iv.
Disposable personal income per capita (Y), in thousands of
dollars.
v. Advertising expenditures (A), in thousands of dollars.
vi. Replacement trend (R), a replacement probability function
constructed from prior
refrigerator sales, based on a 16-year average refrigerator life,
and average age of
refrigerators in market area.
Quarterly data for the years 1997 through 2002 were used to run
the regression on refrigerator sales (in thousands). The results
are described in Tables 1 and 2 below.
TABLE 1: Regression Results
Variable
Coefficient
t-stat
POP
+0.045
1.41
H
+0.657
3.13
P
-1579.0
2.21
Y
+2254.0
1.56
A
+67.5
26. H,A
.015
Y,A
.805
POP,P
.450
H,R
.120
Y,R
.225
POP,A
.674
P,Y
.856
A,R
.022
9.
Assuming that a one-tail test is appropriate for all coefficients
and a significance level of .05, which of the variables in the
model are significant (exclude constant or intercept from
consideration)?
A) R,H,A
B) R,H,A,P
C) R,H,A,P,Y
D) R,H,A,P,Y,POP
10.
What problem can be caused by multicollinearity?
A) The inability to isolate the distinct effects of the related
independent variables.
B) Smaller than expected p-values leading to misinterpretations.
C) Smaller than expected standard errors leading to wrong
conclusions.
D) Significant t-values and a high R-squared.
27. 11.
Which of the following is the best example of the potential
issues associated with multicollineary?
A) Adjusted R-squared is less than R-squared.
B) H and R have a low correlation but both have significant
coefficients.
C) POP and Y are correlated and both have insignificant
coefficients.
12.
Suppose you check on seasonality in the sales of refrigerators
by creating the following variables: Q1 = 1 if first quarter, 0
otherwise; Q2 = 1 if second quarter, 0 otherwise; and Q3 = 1 if
third quarter, 0 otherwise. The coefficient for Q2 equals +0.5
and is statistically significant and Q4 is the “omitted” category.
Interpret Q2.
A) There are, on average, 500 more refrigerators sold in the
second quarter, holding constant the other variables.
B) There are, on average, 500 more refrigerators sold in the
second quarter than the first quarter, holding constant the other
variables.
C) There are, on average, 500 more refrigerators sold in the
second quarter than the third quarter, holding constant the other
variables.
D) There are, on average, 500 more refrigerators sold in the
second quarter than the fourth quarter, holding constant the
other variables.
1.
When testing the equality of population variances, the test
statistic is the ratio of the sample variances (or equivalently, the
ratio of the squared standard deviations).
A) True
28. B) False
2.
When we test for differences between the means of independent
populations, we can only use a one-tail test.
A) True
B) False
3.
The sample size in each independent sample must be the same if
we are to test for differences between means.
A) True
B) False
4.
A statistics professor wanted to test whether the grades on a
statistics quiz were the same for the online and resident MBA
students. The professor took a random sample of 15 students
from each course and is going to conduct a test to determine if
the VARIANCES in the grades for online and resident MBA
students are equal. For this test, the professor should use a t-test
with related or matched samples.
A) True
B) False
29. Situation 6.1.1:
Do Japanese managers have different motivation levels than
American managers? A randomly selected group of each were
administered the Sarnoff Survey of Attitudes Toward Life
(SSATL), which measures motivation for upward mobility.
Higher scores indicate more motivation. The SSATL scores are
summarized below.
Japanese Mgrs
American Mgrs
Sample Size
211
100
Mean SSATL Score
65.75
79.83
Population Std. Deviation
11.07
6.41
5.
What is the appropriate null and alternative hypothesis for
testing the question posed in Situation 6.1.1?
A) µJ - µA ≥ 0; H1:µJ - µA < 0
B) µJ - µA ≤ 0; H1: µJ - µA > 0
C) µJ - µA = 0; H1: µJ - µA ≠ 0
- -
6.
Given the following results generated in Excel, are the
variances in the sample of Japanese managers different than the
variances in the sample of U.S. managers at the .05 level of
significance?
30. Data
Level of Significance
0.05
Population 1 Sample
Sample Size
211
Sample Standard Deviation
11.07
Population 2 Sample
Sample Size
100
Sample Standard Deviation
6.41
Intermediate Calculations
F-Test Statistic
2.982491
Population 1 Sample Degrees of Freedom
210
Population 2 Sample Degrees of Freedom
99
Two-Tailed Test
Lower Critical Value
0.719629
Upper Critical Value
1.419014
p-Value
6.01E-09
31. A) Yes, there are significant differences in the sample
variances.
B) No, there are no significant differences in the sample
variances.
7.
Referring to the data, the results of the previous question, and
how the data were collected in Situation 6.1.1, which of the
following test would be most appropriate to employ?
A) Separate (unequal) variance t test for means.
B) Pooled (equal) variance t test for means
C) Paired or matched sample t test for means
D) F test for variances
8.
If we had been given the following results from Excel (ignoring
any previous findings), are motivation levels for Japanese
managers different from those of U.S. managers at the .05 level
of significance?.
Data
Hypothesized Difference
0
Level of Significance
0.05
Population 1 Sample
Sample Size
32. 211
Sample Mean
65.75
Sample Standard Deviation
11.07
Population 2 Sample
Sample Size
100
Sample Mean
79.83
Sample Standard Deviation
6.41
Intermediate Calculations
Population 1 Sample Degrees of Freedom
210
Population 2 Sample Degrees of Freedom
99
Total Degrees of Freedom
309
Pooled Variance
96.44709
Difference in Sample Means
-14.08
t-Test Statistic
-11.8092
33. Two-Tailed Test
Lower Critical Value
-1.96767
Upper Critical Value
1.967669
p-Value
8.22E-27
A) Yes, there is a significant difference in mean SSATL scores.
B) No, there is no significant difference between mean SSATL
scores.
Situation 6.1.2:
A survey was recently conducted to determine if consumers
spend more on computer-related purchases via the Internet or
store visits. Assume a sample of 8 respondents provided the
following data on their computer-related purchases during a 30-
day period. Using a .05 level of significance, can we conclude
that consumers spend more on computer-related purchases by
34. way of the Internet than by visiting stores?
Expenditures (dollars)
Respondent
In-Store
Internet
1
132
225
2
90
24
3
119
95
4
16
55
5
85
13
6
248
105
7
64
35. 57
8
49
0
9.
Refer to Situation 6.1.2. The test statistic for determining
whether or not consumers spend more on computer-related
purchases by way of the Internet than by visiting stores is
A) 0.80
B) 1.12
C) 1.76
D) 1.89
10.
If we are interested in testing whether the mean of population 1
is significantly smaller than the mean of population 2, the
A) null hypothesis should state μ1 - μ2 < 0
B) null hypothesis should state μ1 - μ2 ≤ 0
C) alternative hypothesis should state μ1 - μ2 < 0
D) alternative hypothesis should state μ1 - μ2 > 0
E) both b and d are correct