Numerical and Verbal Hypothesis Statement
Numerical and Verbal Hypothesis Statement
In business, small and large or personal the ability to have information to make a decision is essential. “Hypothesis testing is used in both science and business to test assumptions and theories and ultimately guide managers when faced making decisions.” (Doane & Seward, 2007, page 347, Chapter 9). We reviewed and analyzed the data set, Real Estate and developed a hypothesis test. The hypothesis test can be described with the following steps:
Step 1: State the Hypothesis
Does the housing in San Diego cost more than the national average? For this example, we have estimated that average housing cost in San Diego to be $300,000. According to the data set, the national average is $221,103. The null hypothesis is San Diego housing is more than or equal to the national average using a confidence level of 95%. The alternative hypothesis is that San Diego housing is not more than the national average. These two hypotheses are expressed numerically as follows:
H0: µ ≥ $ 221.1029
H1: µ < $ 221.1029
Step 2: Specify the Decision Rule
The decision rule is reject the null hypothesis if z > 1.96 or if the p-value is less than alpha. Alpha is this is equal to 0.05.
Reject H0 if z > + 1.645
Reject H0 if p < α
Step 3: Calculate the Test statistics:
The above chart represents the national average as proved by the Real Estate data set. The data has been sorted from lowest to highest and divided by six to form the various groups. The descriptive statistics is shown in the table below.
Price Mean 221.1029
Standard Error 4.597017
Median 213.6
Mode 188.3
Standard Deviation 47.1054
Sample Variance 2218.919
Kurtosis -0.2768
Skewness 0.474013
Range 220.3
Minimum 125
Maximum 345.3
Sum 23215.8
Count 105
Using the numbers from the table, the z value for San Diego is calculated as follows: x̄ = 300,000 z = x̄- μ/s√n, μ = 221.1029 z =
In business, small and large or personal the ability to have information to make a decision is essential. “Hypothesis testing is used in both science and business to test assumptions and theories and ultimately guide managers when faced making decisions.” (Doane & Seward, 2007, page 347, Chapter 9). We reviewed and analyzed the data set, Real Estate and developed a hypothesis test. The hypothesis test can be described with the following steps:
Step 1: State the Hypothesis
Does the housing in San Diego cost more than the national average? For this example, we have estimated that average housing cost in San Diego to be $300,000. According to the data set, the national average is $221,103. The null hypothesis is San Diego housing is more than or equal to the national average using a confidence level of 95%. The alternative hypothesis is that San Diego housing is not more than the national average. These two hypotheses are expressed numerically as follows:
H0: µ ≥ $ 221.1029
H1: µ < $ 221.1029
Step 2: Specify the Decision Rule
The decision rule is reject the null hypothesis if z > 1.96 or if the p-value is less than alpha. Alpha is this is equal to 0.05.
Reject H0 if z > + 1.645
Reject H0 if p < α
Step 3: Calculate the Test statistics:
The above chart represents the national average as proved by the Real Estate data set. The data has been sorted from lowest to highest and divided by six to form the various groups. The descriptive statistics is shown in the table below.
Price Mean 221.1029
Standard Error 4.597017
Median 213.6
Mode 188.3
Standard Deviation 47.1054
Sample Variance 2218.919
Kurtosis -0.2768
Skewness 0.474013
Range 220.3
Minimum 125
Maximum 345.3
Sum 23215.8
Count 105
Using the numbers from the table, the z value for San Diego is calculated as follows: x̄ = 300,000 z = x̄- μ/s√n, μ = 221.1029 z =