Homework Sourse Homework Sourse

The accompanying computer excel output provides details of d

The accompanying computer excel output provides details of data and analysis on the topic of “patient satisfaction”.A hospital administrator wished to study the relation between patient satisfaction (Y) and patients’ age (X1, in years) , severity of illness (X2), and anxiety level (X3).

The output includes a listing of all 46 observations; descriptive statistics for each variable; and, the results of a regression analysis that uses patient satisfaction as the dependent variable, and patient’s age, severity of illness, and anxiety level as independent variables.

Write out the regression equation, with specific intercept and slope estimates.Remember that the regression is already estimated for you so you only need to write the equation of the estimated model. For example: Y=….

For the first row of actual data from the excel file, use the equation in (a) to “predict” the value of Y. For this row, also compute the “residual” (actual value minus the predicted value). Note: Only use the first raw of data and not the whole data set.

Evaluate the statistical significance of each of the three slope estimates. This can be done in a very summary way.Use a significance level of 0.05. Note: You can either use the p-value or t-statistics to determine whether the coefficients are significant or not.

Use the R-square and F-test to evaluate the overall reliability of the regression.

Anxiety Level

-27.79785878

46

0.857532395

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.825950563
R Square 0.682194333
Adjusted R Square 0.659493929
Standard Error 10.05797561
Observations 46
ANOVA
df SS MS F Significance F
Regression 3 9120.463666 3040.155 30.05207794 1.54197E-10
Residual 42 4248.840682 101.1629
Total 45 13369.30435
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 158.4912517 18.12588874 8.743916 5.26095E-11 121.9117272 195.0707761
Patient Age -1.141611847 0.214798809 -5.3148 3.81025E-06 -1.575093393 -0.7081303
Severity of illness -0.442004262 0.491965748 -0.89845 0.374070225 -1.434831336 0.550822812

Anxiety Level

Patient Satisfaction Patient Age Severity of illness Anxiety Level
48 50 51 2.3
57 36 46 2.3
66 40 48 2.2
70 41 44 1.8
89 28 43 1.8
36 49 54 2.9
46 42 50 2.2
54 45 48 2.4
26 52 62 2.9
77 29 50 2.1
89 29 48 2.4
67 43 53 2.4
47 38 55 2.2
51 34 51 2.3
57 53 54 2.2
66 36 49 2
79 33 56 2.5
88 29 46 1.9
60 33 49 2.1
49 55 51 2.4
77 29 52 2.3
52 44 58 2.9
60 43 50 2.3
86 23 41 1.8
43 47 53 2.5
34 55 54 2.5
63 25 49 2
72 32 46 2.6
57 32 52 2.4
55 42 51 2.7
59 33 42 2
83 36 49 1.8
76 31 47 2
47 40 48 2.2
36 53 57 2.8
80 34 49 2.2
82 29 48 2.5
64 30 51 2.4
37 47 60 2.4
42 47 50 2.6
66 43 53 2.3
83 22 51 2
37 44 51 2.6
68 45 51 2.2
59 37 53 2.1
92 28 46 1.8
-13.47016319 7.099660814 -1.8973 0.064678127

-27.79785878

Patient Satisfaction Patient Age Severity of illness Anxiety Level
Mean 61.56522 Mean 38.3913 Mean 50.43478261 Mean 2.286957
Standard Error 2.541378 Standard Error 1.314901 Standard Error 0.635999099 Standard Error 0.044135
Median 60 Median 37.5 Median 50.5 Median 2.3
Mode 57 Mode 29 Mode 51 Mode 2.2
Standard Deviation 17.23646 Standard Deviation 8.918092 Standard Deviation 4.313555759 Standard Deviation 0.299339
Sample Variance 297.0957 Sample Variance 79.53237 Sample Variance 18.60676329 Sample Variance 0.089604
Kurtosis -0.8869 Kurtosis -0.92358 Kurtosis 0.683648277 Kurtosis -0.35856
Skewness -0.02388 Skewness 0.160429 Skewness 0.296429638 Skewness 0.225102
Range 66 Range 33 Range 21 Range 1.1
Minimum 26 Minimum 22 Minimum 41 Minimum 1.8
Maximum 92 Maximum 55 Maximum 62 Maximum 2.9
Sum 2832 Sum 1766 Sum 2320 Sum 105.2
Count 46 Count 46 Count 46 Count
Patient Satisfaction Patient Age Severity of illness Anxiety Level
48 50 51 2.3
57 36 46 2.3
66 40 48 2.2
70 41 44 1.8
89 28 43 1.8
36 49 54 2.9
46 42 50 2.2
54 45 48 2.4
26 52 62 2.9
77 29 50 2.1
89 29 48 2.4
67 43 53 2.4
47 38 55 2.2
51 34 51 2.3
57 53 54 2.2
66 36 49 2
79 33 56 2.5
88 29 46 1.9
60 33 49 2.1
49 55 51 2.4
77 29 52 2.3
52 44 58 2.9
60 43 50 2.3
86 23 41 1.8
43 47 53 2.5
34 55 54 2.5
63 25 49 2
72 32 46 2.6
57 32 52 2.4
55 42 51 2.7
59 33 42 2
83 36 49 1.8
76 31 47 2
47 40 48 2.2
36 53 57 2.8
80 34 49 2.2
82 29 48 2.5
64 30 51 2.4
37 47 60 2.4
42 47 50 2.6
66 43 53 2.3
83 22 51 2
37 44 51 2.6
68 45 51 2.2
59 37 53 2.1
92 28 46 1.8

46

0.857532395

Solution

1.Regression rquation = Y = 158.4912517 -1.141611847*X1 - 0.442004262*X2 - 13.47016319*X3.....

2. Have the original data in the sheet as: In the cell A1 , Patient satisfaction , in A2 Patient age and like that!

Then in cell E1 , name it as Actual value. and in cell E2, write- = 158.4912517 -1.141611847*(B2) - 0.442004262*(C2) - 13.47016319*(D2)..
And carry out the same formula for the same of of \" patient satisfaction\"

now, name F1 cell as residual
and in F2 write- = E2 - A2..

and carry out the same formula for all of them!

you can copy paste the rquation exactly as i wrote adn you will get the residuals!

3) the p-value for Patient age is < 0.05 and so it is statistically significant!

It has no effect in the regression equation!

and for the rest 2 variables, p-value > 0.05.so, they are responsible for the relation!

and R^2 = 0.682194333..I.E, 68.22 % OF VARIATION OF Y IS EXPLAINED BY VARIATION IN X1,X2 AND X3..

AND 31.78% LEFT UNEXPLAINED!

And signifance F is close to 0 and less than 0.05, hence the test is significant!

The accompanying computer excel output provides details of data and analysis on the topic of “patient satisfaction”.A hospital administrator wished to study the
The accompanying computer excel output provides details of data and analysis on the topic of “patient satisfaction”.A hospital administrator wished to study the
The accompanying computer excel output provides details of data and analysis on the topic of “patient satisfaction”.A hospital administrator wished to study the

Get Help Now

Submit a Take Down Notice
Tutor
Tutor: Dr Jack
Most rated tutor on our site