Statistical Analysis - Pre-existing Congestive Heart Failure - Coursework Example

The paper "Statistical Analysis - Pre-existing Congestive Heart Failure " is a good example of business coursework. In this study, there is an analysis of three nominal and three scale variables that are, Gender, Pre-existing congestive heart failure and Recovery of renal function, and Age, Hours on bypass pump, Length of stay in the hospital, selected from the SPSS data file RENAL.sav.

 

Frequency Tables of Nominal Variables

 

gender

   

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

male

female

27

57

32.1

67.9

32.1

67.9

32.1

100.0

 

Total

84

100.0

100.0

 

preexisting congestive heart failure

   

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

no

yes

54

30

64.3

35.7

64.3

35.7

64.3

100.0

 

Total

84

100.0

100.0

 

recovery of renal function

   

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

no

yes

Total

7

35

42

8.3

41.7

50.0

16.7

83.3

100.0

16.7

100.0

 

Missing

-1

42

50.0

   

Total

84

100.0

   

preexisting congestive heart failure * gender Crosstabulation

Count

           

gender

Total

 

preexisting congestive heart failure

 

no

male

12

female

42

 

yes

15

15

30

27

57

84

recovery of renal function * gender Crosstabulation

Count

           

gender

Total

 

recovery of renal function

 

no

male

4

female

3

 

yes

11

24

35

15

27

42

Stem-and-Leaf Plot

 

hours on bypass pump Stem-and-Leaf Plot

 Frequency    Stem &  Leaf

      .00        0 .

     2.00        0 .  55

    16.00        1 .  0000000000000000

    25.00        1 .  5555555555555555555555555

    16.00        2 .  0000000000000000

    13.00        2 .  5555555555555

     7.00        3 .  0000000

      .00        3 .

     1.00        4 .  0

     3.00 Extremes    (>=4.5)

 Stem width:      1.00

 Each leaf:       1 case(s)

Boxplot of Recovery of Renal Function and Hours on Bypass Pump

Descriptive Statistics of Three Scale Variables

 

Statistics

   

age

length of stay in hospital

hours on the bypass pump

N

Valid

Missing

84

0

84

0

83

1

Mean

65.71

20.81

1.9096

Median

68.00

18.00

1.5000

Std. Deviation

10.821

11.067

.88739

Minimum

18

3

.50

Maximum

87

50

5.00

Percentiles

10

25

50

75

51.00

61.00

68.00

73.00

10.00

13.00

18.00

24.75

1.0000

1.5000

1.5000

2.5000

 

90

78.00

38.50

3.0000

Brief Analysis and Conclusion

Here the frequency tables and bar charts of nominal variables; gender, Pre-existing congestive heart failure and Recovery of renal function show that most of the patients were female, most of the patients respond that they had no pre-existing congestive heart failure and respond in yes in response of the question regarding recovery of renal function.

The cross-tabulation shows that from the selected patients there were most of the female patients who respond that there has no pre-existing congestive heart failure, and the table shows that there is no significant impact of gender on the recovery of renal function of the patient.

Now the frequency tables and histograms of age, length of stay in hospital and hours on bypass pump show that most of the patients belong to age group 60 to 78 years, majority of the patients were staying in hospital from 10 to 15 days and it has also been observed that there were most of the patients who spent maximum two and a half hours on bypass pump.

Stem-and-Leaf plot is the visual summary of the data, this plot is a partial sorting of the data and allows to identify the distributional pattern of the data, here in this paper there is the stem and leaf plot of hours on bypass pump. It also shows that most of the patients spend 1 to 2 hours on bypass pump. Boxplot is also called a box-and-whisker plot. A boxplot consists of a box, whiskers, and outliers. Here we have a boxplot between two variables hours of bypass pump and recovery of renal function has a line across the box at the median. The bottom of the box is at the first quartile (Q1) and the top is at the third quartile (Q3). The whiskers are the lines that extend from the top and bottom of the box to the adjacent values, the lowest and highest observations still inside the region defined by the lower limit Q1 - 1.5 (Q3 - Q1) and the upper limit Q3 + 1.5 (Q3 - Q1). Outliers are points outside the lower and upper limits, plotted with asterisks (*), like 14 in the above-given boxplot.

At last, there is descriptive statistics for each 3 scale variables it was observed that there is not much difference in the mean and median of each variable that shows the symmetric pattern, standard deviation shows the variation in the patients’ length of stay in hospital. and the percentiles displays the value below which the specified percentage of cases falls.

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