Drag each value to the correct location on the box plot. Not all values will be used.
1 answer:
Answer:
Min value = 62
Lower quartile = 69
Median = 75.5
Upper quartile = 80
Max value = 83
Outlier = 118
Step-by-step explanation:
Order the data from smallest to largest:
62, 63, 64, 69, 72, 75, 75, 76, 78, 78, 80, 82, 83, 118
The median (Q2) value is the middle value.
As the middle is between the 7th and 8th values,
median = (75 + 76) / 2 = 75.5
Lower quartile (Q1) = 4th value = 69
Upper quartile (Q3) = 11th value = 80
IQR = Q3 - Q1 = 80 - 69 = 11
Outliers < Q1 - 1.5 x IQR = 69 - 1.5 x 11 = 52.5
Outliers > Q3 + 1.5 x IQR = 96.5
Therefore, one outlier at 118
Minimum value = 62
Maximum value = 83 (as 118 is an outlier)
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Answer:
39.17% probability that a woman in her 60s who has a positive test actually has breast cancer
Step-by-step explanation:
Bayes Theorem:
Two events, A and B.
In which P(B|A) is the probability of B happening when A has happened and P(A|B) is the probability of A happening when B has happened.
In this question:
Event A: Positive test.
Event B: Having breast cancer.
3.65% of women in their 60s get breast cancer
This means that
A mammogram can typically identify correctly 85% of cancer cases
This means that
Probability of a positive test.
85% of 3.65% and 100-95 = 5% of 100-3.65 = 96.35%. So
What is the probability that a woman in her 60s who has a positive test actually has breast cancer?
39.17% probability that a woman in her 60s who has a positive test actually has breast cancer