A large corporation was interested in determining whether an association exists between commuting time of their employees and the level of stress-related problems observed on the job. A study of 116 assembly-line workers revealed the following:
Commuting Time |
Stress |
|||
High |
Moderate |
Low |
||
Under 15 min. |
15 |
6 |
21 |
|
15 min. to 45 min. |
9 |
9 |
32 |
|
Over 45 min. |
21 |
7 |
5 |
At the 5% significance level, is there evidence of a relationship between commuting time and stress?
a)
The null and alternate hypothesis are:
H0: There is no association between the two variables.
Ha: There is association between the two variables.
b)
Observed frequencies (Oi):
High | Mod | Low | TOTAL | |
Under 15 | 15 | 6 | 21 | 42 |
15 to 45 | 9 | 9 | 32 | 50 |
Over 45 | 21 | 7 | 5 | 33 |
TOTAL | 45 | 22 | 58 | 125 |
Now, Expected frequency = [(Row total) x (Column total)] / Table total
Expected frequencies (Ei):
High | Mod | Low | TOTAL | |
Under 15 | 7.392 | 19.488 | 42 | |
15 to 45 | 18 | 8.8 | 23.2 | 50 |
Over 45 | 11.88 | 5.808 | 15.312 | 33 |
TOTAL | 45 | 22 | 58 | 125 |
Test statistic value =
c)
The critical value is given by:
Since the test statistic value is greater than the critical value, so we have sufficient evidence to reject null hypothesis H0. Thus, we can say that there is association between the two variables.
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