Respuesta :
Answer:
The data given is
235,000 271,900 183,300 203,000 182,900 225,500 189,000 214,200 237,900 233,500 217,000 230,400 202,950, 216,500 209,900, 245,500
The sample size is n = 16
The population is [tex]\mu = \$201,800[/tex]
The sample mean is mathematically represented as
[tex]\= x =\frac{\sum x_i}{n}[/tex]
=> [tex]\= x =\frac{235,000 + 271,900 + \cdots + 245,500 }{16}[/tex]
=> [tex]\= x = 218653.125[/tex]
Generally the sample standard deviation is mathematically represented as
[tex]s = \sqrt{\frac{\sum (x_i - \= x)^2}{n} }[/tex]
=> [tex]s = \sqrt{\frac{ (235,000 - 218653.125)^2+ (271,900 - 218653.125)^2 + \cdots + (245,500 - 218653.125)^2}{16} }[/tex]
=> [tex]s = 23946.896 [/tex]
The null hypothesis is [tex]H_o : \mu = \$201,800[/tex]
The alternatively hypothesis is [tex]H_o : \mu > \$201,800 [/tex]
Generally the test statistics is mathematically represented as
[tex]t = \frac{\= x - \mu }{ \frac{s}{\sqrt{n} } }[/tex]
=> [tex]t = \frac{218653.125 - 201800 }{ \frac{23946.896 }{\sqrt{16} } }[/tex]
=> [tex]t = 2.82[/tex]
Generally the degree of freedom is mathematically represented as
[tex]df = n - 1[/tex]
=> [tex]df = 16 - 1[/tex]
=> [tex]df = 15[/tex]
Generally the probability of [tex]t = 2.82[/tex] at a degree of freedom of [tex]df = 15[/tex] from the t - distribution table is
[tex]p-value = P( t >2.82 ) =0.00646356[/tex]
The
From the values obtained we see that [tex]p-value < \alpha[/tex]
The decision rule is
Reject the null hypothesis
The conclusion is
There is sufficient evidence to conclude that the real estate company's projection is true
Given that the population variance is unknown then the best statistical distribution to be applied is the t -distribution
Type I Error
The type 1 error occur when the null hypothesis is wrongfully rejected
The consequence in this case is the company will assume that the average selling price has increase and this will lead the company to start expanding the business while in the real sense the average selling price is still $201,800
Type II Error
The type 11 error occur when the null hypothesis is wrongfully accepted(i.e wrongfully failed to reject the null hypothesis)
The consequence in this case is that the company will assume that the average selling price is still $201,800 and will not make plans to increase the business while in the real sense the average selling price has increased
Given that resource is scare the management of the company will want a smaller significance level in order not to commit type I error which will lead to wrongly expanding the business and wastes of resources
generally the critical value of [tex]\frac{\alpha }{2}[/tex] from the normal distribution table is
[tex]Z_{\frac{\alpha }{2} } = 1.96[/tex]
Generally the margin of error is mathematically represented as
[tex]E =Z_{\frac{\alpha }{2} } * \frac{s}{\sqrt{n} }[/tex]
=>[tex]E =1.96* \frac{23946.896}{\sqrt{16} }[/tex]
=>[tex]E = 11733.96[/tex]
Generally the 95% confidence interval is mathematically represented as
[tex]218653.125 - 11733.96 < \mu < 218653.125 + 11733.96[/tex]
=> [tex]206919.165 < \mu < 230387.085[/tex]
Generally there is 95% confidence that the actual average selling price is within this interval
Step-by-step explanation:
