Please answer area in red. The multiple choice answers for each blank are below
ID: 3365709 • Letter: P
Question
Please answer area in red. The multiple choice answers for each blank are below with an arrow leading toward them.
S. The regression equation and the standard error of estimate Aa Calculate the sum of the products and the sum of squares for X. SP- 20.00 and SS 40.00 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 nutrition therapy for the treatment of the simultaneous clustering of depression and pain in cancer patients. The following scores represent the decrease in symptom intensity (on a 10-point scale) following the new nutrition therapy. Find the regression line for predicting Y given X. The slope of the regression line is 0.5 and the Y intercept is Calculate the Pearson correlation coefficient, the predicted variability, and the unpredicted variability. The Pearson Patient Depression (X) Pain (Y) The unpredicted variability is correlation is0.4819The predicted variability is SSregression-.00 SSresidual- 33.07 0.75 2.75 Calculate the standard error of the estimate. The standard error of the estimate is 3.32 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. 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 Mr . 4.9 . The error associated with this guess (that is, the "standard"amount your guess will be away from the true is that now you are told the depression score for this new patient is 5.5. Now your best guess for the new level of pain is The error associated with this guess (that is, the "standard your guess will be away from the true value) is equation, you first transform each of the original into a Finally 2-score. The regression before the r you estimate is 3.32 3.06 2.21 My 4.9 0.4819 0.8787 2.4 Sy = 3.28 0.75 .75 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 1 5 4.00 3 0.752.00 5 70.00 7 2.75 2.00 4.00 0.10 -4.15 2.10 -2.15 4.10 16.00 4.00 0.00 4.00 16.00 0.01 17.22 441 4.62 16.81 0.40 8.30 0.00 -4.30 16,40Explanation / Answer
Result:
My=4.9
The error associated with guess is 3.28
For depression score of 5.5, guess for pain score is 5.15. error associated with this guess is 3.32.
For z scores, regression line is
zy=0.4819zx
Regression Analysis
r²
0.232
n
5
r
0.482
k
1
Std. Error
3.320
Dep. Var.
y
ANOVA table
Source
SS
df
MS
F
p-value
Regression
10.0000
1
10.0000
0.91
.4112
Residual
33.0750
3
11.0250
Total
43.0750
4
Regression output
confidence interval
variables
coefficients
std. error
t (df=3)
p-value
95% lower
95% upper
Intercept
2.4000
3.0159
0.796
.4843
-7.1979
11.9979
x
0.5000
0.5250
0.952
.4112
-1.1708
2.1708
Predicted values for: y
95% Confidence Interval
95% Prediction Interval
x
Predicted
lower
upper
lower
upper
Leverage
5.5
5.15000
0.35104
9.94896
-6.45564
16.75564
0.206
Regression Analysis
r²
0.232
n
5
r
0.482
k
1
Std. Error
3.320
Dep. Var.
y
ANOVA table
Source
SS
df
MS
F
p-value
Regression
10.0000
1
10.0000
0.91
.4112
Residual
33.0750
3
11.0250
Total
43.0750
4
Regression output
confidence interval
variables
coefficients
std. error
t (df=3)
p-value
95% lower
95% upper
Intercept
2.4000
3.0159
0.796
.4843
-7.1979
11.9979
x
0.5000
0.5250
0.952
.4112
-1.1708
2.1708
Predicted values for: y
95% Confidence Interval
95% Prediction Interval
x
Predicted
lower
upper
lower
upper
Leverage
5.5
5.15000
0.35104
9.94896
-6.45564
16.75564
0.206