Essays on Statistical Analysis - Pre-existing Congestive Heart Failure Coursework

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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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