955 words - 4 pages

Nonparametric Hypothesis Test:.

Research can be focused in a number of ways, to help refine our topic to a point where we have clear hypotheses statements. If the housing industry was determined to be doing better than the rest of the economy, a hypothesis test might be in order, with mean prices greater than other housing industries. The test to determine the difference is the one sample run (Wald- Wolfowitz test to determine the mean prices to be equal to each home cost (Doane & Seward, 2007).

However, March home sales were higher than expected. Our Presidents recent trip aboard has secured enough raw material and additional energy products to secure the USA zone of growth for ...view middle of the document...

The hypotheses are:

H0: Homes sales follow a random sequence

H1: Homes prices follow a nonrandom sequence

To test the hypothesis of randomness we first count the number of outcomes of each type A simple way to state the ANOVA hypothesis is::

Hypothesis: The null would be random, the alternative would have a relationship:

Hypothesis: The null would be random, the alternative would have a relationship:

H0: House sales with pools are random.

H1: House sales with pools are not random.

Decision Rule:

α=0.05 so critical value of a two tailed test (α/2): z.025=1.960.

So reject the null if -1.960>z>1.960. Otherwise do not reject.

Start with a table-

Start the count at the first variable. It almost looks binary with all the ones and zeros! The first one option has a single one so we add that. The first zero option has five (counting down from the top). We continue until the end of the data set. The table below shows the pools data.

Run Series Table | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 |

| 1 | 5 | 1 | 2 | 1 | 7 | 4 | 1 | 1 | 3 | 1 | 7 | 1 | 1 | 1 | 4 | 6 | 2 | 1 | 1 | 1 | 4 | 1 | 2 | 1 | 3 | 1 | 2 |

| 5 | 1 | 1 | 1 | 1 | 1 | 1 | 4 | 1 | 2 | 1 | 7 | 4 | 1 | 1 | 4 | 1 | 3 | | | | | | | | | | |

The number of runs is equal to the number of changes in the entire data set. The table helps to count these, as there are many. 28 on the first row and 18 on the second row so 28+18=46 runs (R=46).

n1=the number of yes: (add all of the numbers in the ones columns together) = 38

n2=the number of no: (add all of the numbers in the zero columns together) = 67

n=total...

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