stat 13 lecture 24 regression(continued)

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Stat 13 Lecture 24 regression(continued) • Application in drug discovery ( http://dtp.nci.nih.gov ) • COMPARE • Antitubulin • Taxol (EX=-7.9; SD=.78) vinblastine sulfate (EY=-8.1; SD=.81) ; correlation =0.799 Prediction Many cell-lines are not tested on Taxol, but were tested on vinblastine sulfate Can use vinblastine sulfate to predict Taxol

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Stat 13 Lecture 24 regression(continued). Application in drug discovery ( http://dtp.nci.nih.gov ) COMPARE Antitubulin Taxol (EX=-7.9; SD=.78) vinblastine sulfate (EY=-8.1; SD=.81) ; correlation =0.799 Prediction - PowerPoint PPT Presentation

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Page 1: Stat 13 Lecture 24 regression(continued)

Stat 13 Lecture 24 regression(continued)

• Application in drug discovery (http://dtp.nci.nih.gov)

• COMPARE

• Antitubulin

• Taxol (EX=-7.9; SD=.78)• vinblastine sulfate (EY=-8.1; SD=.81) ; correlation =0.799• Prediction • Many cell-lines are not tested on Taxol, but were tested on vinblastine sulfate • Can use vinblastine sulfate to predict Taxol

Page 2: Stat 13 Lecture 24 regression(continued)

Taxol=-7.9 + .799(7.8/8.1)(vinblastine sulfate +8.1)

• Malanoma UABMEL3 -7.9 (vinblastine sulfate)

• No taxol data

• Predicted Taxol = -7.9 + .799(7.8/8.1)(-7.9+8.1)

• = -7.75

Page 3: Stat 13 Lecture 24 regression(continued)

taxol

-11

-10

-9

-8

-7

-6

-5

-9 -8 -7 -6 -5

Series1

Linear (Series1)

Vertical axis: vinblastine sulfate

Cannot use this line to predict Taxol from vinblastine sulfate;

Redraw the graph by exchanging X and Y

Page 4: Stat 13 Lecture 24 regression(continued)

Percentile rank problem :

Suppose only the ranking of x is given. Predict the ranking for y.

The trick is to pretend X and Y both have mean 0 and standard deviation 1

The reason you can do so is because ranking does not change under scale changes

++++++++++++++++++++++++++++=vinblastine sulfate

Step 1 convert the rank to x by using normal table

Step 2 find y by using y = r x

Step 3 convert y back to percentile rank using normal table again.

Page 5: Stat 13 Lecture 24 regression(continued)

Example