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One Sample Hypothesis Test

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One Sample Hypothesis Test
One Sample Hypothesis Test
Jeremey Yoppini, Mayela Castillo, Kristopher Olstad, Areli Mejia, Heather Smith
RES342
December 21, 2011
Thomas Allen

One Sample Hypothesis Test
Earning potential and income of every person is severely different; many factors have a hand in determining the amount of money a person makes and how much his or her earning potential can increase. Some of the factors currently determining the earning potential of people around the United States are; education, marital status, age, union participation, race, age, years of experience, sex, the industry in which the individual works, and the position held by individual. This paper is going to show the correlation between marital status and income, the team has disregarded all other determinants to answer the research question clearly. The research question that the team has developed and the hypothesis was formed from goes as follows; does marital status affect earning potential?
Every decade that passes, it seems as though people are waiting longer to get married. Waiting for job security, completion of college and social norms are just a few factors that influence this trend. This is a big change from 50 years ago, when most people would get married straight out of high school. The fact is being single has some advantages when deciding to start a career, it also affects ones earning potential. Being single allows more dedication to the job as well as the mobility to go wherever the job may take an individual. While being single may be good for starting a career, being married will actually increase a person’s earning potential in the long run. The mean salary for the single person is $24,864 per year. The mean income for married individuals is $33,303. This leads us to our null hypothesis (H0) that being married will not improve the earning potential of an individual. Our alternate hypothesis (H1) is that being married will help improve the earning potential of an individual.



References: David P. Doane, L. E. (2007). Applied Statistics in Business and Economics. New York: McGraw- Hill.

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