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conclusion using p-value and critical value approaches

A sample of 9 days over the past six months showed that a clinic treated the following numbers of patients: 24, 26, 21, 17, 16, 23, 27, 18, and 25. If the number of patients seen per day is normally distributed, would an analysis of these sample data provide evidence that the variance in the number of patients seen per day is less than 10? Use α = .025 level of significance. What is your conclusion using p-value and critical value approaches. Is the conclusion different in both the cases?

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Hypothesis Formation

H0: σ =10

H1: σ < 10

Test Statistics

χ2 = (n-1).S2/ σ2

Critical Region

Reject H0 in favor of alternative if χ2 test statistic lesser than the critical value of χ2

i.e χ2test statistic < critical χ2

Critical value of χ2 at 0.025 Significance Level for single tail test

Df = n – 1 = 9 – 1 = 8

Critical value of χ2 with df 8 and alpha 0.025 = 2.18

Computation

Data (X)

X – X-bar

(X-X-bar)2

24

2.111111

4.45679

26

4.111111

16.90123

21

-0.88889

0.790123

17

-4.88889

23.90123

16

-5.88889

34.67901

23

1.111111

1.234568

27

5.111111

26.12346

18

-3.88889

15.12346

25

3.111111

9.679012

 

Sum of (X-X-bar)2 = 132.89

S2 = 132.89/9-1

     = 16.61 

χ2 = (9-1)*16.61/10

    = 13.29

Decision

As χ2 statistic is not less than critical value, therefore we can’t say that variance is less than 10. P-value for critical value is 0.01 and it is approximately found from χ2 table.  P-value is greater than our tolerance for ambiguity therefore we can’t that variance is significantly lower than 10.

 

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