Civic Engagement Workbook for Statistics
University of Minnesota, Morris
Fall, 2002
Welcome! This project is a collaboration between Center for Small Towns staff and University of Minnesota, Morris faculty in the Statistics Department. Two courses, Statistics 1601: Introduction to Statistics and Statistics 2601: Mathematical Statistics, are now integrated with a civic engagement component that allows for the survey and analysis of rural statistical data. As a member of these introductory courses, you will be involved in the compilation of variables, the analysis of rural indicators, and the creation of a data book for people working in communities and counties across West Central Minnesota.
This Civic Engagement Workbook will be used
as a supplement for The Introduction to the Practice of Statistics (4^{th}
ed) by Dr. David S. Moore and Dr. George P. McCabe. This workbook will follow the textbook and
provide problem sets for each chapter.
The primary data source for this workbook is the 2000 US Census. The
Census Bureau and the Minnesota Planning data repository web sites, which
contain a wealth of data about the area, will be used throughout these problem
sets.
Homework turned in through the use of this
book must be completed in an approved word processing software program such as
MS Word or Wordperfect. While the data
may need to be written out on paper (see the templates in the appendix), the
final results of the problem sets will require a copy and paste from a
statistical software package, such as SPSS or WebStat into a format that can be
brought together with fellow students’ results. Please turn in both the written and typed results as the
assignments become due.
This
supplement has been created and edited by Dr. Engin Sungur, David Borgerding,
and Benjamin Winchester at the Center for Small Towns, University of Minnesota,
Morris,
600
E. 4^{th} Street, Morris, MN, 56267.
TABLE OF CONTENTS
Introduction to the Civic Engagement
Workbook
Data Sources Used in the Workbook
Minnesota Planning Datanet Web
Regional Economic Information System
Environmental Protection Agency
Counties and Cities in West Central Minnesota
Chapter 1: Looking at
Data – Distributions
Section 1.1. Displaying
Distributions with Graphs
Section 1.2. Describing
Distributions with Numbers
Section 1.3. The Normal
Distributions
Chapter 2: Looking at Data  Relationships
Section 2.3. LeastSquares
Regression
Section 2.4. Cautions about
Regression and Correlation
Section 2.5. An
Application: Exponential Growth and
World Oil Production
Section 2.6. Relations in
Categorical Data
Section 2.7. The Question of
Causation
Section 3.2. Design of
Experiments
Section 3.4. Toward Statistical
Inference
Chapter 4: Probability
and Inference
Section 4.4. Means and
Variances of Random Variables
Section 4.5. General
Probability Rules
Chapter 5: From
Probability to Inference
Section 5.1. Sampling
Distributions for Counts and Proportions
Section 5.2. The Sampling
Distribution of a Sample Mean
Chapter 6: Introduction to Inference
Section 6.1. Estimating with
Confidence
Section 6.2. Tests of
Significance
Section 6.3. Use and Abuse of
Tests
Section 6.4. Power and
Inference as a Decision
Chapter 7: Inference for
Distributions
Section 7.1. Inference for the
Mean of a Population
Section 7.2. Comparing Two
Means
Section 7.3. Optional Topics in
Comparing Distributions
Chapter 8: Inference for Proportions
Section 8.1. Inference for a
Single Proportion
Section 8.2. Comparing Two
Proportions
Chapter 9: Inference for
TwoWay Tables
Section 9.1. Inference for
TwoWay Tables
Section 9.2. Formulas and
Models for TwoWay Tables
Chapter 10: Inference for Regression
Section 10.1. Simple Linear
Regression
Section 10.2. More Detail about
Simple Linear Regression
Chapter 11: Multiple Regression
Chapter 12: OneWay Analysis of Variance
Chapter 13: TwoWay
Analysis of Variance
Chapter 14: Nonparametric
Tests
Section 14.1. Wilcoxon Rank Sum
Test
Section 14.2. The Wilcoxon
Signed Rank Test
Section 14.3. The
KruskalWallis Test
Chapter 15: Logistic Regression
APPENDIX A. Sample data sheets
1.1 Age Histogram Cities
1.1 Liquor Sales Stemplot Counties
1.1 Age Histogram Counties
1.1 Population Time Plot County
1.2 Age Mean Cities
Median
Mode
1.2 Age Mean County
Median
Mode
1.2 Liquor Sales Per Capita Interquartile Range Counties
Outliers
Standard Deviation
Boxplot
1.3 Average Household Size Normal Distribution County
Cities
2.1 Education: Bachelor’s Degree Scatterplot Cities
2.1 Median Household Income Scatterplot Cities
2.1 Population Scatterplot Cities
2.2 Alcohol Use (12^{th} Grade) Correlation r Counties
Driving Under the Influence of Alcohol or Drugs (12^{th}
Grade)
2.2 Total Part I Offenses LeastSquares
Regression Line State
2.3 Median Home Value LeastSquares Regression Line Cities
2.3 Median Household Income LeastSquares Regression
Line Cities
2.4 Total Part I Offenses LeastSquares
Regression Line State
2.4 Total Part I Offenses LeastSquares
Regression Line County
2.5
2.6 Age Marginal
Distribution State
Conditional Distribution
2.6 Age Marginal
Distribution County
Conditional Distribution
2.7 Educational Attainment Causation Cities
2.7 Median Household Income Causation Cities
2.7 Median Age Confounding Cities
2.7 Median Household Income Confounding Cities
3.1
3.2
3.3 Guesses Stemplot,
Dot Plot, Mean
Standard Deviation
3.4
4.1
4.2 Population, Age, and Sex Probability State
4.2 American Indian and Alaskan Native Probability State
4.2 Population, Age, and Sex Probability County
4.2 American Indian and Alaskan Native Probability County
4.3 Householder Mobility Probability State
Discrete
Probability Distribution
4.4
4.5 Suicide Probability State
4.5 Motor Vehicle Injuries Probability State
5.1 Arrests and Apprehensions Mean State
Standard
Deviation
Normal
Approximation
5.1 Arrests and Apprehensions Mean County
Standard
Deviation
Normal
Approximation
5.2
5.3
6.1 Burglary Crimes _{} State
Standard
Deviation of x
Confidence Interval
for _{}
6.1 Burglary Crimes _{} County
Standard
Deviation of x
Confidence
Interval for _{}
6.2 Burglaries Reported Stating
Hypothesis State
Test
Statistic
Pvalue
Value
z
6.2 Burglaries Reported Stating
Hypothesis County
Test
Statistic
Pvalue
Value
z
6.3
6.4
7.1 Grand Total Crimes Reported Stemplot State
Normal
Quantile Plot
Confidence
Interval
7.1 Grand Total Crimes Reported Stemplot County
Normal
Quantile Plot
Confidence
Interval
7.2 Grand Total Crimes Reported TwoSample t Statistic State
7.2 Grand Total Crimes Reported TwoSample t Statistic County
7.3 Grand Total Crimes Reported F Statistic State
7.3 Grand Total Crimes Reported F Statistic State
8.1
8.2 Lung Cancer Hypotheses State
Test
Statistic
Pvalue
Confidence
Interval
8.2 Lung Cancer Hypotheses County
Test
Statistic
Pvalue
Confidence
Interval
9.1 Lung Cancer ChiSquare Test State
Test
Statistic
Degrees
of Freedom
Pvalue
9.1 Lung Cancer ChiSquare Test State
Test
Statistic
Degrees
of Freedom
Pvalue
9.2 Type of Cancer Statistical Significance State
9.2 Type of Cancer Statistical Significance County
10.1
10.2 Median Value of Homes (dollars)
Median Household Income
(dollars) Regression Analysis Cities
LeastSquares
line
Significant
Test for Slope
Confidence Interval for
Slope
11.1 Total Part I Offenses Fitted Regression
Equation State
ANOVA
F Test
Test
Statistic
Degrees
of Freedom
Pvalue
Residuals
11.1 Total Part II Offenses Fitted Regression
Equation State
ANOVA F Test
Test
Statistic
Degrees
of Freedom
Pvalue
Residuals
11.1 Total Part I Offenses Fitted Regression
Equation County
ANOVA
F Test
Test
Statistic
Degrees
of Freedom
Pvalue
Residuals
11.1 Total Part II Offenses Fitted Regression
Equation County
ANOVA
F Test
Test
Statistic
Degrees
of Freedom
Pvalue
Residuals
12.1 Total Vandalism Table of Means State
Table
of Standard Deviations
ANOVA
F
Statistic
Pvalue
12.1 Total Vandalism Table of Means County
Table
of Standard Deviations
ANOVA
F
Statistic
Pvalue
13.1 Smoking & Tobacco Use Marginal
Means State
TwoWay
ANOVA
13.1 Smoking & Tobacco Use Marginal
Means County
TowWay ANOVA
The following pages provide a screenshot of the data sources that are (and can be) used in this workbook. Many of the problems will require you to look up the same variable (such as average household size) for the cities in your region. To accomplish this, we recommend that as you find the data for one city, you enter it into a table, such as the one below.
County 
Variable_{1} (example: age) 
Variable_{2} (example: income) 
Variable_{3} 
Becker 



Clay 



Douglas 



Grant 



Otter Tail 



Pope 



Stevens 



Traverse 



Wilkin 











































The first column is the area, such as a list of cities or counties, that you are interested in gathering data for. If you are asked to gather data for the county, then enter the county names. For cities, enter the city names. Then as you use the web page to display the variable you are looking at, fill in the appropriate cell to the right. This will be a handy way to compile the data in one easy format. These tables can be made in a word processor or a spreadsheet program. If you need help creating these, contact the teaching assistant.
A template for this data can be found in the appendix.
http://factfinder.census.gov/servlet/BasicFactsServlet
http://www.mnplan.state.mn.us/datanetweb/
http://www.bea.doc.gov/bea/regional/reis/
http://www.mnwfc.org/lmi/lmi4.htm
http://fisher.lib.virginia.edu/cbp/
Environmental data – www.epa.gov
Please circle the group that you will be working with.
Region
1
Becker (7) –
Audubon, Callaway, Detroit Lakes, Frazee, Lake Park, Ogema, Wolf Lake
Region
2
Grant (7) – Ashby, Barrett, Elbow Lake, Herman, Hoffman, Norcross, Wendell
Region 3
Pope (10) – Brooten, Cyrus, Farwell, Glenwood, Long Beach, Lowry, Sedan, Starbuck, Villard, Westport
Stevens (5) – Alberta, Chokio, Donnelly, Hancock, Morris
Traverse (4) – Browns Valley, Dumont, Tintah, Wheaton
Wilkin (9) – Breckenridge, Campbell, Doran, Foxhome, Kent, Nashua, Rothsay*, Tenney, Wolverton
West Central
Minnesota has a total of 85 unique cities in 9 counties.
* The city of Rothsay overlays two counties, Otter Tail and Wilkin. Students in Region 3, covering Wilkin County, will be responsible for getting the data for the entire city. To do this you must combine the data from both counties to get an accurate picture of the entire city.
Chapter Objectives (course)
Technique:
Stemplots, Histograms, Time plots.
Objective (civic learning):
Data Information:
http://www.mnplan.state.mn.us/datanetweb/
Warmup Activity
1. Produce a histogram of the Age
variable for each City in your region.
Main Activity
1. Produce a stemplot for Liquor Sales that
includes all of the Counties in West Central Minnesota: Becker County, Clay County, Douglas County,
Grant County, Otter Tail County, Pope County, Stevens County, Traverse County,
and Wilkin County using the variable Liquor Sales Per Capita for 1996 using the
web page http://www.mnplan.state.mn.us/datanetweb/
.
2. Produce a histogram of the Age variable
for each County in your region using the General
Characteristics: Population and Housing
for 2000 at http://factfinder.census.gov
.
3. Produce a time plot for the Population
for each County in your region using the web page http://www.mnplan.state.mn.us/datanetweb/
and go to Census 2000 SF1 Data and under Population Profiles
a. Select Population in 1970, 1980, 1990, 2000
b.
Select Report
c.
For the Choose an Area, choose County, and then hit Enter choice
d.
Select your county and make a time plot for the population from 1970 to
2000.
Technique:
Mean, Median, Quartiles, Interquartile range, The fivenumber summary and boxplots, Standard deviation.
Objective (civic learning):
Data Information:
http://www.mnplan.state.mn.us/datanetweb/
Warmup Activity
1. Calculate the mean, median, mode and
standard deviation of the Median Age for all of the Cities
in your region.
Main Activity
1. Calculate the mean, median, mode and
standard deviation of Median Age for all of the Counties
in West Central Minnesota (not just those in your region).
2. A. Calculate the interquartile range of the
variable Liquor Sales Per Capita for 1996 for the following Counties
in Minnesota: Becker County,
Clay County, Douglas County, Grant County, Otter Tail County, Pope County,
Stevens County, Traverse County, and Wilkin County using http://www.mnplan.state.mn.us/datanetweb/
2. B. From 2.A., are there are any possible
Outliers for Liquor Sales Per Capita?
2. C. From 2.A., calculate the Standard Deviation
for Liquor Sales Per Capita.
3. Produce a Boxplot of the variable Liquor
Sales Per Capita for 1996 that includes the following Counties in
Minnesota: Becker County, Clay County,
Douglas County, Grant County, Otter Tail County, Pope County, Stevens County,
Traverse County, and Wilkin County using http://www.mnplan.state.mn.us/datanetweb/
Technique:
Density curves, Normal Distributions, The 689599.7 rule, Standardizing observations, Normal quartile plots, Normal quantile plots, Density estimation.
Objective (civic learning):
Data Information:
http://www.mnplan.state.mn.us/datanetweb/
Warmup Activity
The variable of interest for this problem is the Average Household Size. This activity will look all Counties in West Central Minnesota. To get the correct data, use the following steps:
1. Calculate the mean and standard deviation of
the Average Household Size in counties across West Central Minnesota.
2. What is the area under the standard normal
curve to the left of the first quartile?
Use this to find the value of the first quartile for a standard normal
distribution. Find the third quartile
similarly.
3. Compute the zscore for each of the
counties.
4. What is the value of the interquartile range
for the standard normal distribution?
5.A. What proportion of counties are within 1
standard deviation of the mean?
5.B. What proportion of counties are within 2
standard deviations of the mean?
5.C. What proportion of counties are within 3
standard deviations of the mean?
6. Compare the proportions found in #5(A,B,C)
with the 689599.7 rule.
7. What is the proportion of counties with an
Average Household Size less than 3?
Main Activity
The variable of interest for this problem is the Average Household Size. This activity will look all Counties in West Central Minnesota. To get the correct data, use the following steps:
1. Calculate the mean and standard deviation of
the Average Household Size in cities across West Central Minnesota.
2. Compute the zscore for each of the cities.
3.A. What proportion of cities are within 1
standard deviation of the mean?
3.B. What proportion of cities are within 2
standard deviations of the mean?
3.C. What proportion of cities are within 3
standard deviations of the mean?
4. Compare the proportions found in #3(A,B,C)
with the 689599.7 rule.
5. What is the proportion of cities with an
Average Household Size less than 3?
Chapter Objectives (course):
Technique:
Scatterplots,
Adding categorical variables to scatterplots, Scatterplot smoothers.
Objective (civic learning):
Data Information:
http://www.mnplan.state.mn.us/datanetweb/
Warmup Activity
1.A. For each City in your region, produce
a scatterplot for the variable Percentage of Population age 25+ with a Bachelors
Degree (or higher) against Median Household Income. Be aware of the scale on each axis. What is the incremental unit
(10/100/10,000)?
For Bachelors Degree:
For Median Household Income:
1.B. Describe the relationship that you see.
Main Activity
1.A. For each City in your region,
produce a scatterplot for the variable Population in 2000 against
Median Household Income.
For Population:
For Median Household Income:
1.B. Describe the relationship that you see.
Technique:
Correlation r.
Objective (civic learning):
Data Information:
http://www.mnplan.state.mn.us/datanetweb/
Warmup Activity
Main Activity
1. Produce a graph and calculate the correlation r for the two variables Alcohol Use (12^{th }grade) and Driving Under the Influence of Alcohol or Drugs (12^{th }grade) for the following counties in Minnesota: Becker County, Clay County, Douglas County, Grant County, Otter Tail County, Pope County, Stevens County, Traverse County, and Wilkin County. To get the correct data, use the following steps:
A. Is there a correlation?
B. If so, describe the correlation
(What direction and strength?).
Technique:
Fitting a line to data, Prediction, Leastsquares regression, r^{2}
Objective (civic learning):
Data Information:
http://www.mnplan.state.mn.us/datanetweb/
Warmup Activity
1.
Main Activity
1. Using all of the Cities in
your region, make a scatterplot of Median Home Value against Median
Household Income. Describe the
form and direction of the relationship.
2. Construct a LeastSquares Regression Line
for each county in your region.
To get the correct data, use the following steps:
3. Interpret the coefficient and the
constant.
4. Predict the income a person must have to
afford a $100,000 home in your region.
Technique:
Residuals, Outliers and influential observations.
Objective (civic learning):
Data Information:
http://www.mnplan.state.mn.us/datanetweb/
Warmup Activity
1. Construct a LeastSquares Regression Line
for the State of Minnesota for the variable Total Part 1 Offenses
Crimes Reported from 1985 to 1999 using http://www.mnplan.state.mn.us/datanetweb/. To get the correct data, use the following
steps:
a.
Go to the web
site and select the category Justice Profiles
b.
Select the
category Reported Crimes
c.
Select County and
highlight all of the years (You can do this by clicking and holding down the
shift key).
d.
Use the
variable Total Part 1 Offenses and put the Total Part 1 Offenses on the yaxis
and the years from 1985 to 1999 on the xaxis.
e.
Determine a
LeastSquares Regression Line of Total Part 1 Offenses Crimes Reported against
Time
A. Compute the residuals and make a plot of the
residuals against x. Describe carefully
the pattern displayed by the residuals.
Main Activity
1. Construct a LeastSquares Regression Line
for each County in your region for the variable Total Part 1 Offenses
Crimes Reported from 1985 to 1999 using http://www.mnplan.state.mn.us/datanetweb/
. To get the correct data, use the following
steps:
A. Compute the residuals and make a plot of the
residuals against x. Describe carefully
the pattern displayed by the residuals.
Technique:
The nature of exponential growth, The logarithm transformation
Objective (civic learning):
Data Information:
Warmup Activity
1.
Main Activity
1.
Technique:
Marginal distributions, Conditional distributions, Simpson’s paradox, The perils of aggregation.
Objective (civic learning):
Data Information:
Warmup Activity
1. A.
Find the marginal distribution of Age for the State
of Minnesota (in percent) and make a bar graph of the distribution. To do the warmup activity, you must first
go to the web site http://factfinder.census.gov. To get the correct data, use the following
steps:
1. B. Find the conditional distribution of age for
the state of Minnesota (in percent) and make a bar graph of this distribution.
1. C. Briefly describe the most important
difference between the two age distributions.
Main Activity
1. Find the marginal distribution of Age for
each County in your region (in percent) and make a bar graph of
the distribution. To do this activity,
you must first go to the web site http://factfinder.census.gov. To get the correct data, use the following
steps:
B. Find the conditional distribution of age for
your county (in percent) and make a bar graph of this distribution.
C. Briefly describe the most important
difference between the two age distributions.
Technique:
Causation, Common
response, Confounding.
Objective (civic learning):
Data Information:
Main Activity
1.A. For each City in your region,
produce a scatterplot for the variable Educational Attainment against
Median Household Income.
For Educational Attainment data:
For Median Household Income data:
1.B. Describe the relationship that you see between Educational Attainment and Median Household Income. Does a high income cause a high level educational attainment or does educational attainment cause a higher income?
2.A. For each City in your region,
produce a scatterplot for the variable Median Age against Median
Household Income.
For Median Age data:
For Median Household Income data:
2.B. Describe the relationship that you see between Age and Household Income. Does this mean that as you get older you will make more money? Is this guaranteed? Explain your response to these questions.
Chapter Objectives (course):
Technique:
Sampling, Experiments.
Objective (civic learning):
Data Information:
Warmup Activity
1.
Main Activity
1.
Extension
Technique:
Comparative
experiments, Randomization, Matched pairs design, Block designs.
Objective (civic learning):
Data Information:
Warmup Activity
1.
Main Activity
1. Results from polls and other statistical studies reported in a newspaper or magazines often emphasize the point that the samples were randomly selected. Why the emphasis on randomization? Couldn't a good investigator do better by carefully choosing respondents to a poll so that various interest groups were represented? Perhaps, but samples selected without objective randomization tend to favor one part of the population over another. For example, polls conducted by sports writers tend to favor the opinions of sport fans. This leaning toward one side of the issue is called sampling bias. In the long run, random samples seem to do a good job of producing samples that fairly represent the population. In other words, randomization reduces sampling bias.
Question: How do random samples compare
to subjective samples in terms of sampling bias?
Technique:
Simple random
samples, Stratified samples, Multistage samples.
Objective (civic learning):
Data Information:
Warmup Activity
1.
Main Activity
1. This is the data that we have obtained from one of our courses on "Random Fortunes".
Student Number 
Initial Guess 
Subjective (Judgmental) Sample Average 
Random Sample Average 
1 
16 
14.4 
10.6 
2 
10 
10.8 
7

3 
18 
14 
6 
4 
15 
15.4 
7 
5 
18 
10 
5 
6 
12 
10 
4 
7 
10 
10.8 
4.8 
8 
15 
10.6 
3.2 
9 
12 
12.6 
5.8 
10 
13 
12.2 
7.9 
11 
12 
14 
5.6 
12 
12 
10.5 
9.2 
13 
12 
12.8 
7 
14 
9 
9.4 
5.7 
15 
18 
14.4 
7.6 
16 
16 
11.4 
8.2 
17 
18 
18 
10.2 
18 
10 
10.2 
8.2 
19 
12 
9.4 
6.6 
20 
10 
10.2 
8.4 
Table X:
A. First look at guesses and the
averages of subjective samples of 5 from each member of the class. Display the
two sets of data on separate stemplots or dot plots. Comment upon the shape of
these distributions and where they center. Why is the center an important point
to consider?
a) Look at the averages from the random samples
of size 5 and construct a stemplot or a dot plot. How does this plot compare
with the plots of the guessed values and the averages from the subjective
samples in terms of center? In terms of spread.
b) From the data the instructor has provided
for the whole class, calculate the mean of the sample averages for the
subjective samples and for the random samples. How do the centers of the
distributions of means compare?
c)
Calculate the
standard deviation of the averages for the subjective and for the random
samples. How do the spreads of the distributions of means compare?
d) Having studied two types of sampling,
subjective and random, which method do you think is doing the better job? Why?
Technique:
Sampling
variability, Sampling distributions, Capturerecapture sampling.
Objective (civic learning):
Data Information:
Warmup Activity
1.
Main Activity
1.
Chapter Objectives (course):
Technique:
Randomness, The uses
of probability.
Objective (civic learning):
Data Information:
Warmup Activity
1.
1.
Technique:
Samples spaces,
Intuitive probability, Finite number of outcomes, Equally likely outcomes.
Objective (civic learning):
Data Information:
4.2.1: http://factfinder.census.gov
Warmup Activity
1. What is the probability of a male between 25
and 44 years of Age residing in the State of Minnesota (in
percent)? To get the correct data, use
the following steps:
2. What is the probability of an American
Indian and Alaskan Native male between 25 years old to 44 years old for
Minnesota (in percent) using the tables below.
Main Activity
1. What is the probability of a
male between 25 and 44 years of Age residing in each of the Counties
in your region (in
percent)? To get the correct data, use the following steps:
a.
Go the web
site http://factfinder.census.gov
.
b.
Under Show Me,
select Population, Age, and Sex in the Census 2000 Geographic Comparison Tables
for the variable
c.
Select the
type of district (State  County)
d.
Select the
state (Minnesota) and press Go.
2. What is the probability of an American Indian
and Alaskan Native male between 25 years old to 44 years old for your county
(in percent) using the tables below.
American Indian and Alaska
Native 

Geographic Area 
Percent 
Minnesota 
1.1 
Becker County 
7.5 
Clay County 
1.4 
Douglas County 
0.2 
Grant County 
0.2 
Ottertail County 
0.3 
Pope County 
0.5 
Stevens County 
0.2 
Traverse County 
2.8 
Wilkin County 
0.4 
Table: American Indian and Alaska Native in
percent.
Geographic Area 
Total Population 
Percent of total population 
Median age (years) 
Males per 100 females 

Under 18 years 
18 to 24 years 
25 to 44 years 
45 to 64 years 
65 years and over 
All ages 
18 years and over 

Minnesota 
4,919,479 
26.2 
9.6 
30.4 
21.8 
12.1 
35.4 
98.1 
95.6 
Becker County 
30,000 
26.6 
7.1 
24.9 
24.9 
16.4 
39.4 
99.4 
97.8 
Clay County 
51,229 
25.0 
17.1 
25.7 
19.3 
12.9 
32.3 
93.7 
89.1 
Douglas County 
32,821 
24.0 
9.2 
25.0 
23.8 
17.9 
39.7 
99.0 
96.9 
Grant County 
6,289 
23.9 
6.9 
23.1 
23.2 
22.9 
42.5 
94.5 
94.3 
Otter Tail County 
57,159 
24.9 
7.2 
24.2 
24.7 
19.0 
41.1 
100.4 
97.8 
Pope County 
11,236 
24.8 
6.7 
23.1 
23.8 
21.5 
42.1 
96.9 
92.9 
Stevens County 
10,053 
21.6 
20.8 
21.6 
19.0 
17.0 
33.9 
93.9 
91.0 
Traverse County 
4,134 
25.3 
5.6 
21.7 
21.2 
26.2 
42.9 
96.7 
93.8 
Wilkin County 
7,138 
27.8 
7.0 
27.7 
21.5 
16.1 
38.1 
95.3 
96.1 
Table: Total population and percent of total
population for the geographic area.
Technique:
Discrete random
variables, Continuous random variables, Normal distributions as probability
distributions.
Objective (civic learning):
Data Information:
4.3.1: http://factfinder.census.gov
Warmup Activity
1.
Main Activity
1. Using the table below.
A. For Minnesota, what is the
probability that a Householder which Moved into their unit between
1980 to 1989 still lives in their unit at 1999 to March 2000?
B. Check that this is a legitimate discrete
probability distribution.
C. Find P(X >= 1980 to 1989).
D. Find P(X > 1980 to 1989).
Year Householder Moved into Unit (Minnesota)
Years 
Probability 
1999 to March 2000 
0.037 
1995 to 1998 
0.044 
1990 to 1994 
0.062 
1980 to 1989 
0.075 
1970 to 1979 
0.090 
1969 or earlier 
0.100 
Table: The year the householder moved into the unit
for the state of Minnesota.
Technique:
The mean of a
random variable, Statistical estimation and the law of large number, The
variance of a random variable.
Objective (civic learning):
Data Information:
Warmup Activity
1.
Main Activity
1.
Technique:
Conditional
probability, Tree diagrams, Decision analysis
Objective (civic learning):
Data Information:
http://www.mnplan.state.mn.us/datanetweb/
Warmup Activity
1.
Main Activity
1. Use the table below for the following
questions about the State of Minnesota for 1999:
A. What is the probability that a randomly
selected suicide victim is less than 20 years old?
B. What is the probability that a randomly
selected motor vehicle injury is under 20 years of age?
C. What is the conditional probability that a fatal
injury occurred, given that the individual was under 20 years of age?
State of Minnesota:
Morbidity and Utilization
Description 
<20 Years 
20+ Years 
Number 
Number 

Fatal Injuries 
203 
1,560 
Motor Vehicle Injuries 
121 
512 
Suicide 
39 
399 
Table: Fatal Injury, Motor Vehicle, and Suicide cases for 1999 in the state of Minnesota.
Chapter Objectives (course):
5.1 Counts and Proportions
5.2 Sample Means
Technique:
The binomial
distributions for sample counts, Binomial distributions in statistical
sampling, Finding binomial probabilities, Binomial mean and standard deviation,
Sample proportions, Normal approximation for counts and proportions.
Objective (civic learning):
Data Information:
http://www.mnplan.state.mn.us/datanetweb/
Warmup Activity
1. Use the information from below about Arrests
and Apprehensions in the State of Minnesota:
Arrests and Apprehensions in the state of Minnesota for 1999:
Population in 1999
= 4,919,479
Arrests &
Apprehensions in 1999 = 269,395
Individuals
involved in an Arrest or Apprehension in 1999 = 5.48%.
A. What are the mean and the standard deviation
of the number X of arrests and apprehensions?
B. Use the normal approximation to find the
probability that at least 269,395 arrests and apprehensions will occur.
C. What is the probability that more than
269,395 arrests and apprehensions will occur.
D. If the state of Minnesota grows to
5,500,000 by the year 2010, what is the probability that more than 301,400
arrests and apprehensions will occur.
Main Activity
1. For each County in your
region:
A. What are the mean and the standard deviation
of the number X of Arrests and Apprehensions?
B. Use the normal approximation to find the
probability that at least the year 1999 arrests and apprehensions will occur.
C. What is the probability that more than the
year 1999 arrests and apprehensions will occur.
D. If your county grows on average of
11.8% every ten years what will be the total population by the year 2010, what
is the probability that more than the year 2010 arrests and apprehensions will
occur.
To get the correct
data for arrests and apprehensions, use the following steps:
b.
Select Justice
Profiles
c.
Select Arrests
and Apprehensions
d.
Select the
type of district (county)
e.
Select 1999
for year and then select Total
f.
Scroll to the
bottom of the page and get the Grand Total/Total for arrests and
apprehensions. The arrests and apprehensions
have gone up on an average of 10.6% every ten years.
To get the correct
data for the population, use the following steps:
Technique:
The mean and
standard deviation of x, The sampling distribution of x, the central limit
theorem, Weibull distributions.
Objective (civic learning):
Data Information:
Warmup Activity
1.
Main Activity
1.
Technique:
Statistical process
control.
Objective (civic learning):
Data Information:
Warmup Activity
Main Activity
Extension
Chapter Objectives (course):
6.1. Estimating with Confidence
6.2. Tests of Significance
6.3. Use and Abuse
of Tests (excluding Power and Inference as Decision)
Technique:
Statistical
confidence, Confidence Intervals, Confidence interval for a population mean,
Choosing the sample size.
Objective (civic learning):
Data Information:
http://www.mnplan.state.mn.us/datanetweb/
1. In the State of Minnesota, for
Reported Burglary Crimes:
A. What is sigma x, the standard
deviation of xbar?
B. Give a 95% confidence interval for
mu .
To get the correct
data, use the following steps:
1. In each County of your region,
for Reported Burglary Crimes:
A. What is sigma x, the standard deviation of
xbar?
B. Give a 95% confidence interval for
mu .
To get the correct
data, use the following steps:
Technique:
Stating hypothesis,
Test statistics, Pvalues, Tests for a population mean, Twosided significance
tests and confidence intervals.
Objective (civic learning):
Data Information:
http://www.mnplan.state.mn.us/datanetweb/
1. In the State
of Minnesota, we want to compare the number of Burglaries
Reported for 1990 and 1999 and see if there is a significant difference
between the years:
A. State the appropriate H_{o}
and H_{a}.
B. Carry out the test. Give the Pvalue, and then interpret the
results in plain language.
C. Calculate the value z of the z test
statistic.
To get the correct
data, use the following steps:
1. For each County in your
region, we want to compare the number of Burglaries Reported for
1990 and 1999 and see if there is a significant difference between the years:
A. State the appropriate Ho and Ha.
B. Carry out the test. Give the Pvalue, and then interpret the
results in plain language.
C. Calculate the value z of the z test
statistic.
To get the correct
data, use the following steps:
Technique:
Objective (civic learning):
Data Information:
1.
1.
Technique:
Power, Increasing
the power, Two types of error, Error probabilities
Objective (civic learning):
Data Information:
1.
1.
Chapter Objectives (course):
7.1 Inference for the Mean of a Population Mean
7.2 Comparing two means
Technique:
The t
distributions, Onesample t confidence interval, One sample t test, Power of
the t test, Sign test.
Objective (civic learning):
Data Information:
http://www.mnplan.state.mn.us/datanetweb/
1. We will examine Reported Crimes for
the State of Minnesota from 1985 to 1999:
A. Display the data with a stemplot and a
normal quatntile plot. Describe the
distribution.
B. Give a 95% confidence interval for
the mean number of Grand Total crimes reported.
To get the correct
data, use the following steps:
1. We will examine Reported Crimes for
each County in your region from 1985 to 1999:
A. Display the data with a stemplot and a
normal quatntile plot. Describe the
distribution.
B. Give a 95% confidence interval for
the mean number of Grand Total crimes reported.
To get the correct
data, use the following steps:
Technique:
The twosample z
statistic, Twosample t procedures, Twosample t significance test, Twosample
t confidence interval, Software approximation for the degrees of freedom,
Pooled twosample t procedures.
Objective (civic learning):
Data Information:
http://www.mnplan.state.mn.us/datanetweb/
1. For Reported Crimes in the State
of Minnesota from 1985 to 1999:
Group the years as
the first five years, 1985 to 1992 and the second five years, 1993 to 1999. Compare the two five year periods to see if
there is a significant difference between the mean amounts of Grand Total
crimes reported. Does the data show a
significant difference between the two means?
Give the null and alternative hypothesizes, and calculate the twosample
t statistic. Obtain the Pvalue. State your practical conclusions.
To get the correct
data, use the following steps:
1. For Reported Crimes in each County
of your region from 1985 to 1999:
Group the years as
the first five years, 1985 to 1992 and the second five years, 1993 to
1999. Compare the two five year periods
to see if there is a significant difference between the mean amounts of Grand
Total crimes reported. Does the data
show a significant difference between the two means? Give the null and alternative hypothesizes, and calculate the
twosample t statistic. Obtain the
Pvalue. State your practical
conclusions.
To get the correct
data, use the following steps:
Technique:
Inference for
population spread, The F test for equality of spread, The power of the
twosample ttest.
Objective (civic learning):
Data Information:
http://www.mnplan.state.mn.us/datanetweb/
1. For the state of
Minnesota from 1985 to 1999:
Group the years as
the first five years, 1985 to 1992 and the second five years, 1993 to
1999. Compare the two five year periods
to see if there is significant evidence of inequality between the standard
deviations of the two populations of Grand Total crimes reported.
A. State the null and alternative
hypothesis.
B. Calculate the F statistic. Between which two levels does the Pvalue
lie?
To get the correct
data, use the following steps:
1. For your county from 1985 to 1999:
Group the years as
the first five years, 1985 to 1992 and the second five years, 1993 to
1999. Compare the two five year periods
to see if there is significant evidence of inequality between the standard
deviations of the two populations of Grand Total crimes reported.
A. State the null and alternative
hypothesis.
B. Calculate the F statistic. Between which two levels does the Pvalue
lie?
To get the correct
data, use the following steps:
Chapter Objectives (course):
8.1 Inference for a single
proportion
8.2 Comparing two proportions
Technique:
Confidence interval
for a single proportion, Significance test for a single proportion, Choosing a
sample size.
Objective (civic learning):
Data Information:
1.
1.
Technique:
Confidence
intervals, Significance tests, Relative risk.
Objective (civic learning):
Data Information:
http://www.mnplan.state.mn.us/datanetweb/
1. For Lung Cancer in the year 2000:
The state of
Minnesota wants to know if there is a difference in Deaths versus Survival
between Male and Female for lung cancer.
Here are the data where n is the number of lung cancer cases
diagnosed and X(Deaths) is the number of lung cancer deaths:
State of Minnesota
Population 
n 
X(Deaths) 
Male 
4,313 
3,725 
Female 
3,061 
2,582 
Table X: Lung Cancer survival and deaths for males
and females for the state of Minnesota.
A.
Give the null and alternative hypotheses that are appropriate for this
problem assuming that we have no prior information suggesting that one
population would have a higher preference than the other.
B.
Test the null hypothesis. Give
the test statistic, the Pvalue, and summarize the results.
C. Give a 90% confidence interval for
the difference in proportions.
1. For Lung Cancer in the year 2000:
Your county wants to know if there is a difference in Deaths versus Survival between Male and Female for lung cancer. Here are the data where n is the number of lung cancer cases diagnosed and X(Deaths) is the number of lung cancer deaths:
Becker County 
Clay County 

Population 
n 
X(Deaths) 
Population 
n 
X(Deaths) 
Male 
38 
27 
Male 
50 
34 
Female 
31 
28 
Female 
22 
16 
Douglas County 
Grant County 

Population 
n 
X(Deaths) 
Population 
n 
X(Deaths) 
Male 
35 
28 
Male 
5 
5 
Female 
22 
21 
Female 
3 
3 
Otter Tail County 
Pope County 

Population 
n 
X(Deaths) 
Population 
n 
X(Deaths) 
Male 
64 
51 
Male 
15 
14 
Female 
44 
30 
Female 
9 
9 
Stevens County 
Traverse County 

Population 
n 
X(Deaths) 
Population 
n 
X(Deaths) 
Male 
11 
8 
Male 
9 
7 
Female 
7 
2 
Female 
5 
3 
Wilkin County 


Population 
n 
X(Deaths) 


Male 
11 
9 


Female 
4 
1 

Table X: Lung Cancer survival and deaths cases for
males and females by the demographic area.
A.
Give the null and alternative hypotheses that are appropriate for this
problem assuming that we have no prior information suggesting that one
population would have a higher preference than the other.
B.
Test the null hypothesis. Give
the test statistic, the Pvalue, and summarize the results.
C. Give a 90% confidence interval for
the difference in proportions.
Chapter Objectives (course):
9.1 Inference for twoway tables
Technique:
The twoway table,
Expected cell counts, The chisquare test, The chisquare test and the z test.
Objective (civic learning):
Data Information:
http://www.mnplan.state.mn.us/datanetweb/
1. For Lung Cancer in the year 2000:
The state of
Minnesota wants to know if there is a significant difference for Male and
Female in Lung Cancer Deaths for the individuals Diagnosed for the year
2000. Use the data from below:
Lung Cancer Deaths
for those Diagnosed 
Gender 

Male 
Female 

Yes 
3,725 
2,582 
No 
588 
479 
Table: Lung Cancer Deaths for
those Diagnosed by Gender for the state of Minnesota.
A. Make a table that includes
the following information for each group (Male or Female): total number of individuals diagnosed with
Lung Cancer in the year 2000, the proportion of Lung Cancer Deaths for those
Diagnosed, and the standard error for the proportion.
B. Perform the chisquare test
for this twoway table. Give the test
statistic, the degrees of freedom, the Pvalue, and your conclusion.
1. For Lung Cancer in the year
2000:
Your county wants to know if there is a significant difference for Male and Female in Lung Cancer Deaths for the individuals Diagnosed for the year 2000. Use the data from below:
Becker County 
Clay County 

Population 
Male 
Female 
Population 
Male 
Female 
Yes 
27 
28 
Yes 
34 
16 
No 
11 
3 
No 
16 
6 
Douglas County 
Grant County 

Population 
Male 
Female 
Population 
Male 
Female 
Yes 
28 
21 
Yes 
5 
3 
No 
7 
1 
No 
0 
0 
Otter Tail County 
Pope County 

Population 
Male 
Female 
Population 
Male 
Female 
Yes 
51 
30 
Yes 
14 
9 
No 
13 
14 
No 
1 
0 
Stevens County 
Traverse County 

Population 
Male 
Female 
Population 
Male 
Female 
Yes 
8 
2 
Yes 
7 
3 
No 
3 
5 
No 
2 
2 
Wilkin County 


Population 
Male 
Female 


Yes 
9 
1 


No 
2 
3 

Table X: Lung Cancer Deaths for those Diagnosed by
Gender for the demographic area.
A. Make a table that includes the following
information for each group (Male or Female):
total number of individuals diagnosed with Lung Cancer in the year 2000,
the proportion of Lung Cancer Deaths for those Diagnosed, and the standard
error for the proportion.
B. Perform the chisquare test for this twoway
table. Give the test statistic, the degrees
of freedom, the Pvalue, and your conclusion.
Technique:
Computations,
Computing conditional distributions, Computing expected cell counts, Computing
the chisquare statistic, Testing independence for the second model.
Objective (civic learning):
Data Information:
http://www.mnplan.state.mn.us/datanetweb/
1. For Cancer in the year 2000:
The state of
Minnesota wants to know if there is a significant difference for Deaths
Occurred for the Type of Cancer the individual has been diagnosed. Use the data from below:
The following table
classifies the women by the Type of Cancer the individual has been diagnosed
with and whether of not a death occurred from that type of cancer.
State of Minnesota

Cancer Survival for Females 

Type of Cancer 
Dead 
Survived 
Breast 
2176 
7333 
Colon & Rectum 
1439 
2163 
Lung 
2582 
479 
Other 
6499 
6679 
Table X: Type of Cancer by Cancer Survival for
Females for the state of Minnesota.
A. Carry out a complete analysis of the
association between Cancer Deaths for Women and the Type of Cancer, including a
description of the association and an assessment of its statistical
significance. Summarize your
conclusions.
1. For Cancer in the year 2000:
Your county wants
to know if there is a significant difference for Deaths Occurred for the Type
of Cancer the individual has been diagnosed.
Create a table that
classifies the women by the Type of Cancer the individual has been diagnosed
with and whether of not a death occurred from that type of cancer.
To get the correct
data, use the following steps:
A. Create a table that classifies the women by
the Type of Cancer the individual has been diagnosed with and whether of not a
death occurred from that type of cancer.
B. Carry out a complete analysis of the association
between Cancer Deaths for Women and the Type of Cancer, including a description
of the association and an assessment of its statistical significance. Summarize your conclusions.
Chapter Objectives (course):
Statistical model
for linear regression, Estimating the regression parameters, Confidence
intervals and significance tests, Confidence intervals for mean response,
Prediction intervals.
Objective (civic learning):
1.
1.
Analysis of
variance for regression, The ANOVA F, Calculations for regression inference,
Preliminary calculations: Inference for
slope and intercept, Confidence intervals for the mean response and prediction
intervals for a future observation, and Inference for correlation.
Objective (civic learning):
Data Information:
http://www.mnplan.state.mn.us/datanetweb/
1. A.
Plot the Median Value of Homes (dollars) versus City and plot the Median
Household Income (dollars) versus City.
Describe the pattern. Are there
any outliers or unusual towns?
B. Perform the regression analysis and
summarize the results. Give the
leastsquares line and the results of the significance test for the slope.
C. Test the null hypothesis that the slope is
zero and describe your conclusion.
D. Give a 95% confidence interval for
the slope.
To get the correct
data, use the following steps:
a.
Go to the web
page http://www.mnplan.state.mn.us/datanetweb/
.
b.
Select Census
2000 SF3 Data
c.
Under Housing
Characteristics, select Value
d.
Then select
City and look at Median (dollars)
e.
Repeat for all
of the following cities: Audubon,
Callaway, Detroit Lakes, Frazee, Lake Park, Ogema, Wolf Lake, Barnesville,
Comstock, Dilworth, Felton, Georgetown, Glyndon, Hawley, Hitterdal, Moorhead,
Sabin, Ulen, Alexandria, Brandon,
Carlos, Evansville, Forada, Garfield, Kensington, Millerville, Miltona, Nelson,
Osakis, Ashby, Barrett, Elbow Lake, Herman, Hoffman, Norcross, Wendell, Battle
Lake, Bluffton, Clitherall, Dalton, Deer Creek, Dent, Elizabeth, Erhard, Fergus
Falls, Henning, New York Mills, Ottertail, Parkers Prairie, Pelican Rapids,
Perham, Richville, Rothsay*, Underwood, Urbank, Vergas, Vining, Wadena,
Brooten, Cyrus, Farwell, Glenwood, Long Beach, Lowry, Sedan, Starbuck, Villard,
Westport, Alberta, Chokio, Donnelly, Hancock, Morris, Browns Valley, Dumont,
Tintah, Wheaton, Breckenridge, Campbell, Doran, Foxhome, Kent, Nashua,
Rothsay*, Tenney, and Wolverton.
f.Then, go to
the web page http://www.mnplan.state.mn.us/datanetweb/
g.
Select Census
2000 SF3 Data
h.
Under Economic
Characteristics, select Income in 1999
i. Select City and look at Median Household
Income (dollars)
j. Repeat for all of the cities from above
Chapter Objectives (course):
Population multiple
regression equation, Multiple linear regression model, Estimation of the
multiple regression parameters, Confidence intervals and significance tests for
regression coeffients, ANOVA table for multiple regression, Squared multiple
correlation R^2, Preliminary analysis, Residuals, Regression using all
variables, Multiple logistic regression.
Objective (civic learning):
http://www.mnplan.state.mn.us/datanetweb/
1. For your state:
A. Run the multiple linear regression using
Year, Total Part I Offenses, and Total Part II Offenses to predict
Burglary. Give the fitted regression
equation.
B. Give the null and alternative
hypotheses for the ANOVA F test. Report the results of this test, giving the
test statistic, degrees of freedom, Pvalue, and conclusion.
C. What percent of the variation in Burglary is
explained by this multiple regression?
D. Summarize the results of the
significance tests for the individual regression coefficients.
E. Analyze the residuals and summarize
your conclusions.
To get the correct
data, use the following steps:
a.
Go to the web site http://www.mnplan.state.mn.us/datanetweb/
b.
Select Justice Profiles
c. Select Arrests and Apprehensions
d.
Under Select Area, and select State of
Minnesota
e.
Highlight or select all of the years
available (1985 to 1999).
1. For your county:
A. Run the multiple linear regression using
Year, Total Part I Offenses, and Total Part II Offenses to predict
Burglary. Give the fitted regression
equation.
B. Give the null and alternative
hypotheses for the ANOVA F test. Report the results of this test, giving the
test statistic, degrees of freedom, Pvalue, and conclusion.
C. What percent of the variation in Burglary is
explained by this multiple regression?
D. Summarize the results of the
significance tests for the individual regression coefficients.
E. Analyze the residuals and summarize
your conclusions.
To get the correct
data, use the following steps:
Chapter
Objectives (course):
Comparing means,
The twosample t statistic, ANOVA hypothesis, ANOVA model, Estimates of
population parameters, Testing hypothesis in oneway ANOVA, The F test,
Multiple comparisons, Software, Power.
Objective (civic
learning):
Data
Information:
12.1: http://www.mnplan.state.mn.us/datanetweb/
1. For the State of Minnesota:
A. Make a table of means and standard
deviations for the three different time periods, and plot the means.
B. State H_{o} and H_{a}
for an ANOVA on these data, and explain in words what ANOVA tests in this
setting.
C. Using computer software, run the
ANOVA. What is the F statistic and it’s
Pvalue? Give the values of s_{p}
and r^{2}. What do you
conclude?
State of Minnesota
Time Period 
Total Vandalism 

1985 to 1989 
5647 
5693 
5524 
5647 
5634 
1990 to 1994 
7163 
6505 
7181 
7454 
8430 
1995 to 1999 
8344 
8573 
8269 
8799 
8115 
Table 12.1: Time Period by Total
Vandalism for the state of Minnesota.
1. For your county:
A. Make a table of means and standard
deviations for the three different time periods, and plot the means.
B. State Ho and Ha for an ANOVA on
these data, and explain in words what ANOVA tests in this setting.
C. Using computer software, run the
ANOVA. What is the F statistic and it’s
Pvalue? Give the values of Sp
and R^2. What do you conclude?
To get the correct
data, use the following steps:
a. Go
to the web site http://www.mnplan.state.mn.us/datanetweb/
b. Select Justice Profiles
c. Select Arrests and Apprehensions
d. Under Select Area, select County
e. Select your county
f. Highlight or select all of the years available (1985 to 1999)
g. Under Part II Offenses, look at Total Vandalism
h. Scroll down to get all of the data.
Chapter Objectives (course):
The twoway ANOVA
model, The ANOVA table for twoway ANOVA.
Objective (civic learning):
http://www.mnplan.state.mn.us/datanetweb/
1. For the state of Minnesota:
The means for the
number of cases of Smoking & Tobacco Use are:
Group 
1994 
1996 
1999 
Smoking & Tobacco Use
(9^{th} Grade) 
12.4 
17.5 
20.5 
Smoking & Tobacco Use
(12^{th} Grade) 
22.4 
26.0 
34.0 
Table X: Means for the number of cases of Smoking
& Tobacco Use (9^{th} Grade) and Smoking & Tobacco Use (12^{th}
Grade) for the state of Minnesota with year.
* Number of cases
per 100
A. Plot the group means with year on the x axis
and number of cases of smoking & tobacco use on the y axis. For each group connect the points for the
different year.
B. Describe the patterns you see. Does there appear to be a difference between
the two groups? Does smoking &
tobacco use appear to vary with year?
If so, how does it vary? Is
there an interaction between group and year?
C. Compute the marginal means. Find the differences between the Smoking
& Tobacco Use (9^{th}) and Smoking & Tobacco Use (12^{th})
mean number of cases for each group.
Use this information to summarize numerically the patterns in the plot.
1. For each county in your region, determine
the mean for the number of cases of Smoking & Tobacco Use for 9^{th}
and 12^{th }grade.
A. Plot the group means with year on
the x axis and number of cases of smoking & tobacco use on the y axis. For each group connect the points for the
different year.
B. Describe the patterns you see. Does there appear to be a difference between
the two groups? Does smoking &
tobacco use appear to vary with year?
If so, how does it vary? Is
there an interaction between group and year?
C. Compute the marginal means. Find the differences between the Smoking
& Tobacco Use (9^{th}) and Smoking & Tobacco Use (12^{th})
mean number of cases for each group.
Use this information to summarize numerically the patterns in the plot.
To get the correct
data, use the following steps:
Chapter Objectives:
Technique:
The rank transformation, The
Wilcoxon rank sum test, The normal approximation.
Objective (civic learning)
1.
1.
Technique:
Objective (civic learning):
1.
1.
Technique: The
KruskalWallis test.
Objective (civic learning):
1.
1.
Chapter Objectives (course):
Technique :
Binomial
distributions and odds, Inference for logistic regression, Multiple logistic regression.
Objective (civic learning):
1.
1.
County 
Variable: 
Variable: 
Variable: 
Becker 



Clay 



Douglas 



Grant 



Otter Tail 



Pope 



Stevens 



Traverse 



Wilkin 



Data Entry Sheet
for Region 1 Cities
City 
Variable: 
Variable: 
Variable: 
Becker County 



Audubon 



Callaway 



Detroit Lakes 



Frazee 



Lake Park 



Ogema 



Wolf Lake 



Clay County 



Barnesville 



Comstock 



Dilworth 



Felton 



Georgetown 



Glyndon 



Hawley 



Hitterdal 



Moorhead 



Sabin 



Ulen 



Douglas County 



Alexandria 



Brandon 



Carlos 



Evansville 



Forada 



Garfield 



Kensington 



Millerville 



Miltona 



Nelson 



Osakis 



Data Entry Sheet
for Region 2 Cities
City 
Variable: 
Variable: 
Variable: 
Grant County 



Ashby 



Barrett 



Elbow Lake 



Herman 



Hoffman 



Norcross 



Wendell 



Otter Tail County 



Battle Lake 



Bluffton 



Clitherall 



Dalton 



Deer Creek 



Dent 



Elizabeth 



Erhard 



Fergus Falls 



Henning 



New York Mills 



Ottertail 



Parkers Prairie 



Pelican Rapids 



Perham 



Richville 



Underwood 



Urbank 



Vergas 



Vining 



Wadena 



Data Entry Sheet
for Region 3 Cities
City 
Variable: 
Variable: 
Variable: 
Pope County 



Brooten 



Cyrus 



Farwell 



Glenwood 



Long Beach 



Lowry 



Sedan 



Starbuck 



Villard 



Westport 



Stevens County 



Alberta 



Chokio 



Donnelly 



Hancock 



Morris 



Traverse County 



Browns Valley 



Dumont 



Tintah 



Wheaton 



Wilkin County 



Breckenridge 



Campbell 



Doran 



Foxhome 



Kent 



Nashua 



Rothsay 



Rothsay (OT county) 



Tenney 



Wolverton 


