One Sample Hypothesis Testing Essay

1129 Words Dec 10th, 2010 5 Pages
One Sample Hypothesis Testing
The significance of earnings is a growing façade in today’s economy. Daily operation, individuals, and families alike rely heavily on each sale or paycheck to provide financial stability throughout. Depending on the nature of labor, wages are typically compensated in accords to one’s experience and education or specialization. Moreover, calculating the specified industry, occupation title, education, experience on-the-job, gender, race, age, and membership to a union will additionally influence wages. To help analyze operation pay scales and remain within budget a business should obtain data pertaining to current variations in wage. Today statistics allow a business or businesses to do so in a timely and
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The wage of one worker being much higher than the others means our data will be skewed right and this data may not be a “good” sample. The existence of this outlier means our results will be skewed meaning we should find a better sample to base our results on. More importantly, the existence of an outlier reminds us that the mean is not always a good measure of the “typical” value of X.” (Doane & Seward, 2007).
Five-step Hypothesis Test
Team B would like to find if average Hispanic workers make more than $30,000 per year. The team’s null hypotheses or (HO) is that Hispanic pay is greater than or equal to $30,000. The team’s alternative hypothesis or (H1) is that Hispanic pay is less than $30,000. The significance level has been set at .05 or 95%. The z score of .05 is -1.645. If the z-value is less than -1.645 then the team can reject the null hypothesis and accept the alternative hypothesis. If the z-value is greater than -1.645 then the team fails to reject the null hypothesis, meaning Hispanic workers do, in fact, make more than $30,000 a year.
Hypotheses:
HO Hispanic pay ≤ 30,000
H1 Hispanic pay > 30,000
Data Set: (University of Phoenix, 2007)
83,601 29,736 15,234 24,509 33,461 13,481
Formula 1:
Mean = (83,601+29,736+15,234+24,509+33,461+13,481)/6
M = 33,337
Formula 2:
Standard Deviation = SQRT(((X1-M)Squared+(X2-M)Squared…)/(N-1))
SD =

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