1. Scatterplots and Correlations
Presented to
Dr. Rosenbaum
Marketing Research 443
Northern Illinois University
Prepared by
Aaron Burden
October 27, 2008

2. Here is the data that you requested about whether urban living has an influence on life expectancy
of males and females. I have conducted a correlation test (see Table 1) and provided scartterplots
to illustrate whether there is a correlation.
Table 1
Correlations
Average Average
female life male life People living
expectancy expectancy in cities (%)
Average female life Pearson Correlation 1 .982** .743**
expectancy Sig. (2-tailed) .000 .000
N 109 109 108
Average male life Pearson Correlation .982** 1 .730**
expectancy Sig. (2-tailed) .000 .000
N 109 109 108
People living in cities (%) Pearson Correlation .743** .730** 1
Sig. (2-tailed) .000 .000
N 108 108 108
**. Correlation is significant at the 0.01 level (2-tailed).
Ho: There is no association between the life expectancy of females versus living in an urban area.
Ha: There is a significant association between life expectancy of females versus living in an urban area.
Test: Correlation
Confidence Level: 95%
Significant Factor: P Value .000 (Pearson Correlation .743 females)

3. Average Female Life Expectancy
Switzerland
80
Average female life expectancy
Singapore
Oman
70
60
Burundi
50
Cent. Afri.R
Uganda
R Sq Linear = 0.553
40
0 20 40 60 80 100
People living in cities (%)
Source: SPSS Inc,
Conclusion: Reject the Null
After reviewing the data and scatterplot, there is a significant association between the life expectancy of females and the
percentage the female population who resided in urban area. First, Switzerland’s data revealed that approximately 70% of the
females who resided in an urban area have an average life expectancy of above 80 years. Secondly, Singapore’s data reveals that
approximately 90% of the females who resided in an urban area have an average life expectancy of about 75 years. Lastly,
Oman’s data revealed that only 15% of the population of females who resided in an urban area, surprising females has an average
life expectancy of approximately 70 years.
The following data will present the countries that had an average life expectancy that was below 50 years for females and they
include: Burundi, Uganda, and Central Africa. First, Burundi’s data reveals that approximately 10% of population of females that
reside in an urban area have an average life expectancy of approximately 48 years. Secondly, Uganda’s data reveals that about
15% of the population of females that reside in an urban area have an average life expectancy of approximately 45 years. Lastly,
Central Africa’s data reveals that approximately 45% of the population of females that resided in urban areas have an average life
expectancy of about 45 years.
Finally, there is a very strong linear association between the life expectancy of females and the percentage of the female
population that lived in an urban area. The R Squared value of .553 indicates if the percentage of the population of females that
resides in an urban area is known, the life expectancy of females can be predicted correctly approximately 56% of the time.
Ho: There is no association between the life expectancy of males versus living in an urban area.
Ha: There is a significant association between life expectancy of males versus living in an urban area.

4. Test: Correlation
Confidence Level: 95%
Significant Factor: P Value .000 (Pearson Correlation .730 Males)
Correlations
Average Average
female life male life People living
expectancy expectancy in cities (%)
Average female life Pearson Correlation 1 .982** .743**
expectancy Sig. (2-tailed) .000 .000
N 109 109 108
Average male life Pearson Correlation .982** 1 .730**
expectancy Sig. (2-tailed) .000 .000
N 109 109 108
People living in cities (%) Pearson Correlation .743** .730** 1
Sig. (2-tailed) .000 .000
N 108 108 108
**. Correlation is significant at the 0.01 level (2-tailed).

5. Average Male Life Expectancy
80
Costa Rica Japan
Average male life expectancy
Singapore
70
Oman
60
Brazil
50
Burundi
Uganda Tanzania Cent. Afri.R R Sq Linear = 0.532
40
0 20 40 60 80 100
People living in cities (%)
Source: SPSS Inc,
Conclusion: Reject the null
After reviewing the data and scatterplot, there is a significant association between the life expectancy of males and the
percentage of the population that resided in an urban area. Japan’s data revealed that approximately 77% of the males that resided
in an urban area have an average life expectancy of approximately 75 years. Secondly, Singapore’s data reveals that approximately
85% of the male population who resided in an urban area had an average life expectancy of about 68 years. Lastly, Oman’s data
revealed that only 15% of male population who reside in an urban area, surprisingly had an average life expectancy for males of
approximately 65 years.
The following data will present the countries that had an average life expectancy that was below 50 years for males and they
include Burundi, Uganda, and Central Africa. First, Burundi’s data reveals that approximately 8% of male population that resided
in an urban area had an average life expectancy of approximately 45 years. Secondly, Uganda’s data reveals that about 10% of
male populations that resided in an urban area had an average life expectancy of approximately 42 years. Lastly, Central Africa’s
data reveals that approximately 47 of the male population that resided in urban areas have an average life expectancy of about 43
years.
Finally, there is a very strong linear association between the life expectancy of males and the percentage of the male population
that lived in an urban area. The R Squared value of .532 indicates if the percentage of the population of males that resides in an
urban area is known, the life expectancy of males can be predicted correctly approximately 53% of the time.