Download MIS 131 Exam 1

Lab Notes MIS 131 Exam 1
Spring, 2006
Dr. Freeman
Name:_________________________________
Section: _______
Instructions: Upload the data file to your desktop. Answer each question on a Scantron
(882) form. Turn in your Scantron form and this copy of the exam with your name on
each. There are 24 questions worth 5 points each, for 120 possible points.
The file DOWweekly.sf includes the variable DJI which contains weekly closing prices
for the Dow Jones industrial Average. Test DJI using the DIFF function to answer
questions 1-4.
1) Is DJI a random walk?
a) Yes
b) No
c) Can’t tell
2) Is the weekly change in closing prices random?
a) Yes <= Run tests for randomness from the Special/Time
Series/Descriptive… window on diff(DJI). (Use the yellow button, tabular
options, and select tests for randomness.) Note that all tests have a pvalue greater than 0.10 indicating that the null hypothesis cannot be
rejected (sample was drawn at random from a normal distribution). Note
that this result is tentative until we test for normality.
b) No
c) Can’t tell
3) Is the weekly change in closing prices normally distributed?
a) Yes <= Use the Describe/Distributions/Distribution Fitting (uncensored…)
window and select the tests for normality. Note that all tests have a pvalue greater than 0.10 so we should not reject the null hypothesis
(sample was drawn from a normal distribution).
b) No
c) Can’t tell
4) Can we reject the hypothesis that the weekly change in DJI comes from a zero mean
normal distribution at the 90% or higher confidence level?
a) Yes <= Use the Describe/Numerical Data/One Variable Analysis window
on DIFF(DJI). (Enter DIFF(DJI) in the dialog box for the window.) Select
hypothesis tests from tabular options (the yellow button). The pane
defaults to testing for a mean of zero with equality. Note that several tests
are run and all of them have p-values greater than 0.10 so we cannot
reject the null hypothesis that the sample comes from a zero mean normal
distribution.
b) No
c) Can’t be determined
Maria imports porcelain egg cups, adds personalized initials and sells sets of four cups on
the internet. She has tried different prices to see the effect on weekly sales. Data is
contained in the file EGGS.SF
5) Does pricing have a significant effect on sales?
a) Yes <= Run a simple regression with sales as dependent (Y) and price as
independent (X) variable. Check the p-value for slope and note that it is
less than 0.1, so we can reject the hypothesis that price has no effect on
sales.
b) No
c) Impossible to tell
6) Which statement is accurate?
a) The relationship between sales and price is not statistically significant.
b) Sales and price are negatively correlated. <= Look at the correlation
coefficient in the simple regression analysis.
c) Sales and price are positively correlated.
d) Sales and price are uncorrelated.
e) Increased sales means increased profits.
7) What percentage of the variation in sales is explained by the variation in price?
a) 8.43
b) 82.4
c) 69 <= Look at the R squared value in the regression analysis.
d) 4.89
e) None of the above.
8) Maria would like to maximize profits. How should she adjust her prices?
a) Increase
b) Decrease
c) Leave unchanged.
d) Can’t tell from this analysis <= We have no information on Maria’s costs so
we cannot tell anything about profits from this analysis.
Howard runs a machine shop and is interested in the relationship between manufacturing
defects and the speed of operating his punch press. He has collected the data shown in
file Howard.sf
9) The data is best explained by regressing defects against
a) Speed
b) Speed^2
c) Speed^3 <= Run simple regression with defects as the dependent and
speed^n as the independent variable, with n = 1, 2, 3. Observe R squared
as n is varied from 1 to 3.
d) Any of the above
10) Can Howard expect to maintain his average defect rate below 2% (20 per 1000) if he
keeps the speed below 100?
a) Yes <= Look at the plot of fitted model chart. A speed of 100 corresponds
to 10^6, which is 1 on the X axis. The model indicates an expected value
just below .02. You can right click on the pane and use the locate function
to determine this.
b) No
c) Can’t tell
Howard has run his machine at a steady speed of 100 and collected defect rate data as
shown in the variable Drate. This is proportional data taken with sample size of 500.
11) Is the process average defect rate below 2%?
a) Yes
b) No <= Run a p-chart with the Drate data, using 500 as the “subgroup”
size. Note that the mean value is 00233….
c) Can’t tell
12) Is Howard’s machine operation in statistical control?
a) Yes
b) No <= Note that the upper control limit is exceeded.
c) Can’t tell
Whack! Energy drink is supposed to have 100 mg of caffeine in each 12 oz. can. Recent
data for the caffeine in Whack! Is listed in the file, Whacko.sf.
13) Is the caffeine content in statistical control using subgroup size 5?
a) Yes <= Use the Special/Quality Control/Variables Control Chart/X-Bar and
R window to analyze caffeine with a subgroup size of 5. Note that the
subgroups all fall within the control limits.
b) No
c) Can’t tell
14) Is the process for controlling caffeine stationary?
a) Yes
b) No <= From the X-Bar chart the data obviously has a discontinuous
mean value. You could also look at caffeine in the special/time
series/descriptive methods window and note the change in mean value.
c) Can’t tell
15) Is their specific cause variation in the process?
a) Yes <= There is an obvious shift in the mean value.
b) No
c) Can’t tell
16) How does the specific cause variation appear?
a) Trend
b) Cycle
c) Jump in Mean <= It’s clear in the charts.
d) Change in Variance
e) It does not appear
A local fast foods enterprise recently conducted a survey to determine customer
preferences for drink and entrée by age group and by gender. The results are shown in
the file Diner.sf.
17) Entrée is related to:
a) Age only
b) Gender only
c) Age and gender
d) Age and drink <= Use the Describe/Categorical Data/Crosstabulations
window. Enter Entree and test against age, drink and gender. You need
to run a Chi Squared test on each pairing. Use the tabular options (the
yellow button) and select Chi Square test. Note that the null hypothesis
can be rejected for age and drink but not for gender based on p-values.
e) None of the above
18) Drink is related to:
a) Age only
b) Age and gender
c) Entrée only <= Run crosstabs with drink against age, entrée and gender
an note the p-values in the chi squared test.
d) Entrée, age and gender
e) None of the above
19) A promotion aimed at teens should include
a) Burgers and cola <= Run cross tabs with age to determine entrée
preferences for teens, then run cross tabs with entrée to determine drink
preference for entrée. You need to go to the data table to determine that
teens are age group 2 and burgers are entrée item 1 and that cola is item
1 on the drinks list.
b) Chicken and root beer
c) Fish and milk
d) Can’t determine
e) None of the above
20) A promotion aimed at mature adults should include
a) Burgers and root beer
b) Chicken and coffee <= Mature adults are group 4 and they prefer item 3 on
the entrée list, chicken. Chicken eaters are group 3 on the list, and they
prefer item 3 on the drinks list, coffee, by a wide margin.
c) Fish and cola
d) Can’t determine
e) None of the above
The file Diameter.sf contains data for a sample of 100 items produced by a calibration
run of a laser guided drill.
21) Can we reject the hypothesis that the sample came from a process with a mean value
of 1.0?
a) Yes
b) No <= Use the Describe/Numerical Data/One Variable Analysis.. window.
Select hypothesis test from the tabular options (yellow button). Use the
pane options pop-up (right click on the pane) to set the mean value to 1
and make sure that alpha is .05 and the alternative hypothesis is “not
equal”. The high p-value indicates that we should not reject the null
hypothesis, the mean is equal to 1.0.
c) Can’t determine
22) The process has an estimated standard deviation of about
a) .02
b) .01 <= From the summary statistics.
c) .03
d) .10
e) Can’t tell
23) The standard deviation for the estimate of the mean for this sample is about
a) .02
b) .002
c) .01
d) .001 <= Use the formula S = σ/sqrt(n), where n is the sample size and σ
is the population standard deviation. Plug in the result from 22) and the
sample size.
e) .0001
24) How many total samples of size 100 are needed to reduce the standard deviation for
the estimate of the mean diameter to one tenth of the value indicated above?
a) 10
b) 100 <= Because of the inverse square root dependence, you would need
100 times the data to reduce the standard deviation of the mean by a
factor of 1/10.
c) 1000
d) 10000
e) Can’t tell