Scatterplots And Correlations.Output
Upcoming SlideShare
Loading in...5
×
 

Scatterplots And Correlations.Output

on

  • 521 views

 

Statistics

Views

Total Views
521
Views on SlideShare
521
Embed Views
0

Actions

Likes
0
Downloads
4
Comments
0

0 Embeds 0

No embeds

Accessibility

Upload Details

Uploaded via as Microsoft Word

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Scatterplots And Correlations.Output Document Transcript

  • 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.