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Statistics Project
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  1. Statistics Project Group 2 Michelle Wilfahrt Michelle May Chelsea Kerns Natalie Tukuafu Contributed by Natalie Tukuafu

  2. Research Question Is the number of hours spent working weekly related to the number of credit hours taken of school per semester in students who work while going to school? Contributed by Natalie Tukuafu

  3. Data Table Contributed by Natalie Tukuafu

  4. Study Plan • Each group member will randomly select 6- 8 people on school campus (class, testing center etc.) and survey them using the following questionnaire/table. • Approach person and explain your purpose for questioning them. • Either ask them and fill this table out yourself or make some copies and have the person fill it out themselves. *note: We are going for positive working responses. So if a person does not work, find another random person that is working and attending school. • Record results. • Report and tally results with group. Contributed by Natalie Tukuafu

  5. Statistics for First Quantitative Variable: Hours Worked per Week Contributed by Michelle Wilfahrt

  6. Histogram Contributed by Michelle Wilfahrt

  7. Boxplot Contributed by Michelle Wilfahrt

  8. Statistics for Second Quantitative Variable: Credit Hours Contributed by Michelle Wilfahrt

  9. Histogram Contributed by Michelle Wilfahrt

  10. Boxplot Contributed by Michelle Wilfahrt

  11. Correlation between two variables. • Linear Correlation Coefficient(R): = - 0.2809 • Equation for line of regression: y = 14.7489 - 0.1009x Contributed by Natalie Tukuafu

  12. Scatter plot including line of regression Contributed by Natalie Tukuafu

  13. Difficulties & Surprises • Difficulties in the communication process between group members using the forum boards format. • The amount of people who did not qualify for our survey. • The amount of people taking heavy loads while working long hours was surprising. • The expectation was that there would be a stronger correlation than we found between the amount of hours a person works and the amount of credit hours they take. contributed by Michelle May

  14. Analysis • The individual variable data were fairly normally distributed. • The scatter plot shows the points widely spread out. • The r value (-.281) suggests a very weak negative relationship. • The r value (-.281) is less than the critical coefficient value for the sample size 33 - |.344|, effectively showing that there is no linear relationship. contributed by Michelle May

  15. Interpretation • Based on their being no linear relationship, it looks like there is no definitive relationship between the variables. • A different sample may show different results. • Lurking variables most likely influence heavily the relationship between the variables. • Lurking variables could include: family situations, whether a person works in a flexible work environment, the type of college itself and the class schedules offered. contributed by Michelle May

  16. Conclusion • In answer to the research question “ Is the number of hours spent working weekly related to the number of credit hours taken of school per semester in students who work while going to school?" • The answer is no according to the results and the scope of this study taken by four students at Salt Lake Community College during the Spring 2011 semester. contributed by Michelle May

  17. Credits Natalie Tukuafu- Title page, research question, study plan, data table, regression line, scatter plot. Michelle Wilfahrt- Variable 1 stats, graph, box plot and Variable 2 stats, graph and box plot. Michelle May- Difficulties and Surprises, analysis, interpretation, conclusion and credits. Chelsea Kerns- While she did contribute her original survey data, she did not participatewith the presentation. Contributed by Michelle May