Bayesian Statistics
These statistics are based upon all of the DNA detection system results that were obtained in the PCR lab for 34 hypothetical patients who were diagnosed as either having cancer or not having cancer.
Calculation 1: The probability that the sample actually has the cancer DNA sequence, given a positive diagnostic signal.
A = [text description] = [frequency shown as a fraction] = [final numerical value]
B = [text description] = [frequency shown as a fraction] = [final numerical value]
P (B|A) = [text description] = [frequency shown as a fraction] = [final numerical value]
P(A|B) = [answer]
Calculation 3: The probability that the patient will develop cancer, given a cancer DNA sequence.
A = [text description] = [frequency shown as a fraction] = [final numerical value]
B = [text description] = [frequency shown as a fraction] = [final numerical value]
P (B|A) = [text description] = [frequency shown as a fraction] = [final numerical value]
P(A|B) = [answer]
New System: Design Strategy
We concluded that a good system Must Have:
[Must have #1 - why? short, ~4 or 5 sentences]
[Must have #2 - why? short, ~4 or 5 sentences]
We concluded that we would Want a good system to have:
[Want #1 - why? short, ~4 or 5 sentences]
[Want #2 - why? short, ~4 or 5 sentences
We concluded that a good system Must Not Have:
[Must Not Have #1 - why? short, ~4 or 5 sentences]
[Must Not Have #2 - why? short, ~4 or 5 sentences]
We concluded that a good system Should Avoid:
[Should Avoid #1 - why? short, ~4 or 5 sentences]
[Should Avoid #2 - why? short, ~4 or 5 sentences]
New System: Machine/ Device Engineering
DESIGN STRATEGY
We chose to include these new features
Feature 1 - explanation of how this addresses any of the the Must Have/ Must Not Have items in the
Feature 2 - description * Etc.
[OR]
We chose keep the devices the same as the original system
[Short paragraph explaining why the original system satisfies the design needs that you listed above]