tewart 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.

MATLAB: An Introduction with Applications
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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.

 

Scores
Deviations
Squared Deviations
Products
Y
X - Mx
Y - My
(х - Мx)2
(Y - My)2
(х — Мx)(Ү - My)
-4.00
-2.40
16.00
5.76
9.60
4
4
-2.00
1.60
4.00
2.56
-3.20
6.
1
0.00
-1.40
0.00
1.96
0.00
8
2.00
2.60
4.00
6.76
5.20
10
4.00
-0.40
0.16
0.16
-1.60
Transcribed Image Text:Scores Deviations Squared Deviations Products Y X - Mx Y - My (х - Мx)2 (Y - My)2 (х — Мx)(Ү - My) -4.00 -2.40 16.00 5.76 9.60 4 4 -2.00 1.60 4.00 2.56 -3.20 6. 1 0.00 -1.40 0.00 1.96 0.00 8 2.00 2.60 4.00 6.76 5.20 10 4.00 -0.40 0.16 0.16 -1.60
Calculate the sum of the products and the sum of squares for X. SP =
10.00
and SSx =
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 SSregression
The unpredicted variability is SSresidual
%D
%D
Calculate the standard error of the estimate. The standard error of the estimate is
Suppose you want to predict the pain 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 pain 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 depression score for this new patient is 5.5. Now your best guess for the new patient's level of pain 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:
ŻY
ZX
Transcribed Image Text:Calculate the sum of the products and the sum of squares for X. SP = 10.00 and SSx = 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 SSregression The unpredicted variability is SSresidual %D %D Calculate the standard error of the estimate. The standard error of the estimate is Suppose you want to predict the pain 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 pain 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 depression score for this new patient is 5.5. Now your best guess for the new patient's level of pain 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: ŻY ZX
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