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Both a gravimetric and a spectrophotometric method are under consideration for determining phosphate content of a particular material. Twelve samples of the material are obtained, each is split in half, and a determination is made on each half using one of the two methods, resulting in the following data:

Sample

1

2

3

4

Gravimetric

54.7

58.5

66.8

46.1

Spectrophotometric

55.0

55.7

62.9

45.5

Short Answer

Expert verified

Reject null hypothesis

Step by step solution

01

testing null hypothesis:

When testing null hypothesis

\({H_0}:\mu = {\mu _0}\)

Versus one of the alternative hypothesis, one could use test static value

\({s_ + }\)= the sum of the ranks associated with positive\(\left( {{x_i} = {\mu _0}} \right)'s\)

The P-value depends on the alternative hypothesis

Alternative hypothesis P-values

\(\begin{array}{l}{H_0}:\mu > {\mu _0}\\{H_0}:\mu < {\mu _0}\\{H_0}:\mu \ne {\mu _0}\end{array}\) \(\begin{array}{l}{P_0}\left( {{S_ + } \ge {s_ + }} \right)\\{P_0}\left( {{S_ + } \le {s_ + }} \right) = {P_0}\left( {{S_ + } \ge \frac{{n\left( {n + 1} \right)}}{2} - {s_ + }} \right)\\2{P_0}\left( {{S_ + } \ge \max \left\{ {{s_ + },\frac{{n\left( {n + 1} \right)}}{2} - {s_ + }} \right\}} \right)\end{array}\)

02

solving further:

The test of interest is

\({H_0}:{\mu _D} = 0\)

Versus alternative hypothesis

\({H_a}:{\mu _D} \ne 0\)

The following table represents values required to compute the test statistic value and corresponding P-value. Value 鈥-1鈥 represents 鈥-鈥 as a sing (negative difference), and value 1 is 鈥-+鈥.

I

\(x_i^1\)

\(x_i^2\)

\({y_i} = x_i^1 - x_i^2\)

\(\left| {{y_i}} \right|\)

Rank(\({y_i}\))

Sign(\({y_i}\))

1

54.7

55

-0.3

0.3

1

-1

2

58.5

55.7

2.8

2.8

10

1

3

66.8

62.9

3.6

3.6

12

1

4

46.1

45.5

0.6

0.6

3

1

5

52.3

51.1

1.2

1.2

6

1

6

74.3

75.4

-1.1

1.1

5

-1

7

92.5

89.6

2.9

2.9

11

1

8

40.2

38.4

1.8

1.8

7

1

9

87.3

86.8

0.5

0.5

2

1

10

74.8

72.5

2.3

2.3

8

1

11

63.2

62.3

0.9

0.9

4

1

12

68.5

66

2.5

2.5

9

1

03

test static value:

The test statistic value is,

\({s_ + }\)= the sum of the ranks associated with positive\(\left( {{x^1}\_i - {x^2}\_i} \right)'s\)

\(\begin{array}{l} = 10 + 12 + ... + 9\\ = 72\end{array}\)

The corresponding P-value is,


\(\begin{array}{l}2{P_0}\left( {{S_ + } \ge \max \left\{ {{s_ + },\frac{{n\left( {n + 1} \right)}}{2} - {s_ + }} \right\}} \right)\\ = 2{P_0}\left( {{S_ + } \ge \max \left\{ {12,\frac{{12.\left( {12 + 1} \right)}}{2} - 72} \right\}} \right)\\ = 2{P_0}\left( {{S_ + } \ge 72} \right)\end{array}\)

In the table in the appendix one could find only particular values ; thus, the null hypothesis should be rejected at\(n = 12\), when

\(2 \cdot 0.005 = 0.01 > P - value = 2{P_0}\left( {{S_ + } \ge 72} \right)\)

\(P < 0.01 < 0.05 = \alpha \)

Reject null hypothesis

Hence, reject null hypothesis.

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Most popular questions from this chapter

Suppose we wish to test.

: the X and Y distributions are identical

versus

: the X distribution is less spread out than the Y

distribution

The accompanying figure pictures X and Y distributions for which is true. The Wilcoxon rank-sum test is not appropriate in this situation because when is true as pictured, the Y鈥檚 will tend to be at the extreme ends of the combined sample (resulting in small and large Y ranks), so the sum of X ranks will result in a W value that is neither large nor small.

Consider modifying the procedure for assigning ranks as follows: After the combined sample of m + n observations is ordered, the smallest observation is given rank 1, the largest observation is given rank 2, the second smallest is

given rank 3, the second largest is given rank 4, and so on. Then if is true as pictured, the X values will tend to be in the middle of the sample and thus receive large ranks. Let W鈥 denote the sum of the X ranks and consider an uppertailed test based on this test statistic. When is true, every possible set of X ranks has the same probability, so W鈥 has the same distribution as does W when H0 is true. The accompanying data refers to medial muscle thickness for arterioles from the lungs of children who died from sudden infant death syndrome (x鈥檚) and a control group of children (y鈥檚). Carry out the test of versus at level .05.

SIDS 4.0 4.4 4.8 4.9

Control 3.7 4.1 4.3 5.1 5.6

Consult the Lehmann book (in the chapter bibliography) for more information on this test, called the Siegel-Tukey test.

Use the large-sample version of the Wilcoxon test at significance level .05 on the data of Exercise 37 in Section 9.3 to decide whether the true mean difference between outdoor and indoor concentrations exceeds .20.

The given data on phosphorus concentration in topsoil for four different soil treatments appeared in the article 鈥淔ertilisers for Lotus and Clover Establishment on a Sequence of Acid Soils on the East Otago Uplands鈥 (N. Zeal. J. of Exptl. Ag., 1984: 119鈥129). Use a distributionfree procedure to test the null hypothesis of no difference in true mean phosphorus concentration (mg/g) for the four soil treatments.

I 8.1 5.9 7.0 8.0 9.0

II 11.5 10.9 12.1 10.3 11.9

III 15.3 17.4 16.4 15.8 16.0

IV 23.0 33.0 28.4 24.6 27.7

The sign test is a very simple procedure for testing hypotheses about a population median assuming only that the underlying distribution is continuous. To illustrate, consider the following sample of 20 observations on component lifetime (hr):

1.7 3.3 5.1 6.9 12.6 14.4 16.4

24.6 26.0 26.5 32.1 37.4 40.1 40.5

41.5 72.4 80.1 86.4 87.5 100.2

We wish to test \({H_0}:\tilde \mu = 25.0\) versus \({H_0}:\tilde \mu > 25.0\)The test statistic is Y 5 the number of observations that exceed 25

a. Determine the P-value of the test when Y 5 15. (Hint: Think of a 鈥渟uccess鈥 as a lifetime that exceeds 25.0. Then Y is the number of successes in the sample. What kind of a distribution does Y have when\(\tilde \mu = 25.0\)?)

b. For the given data, should H0 be rejected at significance level .05? (Note: The test statistic is the number of differences \({X_i} - 25\)that have positive signs, hence the name sign test.)

The accompanying data on cortisol level was reported in the article 鈥淐ortisol, Cortisone, and 11-Deoxycortisol Levels in Human Umbilical and Maternal Plasma in Relation to the Onset of Labor鈥 (J. of Obstetric Gynaecology of the British Commonwealth, 1974: 737鈥745). Experimental subjects were pregnant women whose babies were delivered between 38 and 42 weeks gestation. Group 1 individuals elected to deliver by Caesarean section before labor onset, group 2 delivered by emergency Caesarean during induced labor, and group 3 individuals experienced spontaneous labor. Use the Kruskal-Wallis test at level .05 to test for equality of the three population means.

Group 1 262 307 211 323 452 339

304 154 287 356

Group 2 467 501 455 355 468 362

Group 3 343 772 207 1048 838 687

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