My primary problem is knowing if I\'ve entered the information into the program
ID: 3051703 • Letter: M
Question
My primary problem is knowing if I've entered the information into the program correctly, the write up of results explaining the significance of the values, and what these results tell me. Can you first tell me if these values are correct and assist me in understanding the significance? In terms of df, p-value, percentage likely to, etc.? Please?
A researcher is interested in knowing if there are differences between the incidence of accidents among pilots of different experience levels. They were able to collect 62 responses from a random number of pilots. Among them 17 new pilots were in accidents, 26 were not; 13 experienced pilots were in accidents, 6 were not. Construct a contingency table. Run the Chi-Square test for independence using StatCrunch. To access, click STAT>Tables>Contingency> With summary. State your findings and their significance. Do not forget to give summary statistics in APA format.
Contingency table results:
Contingency table results:
Level of Experience
With Accidents
No Accidents
Total
Inexperienced (Count)
(Row percent)
(Column %)
(% of total)
(Expected count)
(Contrib. to Chi-Square)
(Chi-Square residuals)
(Standardized Chi-Square residuals)
17
(39.53%)
(56.67%)
(27.42%)
(20.81)
(0.7)
(-3.81)
(-0.83)
26
(60.47%)
(81.25%)
(41.94%)
(22.19)
(0.65)
(3.81)
(0.81)
43
(100%)
(69.35%)
(69.35%)
Experienced (Count)
(Row percent)
(Column %)
(% of total)
(Expected count)
(Contrib. to Chi-Square)
(Chi-Square residuals)
(Standardized Chi-Square residuals)
13
(68.42%)
(43.33%)
(20.97%)
(9.19)
(1.58)
(3.81)
(1.26)
6
(31.58%)
(18.75%)
(9.68%)
(9.81)
(1.48)
(-3.81)
(-1.22)
19
(100%)
(30.65%)
(30.65%)
Total (Count)
30
(48.39%)
(100%)
(48.39%)
32
(51.61%)
(100%)
(51.61%)
62
(100%)
(100%)
(100%)
Chi-Square test:
Statistic
DF
Value
P-value
Chi-square
1
4.4027336
0.0359
Contingency table results:
Level of Experience
With Accidents
No Accidents
Total
Inexperienced (Count)
(Row percent)
(Column %)
(% of total)
(Expected count)
(Contrib. to Chi-Square)
(Chi-Square residuals)
(Standardized Chi-Square residuals)
17
(39.53%)
(56.67%)
(27.42%)
(20.81)
(0.7)
(-3.81)
(-0.83)
26
(60.47%)
(81.25%)
(41.94%)
(22.19)
(0.65)
(3.81)
(0.81)
43
(100%)
(69.35%)
(69.35%)
Experienced (Count)
(Row percent)
(Column %)
(% of total)
(Expected count)
(Contrib. to Chi-Square)
(Chi-Square residuals)
(Standardized Chi-Square residuals)
13
(68.42%)
(43.33%)
(20.97%)
(9.19)
(1.58)
(3.81)
(1.26)
6
(31.58%)
(18.75%)
(9.68%)
(9.81)
(1.48)
(-3.81)
(-1.22)
19
(100%)
(30.65%)
(30.65%)
Total (Count)
30
(48.39%)
(100%)
(48.39%)
32
(51.61%)
(100%)
(51.61%)
62
(100%)
(100%)
(100%)
Explanation / Answer
Yes, you correctly entered data and your output is also correct.
Here we want to test whether number of accidents depend on level of experience.
Significant result means we reject null hypothesis (so accept alternative hypothesis).
Here, the null and alternative hypotheses would be:
Null: number of accidents is independent of level of experience
Alternative: number of accidents is dependent on level of experience
from output, df=1, test statistic = 4.40 and p-value = 0.0359.
Since p-value is less than 0.05, reject the null hypothesis. We can conclude that number of accidents is dependent on level of experience.
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