wealth age region37 50 M24 88 U14 64 A13 63 U13 66 .docx

wealth age region
37 50 M
24 88 U
14 64 A
13 63 U
13 66 U
11.7 72 E
10 71 M
8.2 77 U
8.1 68 U
7.2 66 E
7 69 M
6.2 36 O
5.9 49 U
5.3 73 U
5.2 52 E
5 77 M

5 73 M
4.9 62 A
4.8 54 U
4.7 63 U
4.7 23 U
4.6 70 O
4.6 59 E
4.5 96 E
4.5 84 O
4.5 40 E
4.3 60 U
4 77 E
4 68 E
4 83 E
4 68 A
4 40 E
4 62 M
4 69 E

4 49 A
3.9 64 A
3.9 83 A
3.8 41 A
3.8 78 A
3.6 80 A
3.5 68 O
3.4 67 U
3.4 71 O
3.4 54 A
3.3 62 E
3.3 69 A
3.3 58 U
3.2 71 U
3.2 55 O
3 66 E
3 65 E
3 50 U

3 64 E
3 57 A
3 86 M
3 71 E
3 68 E
3 68 E
3 54 U
2.8 68 A
2.8 76 E
2.8 52 E
2.8 73 O
2.8 46 O
2.7 69 U
2.7 63 E
2.6 42 E
2.6 67 E
2.6 62 O
2.6 66 U

2.6 75 U
2.5 74 E
2.5 73 E
2.5 84 M
2.5 49 A
2.4 60 U
2.4 71 O
2.4 76 A
2.4 67 E
2.3 54 A
2.3 57 U
2.3 54 O
2.3 64 O
2.2 85 E
2.2 45 A
2.2 39 O
2.2 54 E
2.1 68 U

2.1 85 U
2 70 M
2 102 M
2 38 U
2 73 A
2 91 E
2 82 U
2 74 M
2 81 M
2 * U
2 62 E
2 62 U
2 67 U
2 80 O
2 68 M
2 80 U
2 * U
2 60 E

2 74 O
1.9 48 U
1.9 60 E
1.9 43 E
1.9 64 O
1.9 67 U
1.8 62 A
1.8 90 E
1.8 66 U
1.8 68 A
1.8 60 A
1.8 53 A
1.8 47 E
1.8 86 U
1.8 67 A
1.7 54 U
1.7 77 E
1.7 61 U

1.7 83 E
1.7 61 U
1.7 58 U
1.7 64 U
1.7 53 A
1.7 67 A
1.6 57 E
1.6 62 A
1.6 * E
1.6 64 O
1.6 69 A
1.6 71 E
1.6 54 U
1.6 78 A
1.5 45 U
1.5 69 U
1.5 59 U
1.5 * A

1.5 82 O
1.5 68 E
1.5 41 E
1.5 60 E
1.5 64 E
1.5 44 E
1.5 7 E
1.5 72 E
1.5 56 E
1.5 60 E
1.4 61 E
1.4 79 O
1.4 42 O
1.4 63 E
1.4 49 E
1.4 56 E
1.4 67 U
1.4 75 E

1.4 43 M
1.4 61 U
1.4 54 O
1.4 47 E
1.4 64 U
1.4 52 A
1.4 73 A
1.3 83 U
1.3 64 E
1.3 71 O
1.3 71 E
1.3 61 M
1.3 83 E
1.3 43 E
1.3 47 U
1.3 79 E
1.3 53 E
1.3 73 U

1.3 72 U
1.3 72 U
1.3 59 A
1.3 77 E
1.3 68 E
1.3 42 E
1.3 61 U
1.2 69 A
1.2 82 O
1.2 * E
1.2 56 U
1.2 42 M
1.2 63 U
1.2 75 U
1.2 * E
1.2 59 A
1.2 70 E
1.2 46 M

1.2 68 U
1.2 68 A
1.2 69 A
1.2 68 O
1.2 64 A
1.1 53 E
1.1 79 E
1.1 49 E
1.1 47 U
1.1 75 U
1.1 76 M
1.1 66 U
1.1 85 U
1.1 66 O
1.1 70 U
1.1 58 E
1.1 72 E
1.1 52 M

1 52 O
1 79 E
1 69 A
1 52 M
1 75 E
1 62 E
1 65 M
1 63 U
1 87 E
1 61 U
1 58 O
1 60 E
1 67 O
1 80 E
1 63 U
1 9 M
1 59 E
1 * E

1 * O
Sheet1DateExportRefinery OutputJan-04283.92246.01Feb-
04241.7237.15Mar-04142.66249.35Apr-04331.02237.72May-
04197.33269.92Jun-04210.95285.3Jul-04256.03227.27Aug-
04268.59226.86Sep-04114.05129.92Oct-04203.37226.18Nov-
04165.71220.87Dec-04308.34235.21Jan-05270230Feb-
05137232Mar-05309250Apr-05184248May-05322270Jun-
05199240Jul-05246250Aug-05237255Sep-05226236Oct-
05287254Nov-05320261Dec-05313277Jan-06313229Feb-
06216258Mar-06217260Apr-06316199May-06215226Jun-
06200231Jul-06269248Aug-06216234Sep-06291219Oct-
06234270Nov-06192277Dec-06275197Jan-07181219Feb-
07176146Mar-07149238Apr-07270253May-07266230Jun-
07196222Jul-07253141Aug-07237230Sep-07216176Oct-
07112194Nov-07217191Dec-07187187Jan-08246191Feb-
08157174Mar-08187187Apr-08160208May-08263208Jun-
08195195Jul-08113177Aug-08240197Sep-08179201Oct-
08172153Nov-08231192Dec-08153231Jan-09277231Feb-
09182177Mar-09183236Apr-09268193May-09158189Jun-
09176204Jul-09228204Aug-09260216Sep-09237194Oct-
09165.2196.7Nov-09101174Dec-09210.597204.42Jan-
10236.584253.788
Sheet2
Sheet3
STAT 202 Miller
Name___________________________
Lab 10-Additional Significance Tests
November 29, 2012
T Tests/Confidence Intervals Using StatCrunch

Some of the following questions utilize various types of t tests.
You have already used a lot of these features. Remember that
all tests are under STATT Statistics.
Paired T test: Use for Related/matched sample test when the
original data is given.
T test-One Sample: Use for a regular one sample test or for
related/matched samples when the differences/changes are
given.
T test-Two Samples: Use for two independent samples. Make
sure that you uncheck the box that says, “Pool Variances.”
-From the menu select the appropriate test. (Recall that “With
data” is used when the data is entered into a column. “With
summary” is used when you are given the necessary summary
statistic/parameter values.)
-Select “Next” to choose a significance test OR confidence
interval. Enter all necessary information and select
“Calculate”.
-StatCrunch will display the standard error (sampling error),
degrees of freedom, the test statistic (t), the P-value for the test
statistic and the lower and upper limits for a confidence
interval.
---------------------------------------------------------------------------
----------------------------------------------------------
For problems 1 & 2:
a) Determine how many samples you are looking at (1 or 2). If
there are two samples decide what type of samples (related or
independent) you are looking at.
b) State your hypotheses (the null hypothesis and the alternative
hypothesis).
c) Decide which test statistic you are going to use depending on
your answers to a) and b). Find its value and the p-value.
Please use StatCrunch to do this and copy and paste your
results.

d) State a conclusion based on your answers to a)-c) and the
significance level α=.05. (This must contain an explanation
supported by your decision to reject or accept H0 and a
discussion of the statistical significance of your findings.)
e) Calculate the 95 % confidence interval. Please use
StatCrunch to do this and copy and paste your results.
1. Researchers are interested in looking at the heights of
professional football players compared to professional
basketball players. They suspect that basketball players are
generally taller than football players. The researchers measured
the heights of a random sample of 45 professional football
players and a random sample of 40 basketball players. The data
is on Blackboard.
a)
b) H0:
Ha:
c)
d)
e)
3. A political group was interested in if there was a difference
between the mean percentage of republican voters and
democratic voters in the Midwest. To look at this they used the
17 counties in Indiana as their samples (gathering data from
various polling locations) and looked at the mean percentage of
Democrat voters and the mean percentage of Republican voters
in each county in a recent election. This data is on Blackboard.
a)

b) H0:
Ha:
c)
d)
e)
Z tests/Confidence Intervals for Proportions Using StatCrunch
Some problems will require you to use significance tests for
proportions. Select the appropriate test by going to
STAT->Proportions (One Sample or Two Sample).
-“With Data” means that you have the actual data values entered
into a column in StatCrunch. This is categorical data so each
data value will be one of the given categories. (StatCrunch will
automatically determine the sample proportion(s), sample
size(s) and standard error. )
-“With Summary” means that you are given the number of
successes (X) or sample proportions and the sample size(s).
-Click “Next” to either enter the correct hypotheses or to
calculate a confidence interval. For a test based on one sample,
enter the appropriate population proportion you are comparing
the sample against. Then select the appropriate alternative
hypothesis. Two sample tests only require that you choose the
correct alternative hypothesis. For a confidence interval, select
the level and just use the “Standard” method.

-When you click “Calculate” the test statistic value and its P-
value are reported and a confidence interval is reported by a
lower and upper limit.
---------------------------------------------------------------------------
---------------------------------------------------------------------------
-----------------
3. Gallup poll results released on November 29,
2012:“Democrats, Republicans Diverge on Capitalism, Federal
Gov't
Little difference on small business, free enterprise,
entrepreneurs”
(http://www.gallup.com/poll/158978/democrats-republicans-
diverge-capitalism-federal-gov.aspx)
Gallup collected information to see how Republicans and
Democrats feel about various terms commonly associated with
politics. The data for “Capitalism” is on Blackboard. Please
note that “1” indicates a “Positive” view and “0” represents a
“Negative” view.
Does the data support the poll’s headline (specifically the part
about capitalism)? Please use a hypothesis test and include a
discussion of statistical significance. You need to include your
StatCrunch results below as well.
H0:
Ha:

4. Reported by Gallup on June 22, 2012
Headline: “Majority of Egyptians Want Military Out of Politics”
Is the headline supported by the given poll results? You will
want to look at the original article to get all of the necessary
details (http://www.gallup.com/poll/155303/Majority-
Egyptians-Military-Politics.aspx). Please use a hypothesis test
and include a discussion of statistical significance. You need to
include your StatCrunch results below as well.
H0:
Ha:
STAT 202 Miller
Name___________________________
November 29, 2012

given.
“Calculate”.
interval.
---------------------------------------------------------------------------
----------------------------------------------------------
For problems 1 & 2:
hypothesis).
results.

is on Blackboard.
a)
b) H0:
Ha:
c)
d)
e)
a)
b) H0:

Ha:
c)
d)
e)

---------------------------------------------------------------------------
---------------------------------------------------------------------------
-----------------
Gov't
entrepreneurs”
H0:
Ha:

H0:
Ha:
STAT 202 Miller
Name___________________________
November 29, 2012

given.
“Calculate”.
interval.
---------------------------------------------------------------------------
----------------------------------------------------------
For problems 1 & 2:
hypothesis).
results.

is on Blackboard.
a)
b) H0:
Ha:
c)
d)
e)
a)
b) H0:
Ha:

c)
d)
e)

---------------------------------------------------------------------------
---------------------------------------------------------------------------
-----------------
Gov't
entrepreneurs”
H0:
Ha:

H0:
Ha:
STAT 202Thursday September 13, 2012 Miller
Name______________________________
Lab 3: Scatter Plots
Please submit your completed lab on Blackboard.
Part 1: More with Assessing NormalityBillionaires (data file is
on Blackboard-Data-billionaires.txt)
With the usual definition of normal, being a billionaire is not at
all normal. If we examine the (relatively) small subset of the
world’s population, which includes the world’s billionaires
from 1992, some of the demographics may have some Normality
to them. Here you will examine the distributions for these

demographics. Fortune magazine reported the following
demographics for the billionaires: wealth (in billions), age
(*=unknown), the region where they are from (Asia, Europe,
Middle East, United States, or Other).
1. Begin by examining the distribution of each variable. Give a
brief description of the overall pattern of each distribution
below (For quantitative variables you need to discuss shape,
center, and spread. For categorical variables you need to
discuss percentages.)
· Wealth-
· Age-
· Region-
2. To get a complete look of how well the distribution of the
billionaires’ ages fit with the standard normal distribution look
at the Normal quantile plot (Graphics-QQ plot). Include the
plot below.
3. What conclusions can you make about the distribution of the
billionaires’ ages based on the plot in #2 and why? Keep in
mind that the horizontal axis corresponds to Z.
4. Also, how do your conclusions relate to what we have
discussed about the correlation, r? Please include an estimation
of what type of value you think that r would have.
5. There are three very obvious outliers that do not fit with the
pattern that the rest of the data follows. Identify the ages of the
outliers. Explain why they appear as outliers in the quantile
plot by looking back at the distribution of ages. Also explain
why, logically, it makes sense for these individuals to be
outliers.
Part 2-More with Scatter Plots (data file is on Blackboard: fuel
oil-Bahrain)
The given data was reported by the UN Statistics Division as

part of the JODI (Joint Oil Data Initiative-
http://www.jodidata.org/WJODI.shtm). JODI was formed in
2003 with “the objective of improving the quality and
transparency of international oil statistics.” Basically, most of
the world felt that it was fair to be able to see data that supports
the frequent fluctuations in oil prices. Over 90 countries now
contribute data. This data set shows the available information
about the amount of fuel oil that Bahrain exported (“Export”)
and the amount of refined fuel oil Bahrain produced (“Refinery
Output”) over several years. Both are measured in thousand
metric tons.
6. I want you to examine the relationship between “Export” and
“Refinery Output.” Treat “Refinery Output” as the explanatory
variable. Display the data together with a scatterplot.
7. Find the value of the correlation between “Export” and
“Refinery Output.”
8. What conclusions can you make based on your scatterplot and
calculated correlation value? Please be very specific and
include both a statistical interpretation and a practical
interpretation.
9. There is an outlier in your data set. It is the data value for
July 2007 when the Refinery Output was 141,000 metric tons
and the Export was 253,000 metric tons. What happens to the
correlation value when this point is removed? Do you notice
any changes in the pattern of the scatterplot?

Name_______________________________
Lab #1: Intro to StatCrunch-Graphs and Descriptive Statistics
STAT 202 Miller
Thursday August 30, 2012
Please review the file under “A Brief Guide to StatCrunch and
Lab Submissions” (posted on Blackboard) while completing this
lab. You will use StatCrunch to answer the following
questions.
Your lab should be submitted as a Word document. Please copy
and paste any graphs that you create into your lab document.
Any responses must be easy to read and labeled with the
appropriate problem number. Please save your solutions to
your g:/ drive (or your own computer) and submit the lab via
Blackboard.
Background: A common psychological experiment tests
numerical memory-a subjects ability to repeat back a string of
numbers. For this particular experiment two trials were
conducted with each of 52 subjects; number strings were
delivered both verbally and visually. The data was collected
from 4 universities and online between July 2010 and December
2010. Along with detailed information pertaining to the
experiment (such as the place, date and time of the experiment,
and type of delivery), the experimenters collected information
about the subject (age and gender) and recorded the longest
number of digits that the subject was able to repeat back to
them (Limit #).
1. Begin by loading the data into StatCrunch. Use the data file
that I have provided (numericalmemory.txt) and follow the

directions provided in “Intro to StatCrunch and Labs.” Make
sure that you select “comma” as your delimiter. Once the file is
loaded save your data to your G drive so that you will be able to
access it again in the future.
2. Classify the variables “Affiliation”, “Gender”, “Age”, “Trial
Num”, and “Limit#” as categorical or quantitative.
3. To get an idea of where the subjects were tested make a pie
chart for “Affiliation.” (Graphics-Pie Chart-with data, Select
the “Affiliation” column and click next to look at other options
and give the graph a title before clicking “Create Graph!”.)
4. To take a look at the distribution of the genders of the
subjects tested make a frequency table (Stat-Tables-Frequency)
and bar graph (Graphics-Bar Plot-with data) for the ”Gender”
data. Note that each subject is listed twice so the frequencies
listed are twice as large as the true values but the relative
frequencies are accurate.
5. Make a stem plot of the “Age” data (Graphics-Stem and
Leaf). Describe the overall pattern of the distribution of “Age.”
6. Now make two histograms (with different bin (interval)
widths) of the “Limit#” data (Graphics-Histogram). You can
designate a starting point and bin width or StatCrunch will
automatically select these for you. After selecting the “Limit#”
column click “next” to set your options.
7. How does the bin width affect the shape (skew, symmetry,
etc.) of the distribution?
8. Make a box plot for the “Limit#” data (Graphics-Boxplot).
You have the option to orient the plot vertically or horizontally:
Click on “Next” when in the Boxplot menu and select (or do not
choose to select) “Draw boxes horizontally.”

9. Find the mean, standard deviation, variance, median, and
quartiles for the Limit# data (Stat-summary stats-column stats).
Select the “Limit#” column and then click “next.” Click on the
summary stats name to deselect it. Copy and paste your results.
10. Based on all of your graphs and calculations for the
“Limit#” data, what can you conclude about the overall
pattern/shape of the distribution of “Limit#”? Is it symmetric,
skewed or neither? (I suggest comparing the two measures of
center as well as using your calculations of spread and how they
relate to the center to support your final conclusion here.)
11. Now I want you to look at the distributions for “Limit#”
separately for the Males and for the Females (using the default
binwidth). To do this choose to Group by “Gender” (there is a
pull-down menu on the first screen) and check the box next to
“Separate Graph for Each Group.” Compare the two
distributions.

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