Stewart Fleishman specializes in the psychiatric aspects of symptom management in cancer patients. Pain, depression, and fatigue can appear as single symptoms, in conjunction with one other symptom, or all together in patients with cancer. You are interested in testing a new kind of cognitive-behavioral therapy for the treatment of the simultaneous clustering of pain and fatigue in cancer patients. The following scores represent the decrease in symptom intensity (on a 10-point scale) following the new cognitive-behavioral therapy. Scores Patient Pain (X) Fatigue (Y) A 3 1.25 B 4 0 C 5 6.5 D 6 2.5 E 7 10 Create a scatter plot of these scores on the grid. For each of the five (X, Y) pairs, drag the orange points (square symbol) in the upper-right corner of the diagram to the appropriate location on the grid. Scores02468101086420FATIGUEPAIN Calculate the means and complete the following table by calculating the deviations from the means for X and Y, the squares of the deviations, and the products of the deviations. Scores Deviations Squared Deviations Products X Y X – MXX Y – MYY (X – MXX)² (Y – MYY)² (X – MXX)(Y – MYY) 3 1.25 4 0 -1.00 -4.05 1.00 16.40 4.05 5 6.5 6 2.5 1.00 -1.55 1.00 2.40 -1.55 7 10 Calculate the sum of the products and the sum of squares for X. SP = and SSXX = . Find the regression line for predicting Y given X. The slope of the regression line is and the Y intercept is . Calculate the Pearson correlation coefficient, the predicted variability, and the unpredicted variability. The Pearson correlation is r = . The predicted variability is SSregressionregression = . The unpredicted variability is SSresidualresidual = . Calculate the standard error of the estimate. The standard error of the estimate is . Suppose you want to predict the fatigue score for a new patient. The only information given is that this new patient is similar to patients A through E; therefore, your best guess for the new patient’s level of fatigue is . The error associated with this guess (that is, the “standard” amount your guess will be away from the true value) is . Suppose that now you are told the pain score for this new patient is 5.5. Now your best guess for the new patient’s level of fatigue is . The error associated with this guess (that is, the “standard” amount your guess will be away from the true value) is . Finally, suppose before estimating the regression equation, you first transform each of the original scores into a z-score. The regression equation you estimate is: ẑYY = zXX

Stewart Fleishman specializes in the psychiatric aspects of symptom management in cancer patients. Pain, depression, and fatigue can appear as single symptoms, in conjunction with one other symptom, or all together in patients with cancer. You are interested in testing a new kind of cognitive-behavioral therapy for the treatment of the simultaneous clustering of pain and fatigue in cancer patients. The following scores represent the decrease in symptom intensity (on a 10-point scale) following the new cognitive-behavioral therapy. Scores Patient Pain (X) Fatigue (Y) A 3 1.25 B 4 0 C 5 6.5 D 6 2.5 E 7 10 Create a scatter plot of these scores on the grid. For each of the five (X, Y) pairs, drag the orange points (square symbol) in the upper-right corner of the diagram to the appropriate location on the grid. Scores02468101086420FATIGUEPAIN Calculate the means and complete the following table by calculating the deviations from the means for X and Y, the squares of the deviations, and the products of the deviations. Scores Deviations Squared Deviations Products X Y X – MXX Y – MYY (X – MXX)² (Y – MYY)² (X – MXX)(Y – MYY) 3 1.25 4 0 -1.00 -4.05 1.00 16.40 4.05 5 6.5 6 2.5 1.00 -1.55 1.00 2.40 -1.55 7 10 Calculate the sum of the products and the sum of squares for X. SP = and SSXX = . Find the regression line for predicting Y given X. The slope of the regression line is and the Y intercept is . Calculate the Pearson correlation coefficient, the predicted variability, and the unpredicted variability. The Pearson correlation is r = . The predicted variability is SSregressionregression = . The unpredicted variability is SSresidualresidual = . Calculate the standard error of the estimate. The standard error of the estimate is . Suppose you want to predict the fatigue score for a new patient. The only information given is that this new patient is similar to patients A through E; therefore, your best guess for the new patient’s level of fatigue is . The error associated with this guess (that is, the “standard” amount your guess will be away from the true value) is . Suppose that now you are told the pain score for this new patient is 5.5. Now your best guess for the new patient’s level of fatigue is . The error associated with this guess (that is, the “standard” amount your guess will be away from the true value) is . Finally, suppose before estimating the regression equation, you first transform each of the original scores into a z-score. The regression equation you estimate is: ẑYY = zXX

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Question

	Scores
Patient	Pain (X)	Fatigue (Y)
A	3	1.25
B	4	0
C	5	6.5
D	6	2.5
E	7	10

Create a scatter plot of these scores on the grid. For each of the five (X, Y) pairs, drag the orange points (square symbol) in the upper-right corner of the diagram to the appropriate location on the grid.

Scores02468101086420FATIGUEPAIN

Calculate the means and complete the following table by calculating the deviations from the means for X and Y, the squares of the deviations, and the products of the deviations.

Scores	Deviations	Squared Deviations	Products
X	Y	X – MXX	Y – MYY	(X – MXX)²	(Y – MYY)²	(X – MXX)(Y – MYY)
3	1.25
4	0	-1.00	-4.05	1.00	16.40	4.05
5	6.5
6	2.5	1.00	-1.55	1.00	2.40	-1.55
7	10

Calculate the sum of the products and the sum of squares for X. SP = and SSXX = .

Find the regression line for predicting Y given X. The slope of the regression line is and the Y intercept is .

Calculate the Pearson correlation coefficient, the predicted variability, and the unpredicted variability. The Pearson correlation is r = . The predicted variability is SSregressionregression = . The unpredicted variability is SSresidualresidual = .

Calculate the standard error of the estimate. The standard error of the estimate is .

Suppose you want to predict the fatigue score for a new patient. The only information given is that this new patient is similar to patients A through E; therefore, your best guess for the new patient’s level of fatigue is . The error associated with this guess (that is, the “standard” amount your guess will be away from the true value) is .

Suppose that now you are told the pain score for this new patient is 5.5. Now your best guess for the new patient’s level of fatigue is . The error associated with this guess (that is, the “standard” amount your guess will be away from the true value) is .

Finally, suppose before estimating the regression equation, you first transform each of the original scores into a z-score. The regression equation you estimate is:

ẑYY

zXX

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Given,

	Scores
Patient	Pain (X)	Fatigue (Y)
A	3	1.25
B	4	0
C	5	6.5
D	6	2.5
E	7	10

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