The one-sample hypothesis test paper has decided to use the real estate data set and form a hypothesis that proves the statement that an average house will cost a buyer more than $150,000 will state the research question to be answered. After the research question is defined, the primary research will begin. The research will include the statement of hypothesis along with variables and the level of measurement. Once the research is done, the methodology and the findings will be concluded.
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Is there enough evidence to support to claim that an average house will cost a buyer more than $150,000 at 5% significance level?
Statement of Hypothesis
Null Hypothesis: An average house will cost a buyer is $150,000
Alternate Hypothesis: An average house will cost a buyer more than $150,000.
Variables and Level of Measurement
Dependent Variables: The dependent variables used in terms of a numeric level of measurement are the semester mean of $221,000 and the standard deviation of $47,000. There are 8 variables and 105 observations contained in the data presented.
Independent Variables: The independent variables used in terms of numeric levels of measurement is are the 105 students along at 5% significance level.
State the null and alternative hypotheses:
Ho: µ= $150,000→ An average house will cost a buyer is $150,000
H1: µ > $150,000→ An average house will cost a buyer more than $150,000
State the significance level and decision rule:
α = 0.05
This is one-tailed z-test.
If test statistic is greater than 1.645 of the critical value, reject Ho and accept H1
Otherwise, do not reject H0
Find the critical value of z:
Critical Value: +1.645
Calculate the Test Statistic of z:
Test statistic of z =(221-150)/(47/Sqrt(105))=15.47
Make a Decision:
Since test statistic of z=15.47 is greater than the critical value of z=1.645, it falls in the rejection region, we reject the null hypothesis and conclude that there is NO statistical significant evidence to support the claim an average house will cost a buyer more than $150,000
Doane, D. P., & Seward, L. W. (2007). Applied statistics in business and economics. New York, NY: McGraw-Hill.
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