Team A – One Sample Hypothesis Testing Paper
University of Phoenix
RES 342 – RESEARCH AND EVALUATION II
Introduction
Within the housing sector these days it's important that homes are competitively priced,
along with taking into consideration the venue. By considering aspects that may impact
the cost of a house, everybody active in the real estate procedure can increase the cost
efficiency. The group theory tries to determine a cost comparison in the center of a town
against the outlying places. The team initially determines the aim of the study and after
that examines the outcomes by utilizing the five-step procedure. In this document we
will express the hypotheses, express the selection principle, compute the predicted
frequencies, compute the test statistic, and take the decision.
One Sample Hypothesis Testing Paper
The purpose of our research is to examine factors that impact the price of homes.
The problem statement is to examine the relationship between distance from the
center of the city and the price of homes within a certain radius. The research
question our team is trying to answer is; will the data reveal a significant
difference in price for houses less than 15 miles from the center of the city
(Group 1) and those houses equal to or greater than 15 miles from the center of
the city (Group 2)?
The average home values in Team 1 are shown by µH1 and average home values in
Team 2 are shown by µH2.
1. Null Hypothesis: There's no statistically substantial difference in the average of home
values Team 1 and the average of home values Team 2.
Ho: μH1 = μH2
2. Alternate Hypothesis: There's a statistically substantial difference in the average of
home values Team 1 and the average of home values Team 2.
H1: μH1 ≠ μH2
Five Step Process
The five actions theory test is; 1) express the theory, 2) express the selection principle,
3) compute the predicted frequencies, 4) compute the test statistic, and 5) take the
decision. (Doane and Seward, 2007)
Hypothesis
The problem statement is to examine the relationship between distance from the
center of the city and the price of homes, or will the data reveal a significant
difference in price for houses less than 15 miles from the center of the city
(Group 1) and those houses equal to or greater than 15 miles from the center of
the city (Group 2)? Another way to state this is the further away from the city the
values of the homes are lower compared to the more expensive homes closer to
the city?
Decision Rule
The crucial valuation on this test will be level 0.05 so as to make certain the outcomes
are substantial and to make certain the probability of getting the outcomes by possibility
are lower than 0.05. The 2 samples employed are separate samples. Utilizing Megastat
our sample standard deviation for Team 1 is 48.1. The ‘n’ for Team 1 is 52 because
there are 52 home values shown. The sample standard deviation for Team 2 is 43.9.
The ‘n’ is 53 because there are 53 prices shown.
Expected Frequencies
Within Team 1, our average is a bit more compared to our average and shows a great
skew towards the right. By utilizing Megastat, Team 1 shows a right skew of 0.396.
Within Team 2, our average is a bit more compared to our mean and shows a great
skew towards the right. By utilizing Megastat, Team 2 shows a right skew of 0.530.
Test Statistic
Team 1 consists of properties under fifteen miles from the town and it has an average
cost of 232, having a standard deviation of 48.1. Team 2 consists of properties
equivalent to or longer compared to 15 miles from the town and it has an average cost
of 210.4 having a standard deviation of 43.9. Depending on a 95% assurance interval
and a population sample of 105, the null theory will be turned down in case the
computed t-value is more than 1.984 and it is to the right of the crucial value in the
normal distribution bell curve. In case the computed t-value is less than 1.984 and
comes to the left of the crucial value in the normal distribution bell curve, the null theory
will not be turned down. (Doane, Seward, 2007).
Group 1: Less than 15 miles from city
Group 2: Equal to or more than 15 miles from city
The Decision
Depending on the information as well as the statistical computations, it seems that the
average cost of houses inside the 15 or lesser miles of the town are greater than the
average cost of houses outside the 15 miles of the town. These outcomes inform us that
we will not succeed to refuse the null theory (house close to the town cost more) and
refuse the alternate hypothesis (houses in a suburb cost more).
The Results
Agreeing or rejecting the Ho theory is dependent on the selection principle. The
selection principle for this experiment determines the crucial value that generates the
threshold for agreeing or rejecting the theory. The 2 samples employed are separate
samples. Team 1 consists of houses under 15 miles from the town and it has an
average cost of 232, having a standard deviation of 48.1. Team 2 consists of houses
equal to or longer than 15 miles from the town and it has an average cost of 210.4 with
a standard deviation of 43.9. Depending on a 95% assurance interval as well as a
population sample of 105, the null theory will be turned down in case the computed tvalue is more than 1.984 and is hence, to the right of the crucial value within the normal
distribution bell curve. In case the computed t-value is less than 1.984 and for this
reason, comes to the left of the crucial value on the normal distribution bell curve, the
null theory won't be turned down. (Doane, Seward, 2007).
The crucial value of this test will be point 0.05 to make certain the outcomes are
substantial. This will assist to ensure the possibility of having these outcomes by
possibility is less than 0.05.
Depending on the mean price of houses in teams 1 and 2, the computed t-value is 2.41.
The computed t-value is more than the 1.984 having a 95% assurance interval. The null
theory thus remains, turned down. After finishing the theory test employing the above
design experiment and decision principle [refuse Ho in case the computed t-value is
more than 1.984|], it was established that the null theory which mentioned, “There is no
statistically significant difference in the mean of home prices in group 1 and the mean of
home prices in group 2” is turned down depending on a 95% assurance interval.
Conclusion
It may be stated that people purchase a home not just for cost but area. By carrying out
investigation to find out the important drivers of house rate, the group has the ability to
determine the importance of area. The group investigation determined a confidence
interval of 95% which informs us that there's a statistically substantial difference in the
average of home values Team 1 and the average of home values Team 2. For
homebuilders and real estate agents, finding the gap in prices for a home in the town
against a home at least 15 miles from town centre is crucial to living in today's current
unsteady economic climate.